We report the first-ever experimental observation of backward stimulated Brillouin scattering (SBS) in thin-film lithium niobate (TFLN) waveguides. The peak Brillouin gain coefficient of the z-cut LN waveguide with a crystal rotation angle of 20$^{\circ}$ is as high as 84.9m$^{-1}$W$^{-1}$, facilitated by surface acoustic waves (SAW) at 8.06GHz.

A statistical physical model of the two basic properties of clusters within a hurricane--their convectivity and rotation--reveals a relationship for the time evolution of a hurricane. Non-doppler data from NEXRAD surface radar imagery, of Hurricane Irma transiting over Florida, is decomposed into unique clusters on the basis of an annealing process using entropy and energy differences between pixels. Application of the concept of entropic forces between a cluster's pixels, provides an estimate of the radial velocity of each cluster by application of Stokes' theorem. The ratio of the characteristic rotation and convectivity, associated with radial flow, integrated over the extent of the hurricane, closely tracks the hurricane's state, providing more time resolution than aircraft sorties alone allow. It is concluded that monitoring the rotational and convective state, in conjunction with the size of a cluster, is capable of quickly providing forecasters and others with changes in a hurricane's state. It is also shown that entropic tornado state can be similarly described in terms of convectivity and rotation rate.

A crucial aspect of democracy is the notion of free and fair elections. While much attention has been placed on factors such as voter turnout and the quality of the candidates, the voting system used in an election is just as consequential in determining the winner. In Social Choice Theory, voting systems are evaluated based on whether or not they violate fairness criteria. Arrow's Impossibility Theorem states that no voting method can jointly satisfy five fairness criteria: unanimity, transitivity, independence of irrelevant alternatives, unrestricted domain, and non-dictatorship. However, voting systems are not equally likely to violate criteria. This paper takes a statistical approach to determine which of 12 voting systems is the fairest based on their probabilistic likelihood of violating nine social choice criteria. The criteria include Arrow's five conditions, the Condorcet winner and loser criteria, the majority criteria, and citizen sovereignty. Elections with up to 10 million voters and between three and six candidates are simulated using Impartial Culture and Impartial Anonymous Culture. We first compute the frequencies with which voting systems violate individual criteria and then compute Joint Satisfaction frequencies, the likelihood that a voting system jointly satisfies all nine criteria. We find that Pairwise Majority is the most likely to jointly satisfy the nine conditions, but increasingly violates transitivity as the number of candidates increases. Of the systems that satisfy transitivity, the Baldwin method has the highest Joint Satisfaction frequency in elections with three candidates. As the number of candidates increase, the Joint Satisfaction frequencies decrease rapidly for all systems.

In this work, we present a method to generate a configurational level fingerprint for polymers using the Bead-Spring-Model. Unlike some of the previous fingerprinting approaches that employ monomer-level information where atomistic descriptors are computed using quantum chemistry calculations, this approach incorporates configurational information from a coarse-grained model of a long polymer chain. The proposed approach may be advantageous for the study of behavior resulting from large molecular weights. To create this fingerprint, we make use of two kinds of descriptors. First, we calculate certain geometric descriptors like Re2, Rg2 etc. and label them as Calculated Descriptors. Second, we generate a set of data-driven descriptors using an unsupervised autoencoder model and call them Learnt Descriptors. Using a combination of both of them, we are able to learn mappings from the structure to various properties of the polymer chain by training ML models. We test our fingerprint to predict the probability of occurrence of a configuration at equilibrium, which is approximated by a simple linear relationship between the instantaneous internal energy and equilibrium average internal energy.

The evolution of synchrotrons towards higher brilliance beams has increased the possible sample-to-detector propagation distances for which the source confusion circle does not lead to geometrical blurring. This makes it possible to push near-field propagation driven phase contrast enhancement to the limit, revealing low contrast features which would otherwise remain hidden under an excessive noise-to-signal ratio. Until today this possibility was hindered, in most objects of scientific interest, by the simultaneous presence of strong phase gradient regions and low contrast features. The strong gradients, when enhanced with the now possible long propagation distances, induce such strong phase effects that the linearisation assumptions of current state-of-the-art single-distance phase retrieval filters are broken, and the resulting image quality is jeopardized. Our work provides an innovative algorithm which efficiently performs the phase retrieval task over the entire near-field range, producing images of exceptional quality for mixed objects.

X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.

The field of opinion dynamics has evolved steadily since the earliest studies applying magnetic physics methods to better understand social opinion formation. However, in the real world, complete agreement of opinions is rare, and biaxial consensus, especially on social issues, is rare. To address this challenge, Ishii and Kawabata (2018) proposed an extended version of the Bounded Confidence Model that introduces new parameters indicating dissent and distrust, as well as the influence of mass media. Their model aimed to capture more realistic social opinion dynamics by introducing coefficients representing the degree of trust and distrust, rather than assuming convergence of opinions. In this paper, we propose a new approach to opinion dynamics based on this Trust-Distrust Model (TDM), applying the dimer allocation and Ising model. Our goal is to explore how the interaction between trust and distrust affects social opinion formation. In particular, we analyze through mathematical models how various external stimuli, such as mass media, third-party opinions, and economic and political factors, affect people's opinions. Our approach is to mathematically represent the dynamics of trust and distrust, which traditional models have not addressed. This theoretical framework provides new insights into the polarization of opinions, the process of consensus building, and how these are reflected in social behavior. In addition to developing the theoretical framework by applying the dimer configuration, the dimer model and the Ising model, this paper uses numerical simulations to show how the proposed model applies to actual social opinion formation. This research aims to contribute to a deeper understanding of social opinion formation by providing new perspectives in the fields of social science, physics, and computational modeling.

Due to the vulnerability of the Caribbean islands to the climate change issue, it is important to investigate the behavior of rainfall. In addition, the soil of the French West Indies Islands has been contaminated by an insecticide (Chlordecone) whose decontamination is mainly done by drainage water. Thus, it is crucial to investigate the fluctuations of rainfall in these complex environments. In this study, 19 daily rainfall series recorded in different stations of Guadeloupe archipelago from 2005 to 2014 were analyzed with the multifractal detrended fluctuation analysis (MF-DFA) method. The aim of this work is to characterize the long-range correlations and multifractal properties of the time series and to find geographical patterns over the three most important islands. This is the first study that addresses the analysis of multifractal properties of rainfall series in the Caribbean islands. This region is typically characterized by the almost constant influence of the trade winds and a high exposure to changes in the general atmospheric circulation. 12 stations exhibit two different power-law scaling regions in rainfall series, with distinct long-range correlations and multifractal properties for large and small scales. On the contrary, the rest of stations only show a single region of scales for relatively small scales. Hurst exponents reveal persistent long-range correlations. In the most eastern analyzed areas, larger scales exhibit higher persistence than smaller scales, which suggests a relationship between persistence and the highest exposure to the trade winds. Stronger conclusions can be drawn from multifractal spectra, which indicate that most rainfall series have a multifractal nature with higher complexity and degree of multifractality at the smallest scales. Furthermore, a clear dependence of multifractal nature on the latitude is revealed.

Network sparsification represents an essential tool to extract the core of interactions sustaining both networks dynamics and their connectedness. In the case of infectious diseases, network sparsification methods remove irrelevant connections to unveil the primary subgraph driving the unfolding of epidemic outbreaks in real networks. In this paper, we explore the features determining whether the metric backbone, a subgraph capturing the structure of shortest paths across a network, allows reconstructing epidemic outbreaks. We find that both the relative size of the metric backbone, capturing the fraction of edges kept in such structure, and the distortion of semi-metric edges, quantifying how far those edges not included in the metric backbone are from their associated shortest path, shape the retrieval of Susceptible-Infected (SI) dynamics. We propose a new method to progressively dismantle networks relying on the semi-metric edge distortion, removing first those connections farther from those included in the metric backbone, i.e. those with highest semi-metric distortion values. We apply our method in both synthetic and real networks, finding that semi-metric distortion provides solid ground to preserve spreading dynamics and connectedness while sparsifying networks.

A statistical model for data emanating from digital image sensors is developed and used to define a notion of the system level photon counting accuracy given a specified quantization strategy. The photon counting accuracy for three example quantization rules is derived and the performance of each rule is compared.

Measuring the spectral phase of a pulse is key for performing wavelength resolved ultrafast measurements in the few femtosecond regime. However, accurate measurements in real experimental conditions can be challenging. We show that the reflectivity change induced by coherent phonons in a quantum material can be used to infer the spectral phase of an optical probe pulse with few-femtosecond accuracy.

This paper explores the electrical and thermal conductivity of complex contact spots on the surface of a half-space. Employing an in-house Fast Boundary Element Method implementation, various complex geometries were studied. Our investigation begins with annulus contact spots to assess the impact of connectedness. We then study shape effects on "multi-petal" spots exhibiting dihedral symmetry, resembling flowers, stars, and gears. The analysis culminates with self-affine shapes, representing a multi-scale generalization of the multi-petal forms. In each case, we introduce appropriate normalizations and develop phenomenological models. For multi-petal shapes, our model relies on a single geometric parameter: the normalized number of "petals". This approach inspired the form of the phenomenological model for self-affine spots, which maintains physical consistency and relies on four geometric characteristics: standard deviation, second spectral moment, Nayak parameter, and Hurst exponent. As a by product, these models enabled us to suggest flux estimations for an infinite number of petals and the fractal limit. This study represents an initial step into understanding the conductivity of complex contact interfaces, which commonly occur in the contact of rough surfaces.

This document describes an approach used in the Multi-Machine Disruption Prediction Challenge for Fusion Energy by ITU, a data science competition which ran from September to November 2023, on the online platform Zindi. The competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT - with most of the training data coming from the last two, and the test data coming from the first one. Each device has multiple diagnostics and signals, and it turns out that a critical issue in this competition was to identify which signals, and especially which features from those signals, were most relevant to achieve accurate predictions. The approach described here is based on extracting features from signals, and then applying logistic regression on top of those features. Each signal is treated as a separate predictor and, in the end, a combination of such predictors achieved the first place on the leaderboard.

In this study, a smoothed particle hydrodynamics (SPH) model that applies a segment-based boundary treatment is used to simulate natural convection. In a natural convection simulated using an SPH model, the wall boundary treatment is a major issue because accurate heat transfer from boundaries should be calculated. The boundary particle method, which models the boundary by placing multiple layers of particles on and behind the wall boundary, is the most widely used boundary treatment method. Although this method can impose accurate boundary conditions, boundary modeling for complex shapes is challenging and requires excessive computational costs depending on the boundary shape. In this study, we utilize a segment-based boundary treatment method to model the wall boundary and apply this method to the energy conservation equation for the wall heat transfer model. The proposed method solves the problems arising from the use of boundary particles and simultaneously provides accurate heat transfer calculation results for the wall. In various numerical examples, the proposed method is verified through a comparison with available experimental results, SPH results using the boundary particle method, and finite volume method (FVM) results.

The most significant advances in the accelerator-based light sources (i.e., x-ray free electron lasers) are driven by the development of bunch compression and high-brightness sources in the last several decades. For bunch compression, the symmetric four-dipole $C$-chicane is typically exploited since it is simple, effective, and naturally dispersion-free at all orders. However, the emission of the coherent synchrotron radiation (CSR), becomes a main contributing factor to transverse emittance degradation during bunch compression. Suppressing CSR effect is necessary otherwise the anticipative compression schemes are susceptible to beam quality degradation. To alleviate this difficulty, this paper reports a CSR-cancelled theoretical design for asymmetric $C$- and $S$-chicanes. The CSR cancelation conditions of the two chicanes are derived with the aid of the point-kick model. A rigorous analysis is provided to verify the proposed CSR cancelation conditions for asymmetric $C$- and $S$-chicanes, performed by the integration method and ELEGANT simulations. Compared to the symmetric $C$- and $S$-chicanes with identical bunch compression targets, the CSR-induced emittance growth of the asymmetric ones can be reduced by two orders of magnitude and at the order of 0.1\%. The demonstration of this novel discovery opens the door for the chicane bunch compressors to meet the ever-increasing demands of future accelerator applications, particularly important in the field of x-ray free electron lasers.

The spotlight is on the intertwined nature of sustainability and energy transition. As the world grapples with environmental challenges, the push for a green approach to energy is more crucial than ever. This transition promises not just a cleaner planet but also better public health and job opportunities. There is a call for united front from policymakers, businesses, and communities to fast-track this eco-friendly shift.

Compact and robust frequency-stabilized laser sources are critical for a variety of fields that require stable frequency standards, including field spectroscopy, radio astronomy, microwave generation, and geophysical monitoring. In this work, we applied a simple and compact fiber ring-resonator configuration that can stabilize both a continuous-wave laser and a self-referenced optical frequency comb to a vibration-insensitive optical fiber delay-line. We could achieve a thermal-noise-limited frequency noise level in the 10 Hz - 1 kHz offset frequency range for both the continuous-wave laser and the optical frequency comb with the minimal frequency instability of 2.7x10-14 at 0.03-s and 2.6x10-14 at 0.01-s averaging time, respectively, in non-vacuum condition. The optical fiber spool, working as a delay reference, is designed to be insensitive to external vibration, with a vibration sensitivity of sub-10-10 [1/g] and volume of 32 mL. Finally, the ring-resonator setup is packaged in a palm-sized aluminum case with 171-mL volume with a vibration-insensitive spool, as well as an even smaller 97-mL-volume case with an ultra-compact 9-mL miniaturized fiber spool.

The transition of hypersonic boundary layer can lead to a several-fold increase in surface heat flux and skin friction for the aircraft, significantly impacting its flight performance. The corrugated wall, as a passive control method for boundary layer flow, also serves as a type of wall microstructure, making its study on the local rarefaction effect of considerable engineering significance. In this study, we employed the conservative discrete unified gas dynamic scheme and utilized a domain-wide numerical simulation method. Initially, we simulated the hypersonic flat plate flow with different depths of corrugated walls under the conditions of incoming flow Mach number of 6 and Reynolds number of ${{10}^{7}}$. Subsequently, we investigated the effects of corrugated walls, including flat plate corrugated walls and wedge corrugated walls, under varying Reynolds numbers for an incoming flow Mach number of 6, and discussed the impact of local rarefaction effect of corrugated walls under different Reynolds numbers. By using the local Knudsen number as the criterion, we found that under these conditions, the occurrence of local rarefaction effect near the corrugated wall due to consecutive failures does not take place when the incoming Reynolds number reaches ${{10}^{7}}$ or ${{10}^{6}}$. However, when the incoming Reynolds number drops to ${{10}^{5}}$, the local rarefaction effect near the corrugated wall becomes evident, with the appearance of non-equilibrium effects in translational and rotational temperatures of molecules. This phenomenon becomes more pronounced as the Reynolds number decreases further.

In addressing the demands of industrial high-fidelity computation, the present study introduces a rapid and accurate customized solver developed on the OpenFOAM platform. To enhance computational efficiency, a novel integrated acceleration strategy is introduced. Initially, a sparse analytical Jacobian approach utilizing the SpeedCHEM chemistry library was implemented to increase the efficiency of the ODE solver. Subsequently, the Dynamic Load Balancing (DLB) code was employed to uniformly distribute the computational workload for chemistry among multiple processes. Further optimization was achieved through the introduction of the Open Multi-Processing (OpenMP) method to enhance parallel computing efficiency. Lastly, the Local Time Stepping (LTS) scheme was integrated to maximize the individual time step for each computational cell, resulting in a noteworthy minimum speed-up of over 31 times. The effectiveness and robustness of this customized solver were systematically validated against three distinct partially turbulent premixed flames, Sandia Flames D, E, and F. Additionally, a comparative analysis was conducted, encompassing different turbulence models, turbulent Prandtl numbers, and model constants, resulting in the recommendation of optimal numerical parameters for various conditions. The present study offers one viable solution for rapid and accurate calculations in the OpenFOAM platform, while also providing insights into the selection of turbulence models and parameters for industrial numerical simulation.

Polarized electron beam production via laser wakefield acceleration in pre-polarized plasma is investigated by particle-in-cell simulations. The evolution of the electron beam polarization is studied based on the Thomas-Bargmann-Michel-Telegdi equation for the transverse and longitudinal self-injection, and the depolarization process is found to be influenced by the injection schemes. In the case of transverse self-injection as found typically in the bubble regime, the spin precession of the accelerated electrons is mainly influenced by the wakefield. However, in the case of longitudinal injection in the quasi-one-dimensional regime (for example, F. Y. Li \emph{et al}., Phys. Rev. Lett. 110, 135002 (2013)), the direction of electron spin oscillates in the laser filed. Since the electrons move around the laser axis, the net influence of the laser field is nearly zero and the contribution of the wakefield can be ignored. Finally, an ultra-short electron beam with polarization of $99\%$ can be obtained using longitudinal self-injection.

Radiation measurement relies on pulse detection, which can be performed using various configurations of high-speed analog-to-digital converters (ADCs) and field-programmable gate arrays (FPGAs). For optimal power consumption, design simplicity, system flexibility, and the availability of DSP slices, we consider the Radio Frequency System-on-Chip (RFSoC) to be a more suitable option than traditional setups. To this end, we have developed custom RFSoC-based electronics and verified its feasibility. The ADCs on RFSoC exhibit a flat frequency response of 1-125 MHz. The root-mean-square (RMS) noise level is 2.1 ADC without any digital signal processing. The digital signal processing improves the RMS noise level to 0.8 ADC (input equivalent 40 Vrms). Baseline correction via digital signal processing can effectively prevent photomultiplier overshoot after a large pulse. Crosstalk between all channels is less than -55 dB. The measured data transfer speed can support up to 32 kHz trigger rates (corresponding to 750 Mbps). Overall, our RFSoC-based electronics are highly suitable for pulse detection, and after some modifications, they will be employed in the Kamioka Liquid Scintillator Anti-Neutrino Detector (KamLAND).

The model-based library (MBL) method has already been established for the accurate measurement of critical dimension (CD) of semiconductor linewidth from a critical dimension scanning electron microscope (CD-SEM) image. In this work the MBL method has been further investigated by combing the CD-SEM image simulation with a neural network algorithm. The secondary electron linescan profiles were calculated at first by a Monte Carlo simulation method, enabling to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g., top CD, sidewall angle and height) for Si and Au trapezoidal line structures. The machine learning methods have then been applied to predicate the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviation of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. This result shows that the machine learning methods can be practically applied to the MBL method for the purpose of reducing the library size, accelerating the construction of the MBL database and enriching the content of an available MBL database.

Waveguide lattices offer a compact and stable platform for a range of applications, including quantum walks, topological effects, condensed matter system simulation, and classical and quantum information processing. In such lattices, the Hamiltonian's hopping and on-site terms determine the optical evolution, which can be engineered using waveguide spacing and refractive index profile. While waveguide lattices have been realized in various photonic platforms, these devices have always been static and designed for specific applications. We present a programmable waveguide array in which the Hamiltonian terms can be electro-optically tuned to implement various Hamiltonian continuous-time evolutions on a single device. We used a single array with 11 waveguides in lithium niobate, controlled via 22 electrodes, to perform a range of experiments that realized the Su-Schriffer-Heeger model, the Aubrey-Andre model, and Anderson localization, which is equivalent to over 2500 static devices. Our architecture's micron-scale local electric fields independently control waveguide coupling coefficients and effective indices, which overcomes cross-talk limitations of thermo-optic phase shifters in other platforms such as silicon, silicon-nitride, and silica. Electro-optic control allows for ultra-fast and more precise reconfigurability with lower power consumption, and with quantum input states, our platform can enable the study of multiple condensed matter quantum dynamics with a single device.

We investigate the hot electrons generated from two-plasmon decay (TPD) instability driven by laser pulses with intensity modulated by a frequency $\Delta \omega_m$. Our primary focus lies on scenarios where $\Delta \omega_m$ is on the same order of the TPD growth rate $ \gamma_0$ ( $\Delta \omega_m \sim \gamma_0$), corresponding to moderate laser frequency bandwidths for TPD mitigation. With $\Delta \omega_m$ conveniently modeled by a basic two-color scheme of the laser wave fields in fully-kinetic particle-in-cell simulations, we demonstrate that the energies of TPD modes and hot electrons exhibit intermittent evolution at the frequency $\Delta \omega_m$, particularly when $\Delta \omega_m \sim \gamma_0$. With the dynamic TPD behavior, the overall ratio of hot electron energy to the incident laser energy, $f_{hot}$, changes significantly with $\Delta \omega_m$. While $f_{hot}$ drops notably with increasing $\Delta \omega_m$ at large $\Delta \omega_m$ limit as expected, it goes anomalously beyond the hot electron energy ratio for a single-frequency incident laser pulse with the same average intensity when $\Delta \omega_m$ falls below a specific threshold frequency $\Delta \omega_c$. We find this threshold frequency primarily depends on $\gamma_0$ and the collisional damping rate of plasma waves, with relatively lower sensitivity to the density scale length. We develop a scaling model characterizing the relation of $\Delta \omega_c$ and laser plasma conditions, enabling the potential extention of our findings to more complex and realistic scenarios.

Light source is indispensable component in on-chip system. Compared with hybrid or heterogeneous integrated laser, monolithically integrated laser is more suitable for high density photonic integrated circuit (PIC) since the capability of large-scale manufacturing, lower active-passive coupling loss and less test complexity. Recent years have seen the spark of researches on rare-earth ion doped thin film lithium niobate (REI:TFLN), demonstrations have been made both in classical and quantum chips. However, low output power and limited quantum emitting efficiency hinder the application of the chip-scale laser source based on REI:TFLN. Here a highly efficient integrated laser assisted by cascaded amplifiers is proposed and experimentally prepared on Erbium-doped TFLN. A slope efficiency of 0.43% and a linewidth of 47.86 kHz are obtained. The maximum integrated laser power is 7.989 {\mu}W. Our results show a viable solution to improve efficiency by self-amplification without changing the intrinsic quantum emitting efficiency of the material, and our design has potential application in incorporating with functional devices such as optical communications, integrated quantum memory and quantum emission.

In this research, we introduce an innovative three-network architecture that comprises an encoder-decoder framework with an attention mechanism. The architecture comprises a 1st-order-pre-trainer, a 2nd-order-improver, and a discriminator network, designed to boost the order accuracy of charge density in Particle-In-Cell (PIC) simulations. We acquire our training data from our self-developed 3-D PIC code, JefiPIC. The training procedure starts with the 1st-order-pre-trainer, which is trained on a large dataset to predict charge densities based on the provided article positions. Subsequently, we fine-tune the 1st-order-pre-trainer, whose predictions then serve as inputs to the 2nd-order-improver. Meanwhile, we train the 2nd-order-improver and discriminator network using a smaller volume of 2nd-order data, thereby achieving to generate charge density with 2nd-order accuracy. In the concluding phase, we replace JefiPIC's conventional particle interpolation process with our trained neural network. Our results demonstrate that the neural network-enhanced PIC simulation can effectively simulate plasmas with 2 nd-order accuracy. This highlights the advantage of our proposed neural network: it can achieve higher-accuracy data with fewer real labels.

Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for diagnosing the photosynthetic characteristics of terrestrial ecosystems. Despite the increasing spatial and temporal resolutions of these satellite retrievals, records of SIF are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function and structure. In this study, we leverage the two surface reflectance bands (red and near-infrared) available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2022) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2022). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data, a neural network is then built to reproduce the Orbiting Carbon Observatory-2 SIF using AVHRR and MODIS, and used to map SIF globally over the entire 1982-2022 period. Compared with the previous MODIS-based CSIF product relying on four reflectance bands, our two-band-based product has similar skill but can be advantageously extended to the bias-corrected AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv; all based empirically on red and near-infrared bands) shows a higher or comparable correlation of LCSIF with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity of LCSIF for representing terrestrial photosynthesis. Globally, LCSIF-AVHRR shows an accelerating upward trend since 1982, with an average rate of 0.0025 mW m-2 nm-1 sr-1 per decade during 1982-2000 and 0.0038 mW m-2 nm-1 sr-1 per decade during 2001-2022. Our LCSIF data provide opportunities to better understand the long-term dynamics of ecosystem photosynthesis and their underlying driving processes.

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.

The article investigates liquid oxygen (LOx)-methane supercritical combustion dynamics in a multi-element rocket scale combustor using large eddy simulation (LES). A complex framework of real gas thermodynamics and flamelet generated manifold (FGM) combustion model is invoked to simulate transcritical oxygen injection and supercritical methane combustion. A benchmark Mascotte chamber, Rocket Combustion Modelling (RCM) Test Case, i.e. RCM-3 (V04)/G2 test case, is used to validate the real gas FGM model in the LES framework. The validation study accurately reproduces experimental flame structure and OH concentration, demonstrating the FGM model's importance in incorporating finite rate kinetics in LOx-methane combustion. Subsequently, the numerical framework investigates a specially designed multi-element combustion chamber featuring seven bidirectional swirl coaxial injectors. The analyses capture the complex hydrodynamics and combustion dynamics associated with multiple swirl injectors operating at supercritical pressure, effectively demonstrating the initiation of transverse acoustic waves and examining the effect of local sound speed on the evolution of acoustic modes in the combustor. The dominant frequency modes shed light on understanding the role of injectors in enhanced combustor dynamics. Spectral analysis reveals the interplay of the upstream injector and chamber acoustics due to possible frequency coupling. The results also highlight the effect of fuel injection temperature on the stability of the combustor, revealing a violent dynamic activity for lower fuel injection temperature associated with the longitudinal acoustic mode of the combustor. The investigation appropriately reproduces self-sustained limit cycle oscillations at lower fuel injection temperatures and corroborates the conventional understanding of combustor instability.

This article is an extended version of the article published by EDP Sciences at the occasion of the 150 years of the ``Soci\'et\'e Fran\c{c}aise de Physique'' (in French)

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multi-modal 3T MRI. The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in the Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided: 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows: PSNR: 27.0+-0.9 dB, SSIM: 0.945+-0.010, and MSE: 2.0+-0.4E-3. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, especially when 7T MRI scanners are unavailable.

The angular momentum of light can be described by positions on various Poincar\'e spheres, where different structured light beams have proven useful for numerous optical applications. However, the dynamic generation and control of arbitrary structured light on different Poincar\'e spheres is still handled via bulky optics in free space. Here we propose and demonstrate multimode silicon photonic integrated meta-waveguides to generate arbitrary structured light beams on polarization/orbit/higher-order/hybrid Poincar\'e spheres. The multimode meta-waveguides are inversely designed to map polarization states/higher-order spatial modes to orbit angular momentum, generating polarization-/charge-diverse orbit angular momentum modes. Based on the fundamental orbit angular momentum mode basis enabled by the meta-waveguides, different structured-light fields on polarization/orbit/higher-order/hybrid Poincar\'e spheres could be flexibly generated by controlling the relative amplitude and phase profiles of on-chip guided modes. The demonstrated photonic integrated devices hold great potential for the flexible manipulation of structure light beams in many applications.

We study the time-reflection and time-refraction of waves caused by a spatial interface with a medium undergoing a sudden temporal change in permittivity. We show that monochromatic waves are transformed into a pulse by the permittivity change, and that time-reflection is enhanced at the vicinity of the critical angle for total internal reflection. In this regime, we find that the evanescent field is transformed into a propagating pulse by the sudden change in permittivity. These effects display enhancement of the time-reflection and high sensitivity near the critical angle, paving the way to experiments on time-reflection and photonic time-crystals at optical frequencies.

Recent reports of upconversion and white light emission from graphitic particles warrant an explanation of the physics behind the process. We offer a model, wherein the upconversion is facilitated by photo-induced electronic structure modification allowing for multi-photon processes. As per the prediction of the model, we experimentally show that graphite upconverts infrared light centered around 1.31~$\mu$m to broadband white light centered around 0.85 $\mu$m. Our results suggest that upconversion from shortwave infrared ($\sim$3~$\mu$m) to visible region may be possible. Our experiments show that the population dynamics of the electronic states involved in this upconversion process occur in the timescale of milliseconds.

The changing topology of a network is driven by the need to maintain or optimize network function. As this function is often related to moving quantities such as traffic, information, etc. efficiently through the network the structure of the network and the dynamics on the network directly depend on the other. To model this interplay of network structure and dynamics we use the dynamics on the network, or the dynamical processes the network models, to influence the dynamics of the network structure, i.e., to determine where and when to modify the network structure. We model the dynamics on the network using Jackson network dynamics and the dynamics of the network structure using minimal specialization, a variant of the more general network growth model known as specialization. The resulting model, which we refer to as the integrated specialization model, coevolves both the structure and the dynamics of the network. We show this model produces networks with real-world properties, such as right-skewed degree distributions, sparsity, the small-world property, and non-trivial equitable partitions. Additionally, when compared to other growth models, the integrated specialization model creates networks with small diameter, minimizing distances across the network. Along with producing these structural features, this model also sequentially removes the network's largest bottlenecks. The result are networks that have both dynamic and structural features that allow quantities to more efficiently move through the network.

We present a hydrodynamic simulation system using the GPU compute shaders of DirectX for simulating virtual agent behaviors and navigation inside a smoothed particle hydrodynamical (SPH) fluid environment with real-time water mesh surface reconstruction. The current SPH literature includes interactions between SPH and heterogeneous meshes but seldom involves interactions between SPH and virtual boid agents. The contribution of the system lies in the combination of the parallel smoothed particle hydrodynamics model with the distributed boid model of virtual agents to enable agents to interact with fluids. The agents based on the boid algorithm influence the motion of SPH fluid particles, and the forces from the SPH algorithm affect the movement of the boids. To enable realistic fluid rendering and simulation in a particle-based system, it is essential to construct a mesh from the particle attributes. Our system also contributes to the surface reconstruction aspect of the pipeline, in which we performed a set of experiments with the parallel marching cubes algorithm per frame for constructing the mesh from the fluid particles in a real-time compute and memory-intensive application, producing a wide range of triangle configurations. We also demonstrate that our system is versatile enough for reinforced robotic agents instead of boid agents to interact with the fluid environment for underwater navigation and remote control engineering purposes.

Assisted with Magnetospheric Multiscale (MMS) mission capturing unprecedented high-resolution data in the terrestrial magnetotail, we apply a local streamline-topology classification methodology to investigate the categorization of the magnetic-field topological structures at kinetic scales in the turbulent reconnection outflow. It is found that strong correlations between the straining and rotational part of the velocity gradient tensor as well as the magnetic-field gradient tensor. The strong energy dissipation prefers to occur at regions with high magnetic stress or current density, which is contributed mainly by O-type topologies. These results indicate that the kinetic structures with O-type topology play more import role in energy dissipation in turbulent reconnection outflow.

The traveling tube (TWT) design in a nutshell comprises of a pencil-like electron beam (e-beam) in vacuum interacting with guiding it slow-wave structure (SWS). In our prior studies the e-beam was represented by one-dimensional electron flow and SWS was represented by a transmission line (TL). We extend in this paper our previously constructed field theory for TWTs as well the celebrated Pierce theory by replacing there the standard transmission line (TL) with its generalization allowing for the low frequency cutoff. Both the standard TL and generalized transmission line (GTL) feature uniformly distributed shunt capacitance and serial inductance, but the GTL in addition to that has uniformly distributed serial capacitance. We remind the reader that the standard TL represents a waveguide operating at the so-called TEM mode with no low frequency cutoff. In contrast, the GTL represents a waveguide operating at the so-called TM mode featuring the low frequency cutoff. We develop all the details of the extended TWT field theory and using a particular choice of the TWT parameters we derive a physically appealing factorized form of the TWT dispersion relations. This form has two factors that represent exactly the dispersion functions of non-interacting GTL and the e-beam. We also find that the factorized dispersion relations comes with a number of interesting features including: (i) focus points that belong to each dispersion curve as TWT principle parameter varies; (ii) formation of 'hybrid" branches of the TWT dispersion curves parts of which can be traced to non-interacting GTL and the e-beam.

An optical field around regular polygon metal nanostructures excited by circularly polarized light can exhibit rotationally displaced intensity distributions. Although this phenomenon has been recognized, its underlying mechanisms has not been sufficiently explained. Herein, finite-difference time-domain simulations and model analyses reveal that the rotationally displaced optical intensity distribution can be generated when each of the linear polarization components that constitute circular polarization excites a superposition of multiple modes. The proposed model reasonably explains the rotationally displaced patterns for a square nanoantenna and other regular-polygon nanoantennas.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.

Identifying the constituting materials of concealed objects is crucial in a wide range of sectors, such as medical imaging, geophysics, nonproliferation, national security investigations, and so on. Existing methods face limitations, particularly when multiple materials are involved or when there are challenges posed by scattered radiation and large areal mass. Here we introduce a novel brute-force statistical approach for material identification using high spectral resolution detectors, such as HPGe. The method relies upon updated semianalytic formulae for computing uncollided flux from source of gamma radiation, shielded by a sequence of nested spherical or cylindrical materials. These semianalytical formulae make possible rapid flux estimation for material characterization via combinatorial search through all possible combinations of materials, using a high-resolution HPGe counting detector. An important prerequisite for the method is that the geometry of the objects is known (for example, from X-ray radiography). We demonstrate the viability of this material characterization technique in several use cases with both simulated and experimental data.

We report a comprehensive investigation and experimental realization of spectral bifurcations of ultrafast soliton pulses. These bifurcations are induced by the interplay between fractional group-velocity dispersion and Kerr nonlinearity (self-phase modulation) within the framework of the fractional nonlinear Schr\"{o}dinger equation. To capture the dynamics of the pulses under the action of the fractional dispersion and nonlinearity, we propose an effective `force' model based on the frequency chirp, which characterizes their interactions as either `repulsion', `attraction', or `equilibration'. By leveraging the `force' model, we design segmented fractional dispersion profiles that directly generate spectral bifurcations \{1\}$\rightarrow$ \{N\} at relevant nonlinearity levels. These results extend beyond the traditional sequence of bifurcations \{1\}$\rightarrow$ \{2\}$\rightarrow$ \{3\} ... $\rightarrow$ \{N\} associated with the growth of the nonlinearity. The experimental validation involves a precisely tailored hologram within a pulse shaper setup, coupled to an alterable nonlinear medium. Notably, we achieve up to N=5 in \{1\}$\rightarrow$ \{N\} bifurcations at a significantly lower strength of nonlinearity than otherwise would be required in a sequential cascade. The proposal for engineering spectral bifurcation patterns holds significant potential for ultrafast signal processing applications. As a practical illustration, we employ these bifurcation modes to optical data squeezing and transmitting it across a 100-km-long single-mode fiber.

As the realm of spectral imaging applications extends its reach into the domains of mobile technology and augmented reality, the demands for compact yet high-fidelity systems become increasingly pronounced. Conventional methodologies, exemplified by coded aperture snapshot spectral imaging systems, are significantly limited by their cumbersome physical dimensions and form factors. To address this inherent challenge, diffractive optical elements (DOEs) have been repeatedly employed as a means to mitigate issues related to the bulky nature of these systems. Nonetheless, it's essential to note that the capabilities of DOEs primarily revolve around the modulation of the phase of light. Here, we introduce an end-to-end computational spectral imaging framework based on a polarization-multiplexed metalens. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high-fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial-spectral reconstruction performance, underscoring the efficacy of this system in practical, real-world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

In this paper, we develop an efficient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical integration. Firstly, we briefly review the principles of multipole decomposition, highlighting two numerical projection methods including surface and volume integration. Secondly, we discuss the Lebedev and Gaussian quadrature methods, provide a detailed recipe to select the quadrature points and the corresponding weighting factor, and illustrate the integration accuracy and numerical efficiency (that is, with very few sampling points) using a unit sphere surface and regular tetrahedron. In the demonstrations of an isotropic dielectric nanosphere, a symmetric scatterer, and an anisotropic nanosphere, we perform multipole decomposition and validate our numerical projection procedure. The obtained results from our procedure are all consistent with those from Mie theory, symmetry constraints, and finite element simulations.

We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature ($S \times T$) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal mol$^{-1}$ for $> 78$ \% and under 1.3 kcal mol$^{-1}$ for $> 92$ \% of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of $S \times T$ and ZPVE covering 36 kcal mol$^{-1}$ and 161 kcal mol$^{-1}$, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.

The study extends the structural ensemble dynamics theory of canonical wall turbulence to describe the mean velocity and Reynolds stresses profiles of equilibrium pressure-gradient (PG) turbulent boundary layers (TBLs). First, a modified defect power law is constructed to describe the total stress profiles of equilibrium PG TBLs. Crucially, a PG-induced characteristic stress is identified to determine the total stress variation. Second, four length functions are formulated with their dilation symmetry properties to quantify the Reynolds shear and normal stresses profiles. With a defect-power-law exponent, a nominal von K\'{a}rm\'{a}n constant, and a buffer layer thickness in the length functions, all dependently of PG, the entire profiles of the mean velocity, Reynolds shear and wall-normal normal stresses are accurately described for several equilibrium APG TBLs covering wide ranges of Reynolds number (Re) and PG. The streamwise and spanwise normal stresses are subjected to anormalous scaling modifications induced by APG with different mechanisms. With the modifications, the entire profiles of the streamwise and spanwise Reynolds normal stresses are accurately described. In particular, the outer-layer similarity among different Reynolds stress components is well captured. The study presents a novel approach for describing and predicting the mean-flow profiles of equilibrium PG TBLs with a limited number of empirical parameters, sound physical interpretations, and great accuracy, which paves a way to quantify the PG, Re, and other boundary-layer effects for a deep understanding of the flows.

We calibrate a Kuramoto model inspired representation of peer-to-peer collaboration using data on the maximum team-size where coordination breaks down. The Kuramoto model is modified, normalising the coupling by the degree of input and output nodes, reflecting dispersion of cognitive resources in both absorbing incoming-and tracking outgoing-information. We find a critical point, with loss of synchronisation as the number of nodes grows and analytically determine this point and calibrate the coupling with the known maximum team size. We test that against the known `span of control' for a leader/supervisor organisation. Our results suggest larger maximum team sizes than early management science proposes, but are consistent with recent studies.

Machine learning has great potential for efficient reconstruction and prediction of flow fields. However, existing datasets may have highly diversified labels for different flow scenarios, which are not applicable for training a model. To this end, we make a first attempt to apply the self-supervised learning (SSL) technique to fluid dynamics, which disregards data labels for pre-training the model. The SSL technique embraces a large amount of data ($8000$ snapshots) at Reynolds numbers of $Re=200$, $300$, $400$, $500$ without discriminating between them, which improves the generalization of the model. The Transformer model is pre-trained via a specially designed pretext task, where it reconstructs the complete flow fields after randomly masking $20\%$ data points in each snapshot. For the downstream task of flow reconstruction, the pre-trained model is fine-tuned separately with $256$ snapshots for each Reynolds number. The fine-tuned models accurately reconstruct the complete flow fields based on less than $5\%$ random data points within a limited window even for $Re=250$ and $600$, whose data were not seen in the pre-trained phase. For the other downstream task of flow prediction, the pre-training model is fine-tuned separately with $128$ consecutive snapshot pairs for each corresponding Reynolds number. The fine-tuned models then correctly predict the evolution of the flow fields over many periods of cycles. We compare all results generated by models trained via SSL and models trained via supervised learning, where the former has unequivocally superior performance. We expect that the methodology presented here will have wider applications in fluid mechanics

A hybrid method is developed to simulate two-phase flows with soluble surfactants. In this method, the interface and bulk surfactant concentration equations of diffuse-interface form, which include source terms to consider surfactant adsorption and desorption dynamics, are solved in the entire fluid domain by the finite difference method, while two-phase flows are solved by a lattice Boltzmann color-gradient model, which can accurately simulate binary fluids with unequal densities. The flow and interface surfactant concentration fields are coupled by a modified Langmuir equation of state, which allows for surfactant concentration beyond critical micelle concentration. The capability and accuracy of the hybrid method are first validated by simulating three numerical examples, including the adsorption of bulk surfactants onto the interface of a stationary droplet, the droplet migration in a constant surfactant gradient, and the deformation of a surfactant-laden droplet in a simple shear flow, in which the numerical results are compared with theoretical solutions and available literature data. Then, the hybrid method is applied to simulate the buoyancy-driven bubble rise in a surfactant solution, in which the influence of surfactants is identified for varying wall confinement, Eotvos number and Biot number. It is found that surfactants exhibit a retardation effect on the bubble rise due to the Marangoni stress that resists interface motion, and the retardation effect weakens as the Eotvos or Biot number increases. We further show that the weakened retardation effect at higher Biot numbers is attributed to a decreased non-uniform effect of surfactants at the interface.

It is shown that four-component (4C), quasi-four-component (Q4C), and exact two-component (X2C) relativistic Hartree-Fock (HF) equations can be implemented in an unified manner, by making use of the atomic nature of the small components of molecular 4-spinors. A model density matrix approximation can first be invoked for the small-component charge/current density functions, which gives rise to a static, pre-molecular mean field (pmf) to be combined with the one-electron term. As a result, only the nonrelativistic-like two-electron term of the 4C/Q4C/X2C Fock matrix needs to be updated during the iterations. A `one-center small-component' approximation can then be invoked in the evaluation of relativistic integrals. That is, all atom-centered small-component basis functions are regarded as extremely localized nearby the position of the atom to which they belong, such that they have vanishing overlaps with all small- or large-component functions centered at other nuclei. Under these approximations, the 4C, Q4C, and X2C mean-field and many-electron Hamiltonians share precisely the same structure and accuracy. Beyond these is the effective quantum electrodynamics Hamiltonian that can be constructed in the same way. Such approximations lead to errors that are orders of magnitude smaller than other sources of errors (e.g., truncation errors in the one- and many-particle bases as well as uncertainties of experimental measurements) and are hence safe to use for whatever purposes. The quaternion forms of the 4C, Q4C, and X2C equations are also presented in the most general way, based on which the corresponding Kramers-restricted open-shell (RKOHF) variants are formulated for `high-spin' open-shell systems.

Using a sample of a periodic structure [Mo/Si]$^{50}$, known as a multilayer X-ray mirror, the spectral-angular properties of diffracted transition radiation and parametric radiation in the ultrasoft X-ray region generated by the periodic structure upon interaction with a beam of relativistic electrons with an energy of 5.7 MeV are numerically studied. Based on calculations, an experimental approach is proposed for separating and identifying the contributions of PXR and DTR. The ultrasoft X-ray radiation under study can be useful for eliminating coherent effects occurring in the optical range when diagnosing the size of submicron electron beams.

We treat the accurate simulation of the calcination reaction in particles, where the particles are large and, thus, the inner-particle processes must be resolved. Because these processes need to be described with coupled partial differential equations that must be solved numerically, the computation times for a single particle are too high for use in simulations that involve many particles. Simulations of this type arise when the Discrete Element Method (DEM) is combined with Computational Fluid Dynamics (CFD) to investigate industrial systems such as quick lime production in lime shaft kilns. We show that, based on proper orthogonal de-composition and Galerkin projection, reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational cost. Replacing the finite-volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 60% for the simple example treated here.

FNO and DeepONet are by far the most popular neural operator learning algorithms. FNO seems to enjoy an edge in popularity due to its ease of use, especially with high dimensional data. However, a lesser-acknowledged feature of DeepONet is its modularity. This feature allows the user the flexibility of choosing the kind of neural network to be used in the trunk and/or branch of the DeepONet. This is beneficial because it has been shown many times that different types of problems require different kinds of network architectures for effective learning. In this work, we will take advantage of this feature by carefully designing a more efficient neural operator based on the DeepONet architecture. We introduce U-Net enhanced DeepONet (U-DeepONet) for learning the solution operator of highly complex CO2-water two-phase flow in heterogeneous porous media. The U-DeepONet is more accurate in predicting gas saturation and pressure buildup than the state-of-the-art U-Net based Fourier Neural Operator (U-FNO) and the Fourier-enhanced Multiple-Input Operator (Fourier-MIONet) trained on the same dataset. In addition, the proposed U-DeepONet is significantly more efficient in training times than both the U-FNO (more than 18 times faster) and the Fourier-MIONet (more than 5 times faster), while consuming less computational resources. We also show that the U-DeepONet is more data efficient and better at generalization than both the U-FNO and the Fourier-MIONet.

An optomechanical microcavity can considerably enhance the interaction between light and mechanical motion by confining light to a sub-wavelength volume. However, this comes at the cost of an increased optical loss rate. Therefore, microcavity-based optomechanical systems are placed in the unresolved-sideband regime, preventing sideband-based ground-state cooling. A pathway to reduce optical loss in such systems is to engineer the cavity mirrors, i.e., the optical modes that interact with the mechanical resonator. In our work, we analyze such an optomechanical system, whereby one of the mirrors is strongly frequency-dependent, i.e., a suspended Fano mirror. This optomechanical system consists of two optical modes that couple to the motion of the suspended Fano mirror. We formulate a quantum-coupled-mode description that includes both the standard dispersive optomechanical coupling as well as dissipative coupling. We solve the Langevin equations of the system dynamics in the linear regime showing that ground-state cooling from room temperature can be achieved even if the cavity is per se not in the resolved-sideband regime, but achieves effective sideband resolution through strong optical mode coupling. Importantly, we find that the cavity output spectrum needs to be properly analyzed with respect to the effective laser detuning to infer the phonon occupation of the mechanical resonator. Our work also predicts how to reach the regime of nonlinear quantum optomechanics in a Fano-based microcavity by engineering the properties of the Fano mirror.

We demonstrate an integrated electro-optically tunable narrow-linewidth III-V laser with an output power of 738.8 {\mu}W and an intrinsic linewidth of 45.55 kHz at the C band. The laser cavity is constructed using a fiber Bragg grating (FBG) and a tunable Sagnac loop reflector (TSLR) fabricated on thin film lithium niobate (TFLN). The combination of the FBG and the electro-optically tunable TSLR offers the advantages of single spatial mode, single-frequency, narrow-linewidth, and wide wavelength tunability for the electrically pumped hybrid integrated laser, which features a frequency tuning range of 20 GHz and a tuning efficiency of 0.8 GHz/V.

This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing as a method to gain deeper insights. In this context, we discuss network clustering, media influence, and consensus building. The location and distribution of dimers will be analyzed to gain insight into the structure and dynamics of the network. Dimertiling has been applied in various fields other than network analysis, such as physics and sociology. The paper concludes by emphasizing the importance of diverse perspectives, network analysis, and influential entities in consensus building. It also introduces torus-based visualizations that aid in understanding complex network structures.

In low energy atom-surface scattering, it is possible for the atom to be reflected in a region of attractive potential with no classical turning point. This phenomenon has come to be known as quantum reflection and it can reduce the sticking probability of atoms to surfaces, as well be used for atom trapping. We simulate the quantum reflection process in a one-dimensional model with a slow-moving atom moving in a Morse potential in the presence of an applied laser field. We show that in the case of laser-assisted quantum reflection, the laser field imparts additional momentum and kinetic energy to the atom. This results in a decreased distance of closest approach between the atom and surface. Our results show that the distance of closest approach and can be controlled through the timing and intensity of the laser pulse, which may result in enhanced sticking probability and/or reduced quantum reflection probability.

An investigation of turbulent boundary layers (TBLs) is presented for a NACA0012 airfoil at angles of attack 9 and 12 deg. Wall-resolved large eddy simulations (LES) are conducted for a freestream Mach number M = 0.2 and chord-based Reynolds number Re = 4x10^5, where the boundary layers are tripped near the airfoil leading edge on the suction side. For the angles of attack analyzed, mild, moderate and strong adverse pressure gradients (APGs) develop over the airfoil. Despite the strong APGs, the mean flow remains attached along the entire airfoil suction side. Similarly to other APG-TBLs investigated in the literature, a secondary peak appears in the Reynolds stress and turbulence production profiles. This secondary peak arises in the outer layer and, for strong APGs, it may overcome the first peak typically observed in the inner layer. The analysis of the turbulence production shows that other components of the production tensor become important in the outer layer besides the shear term. For moderate and strong APGs, the mean velocity profiles depict three inflexion points, the third being unstable under inviscid stability criteria. In this context, an embedded shear layer develops along the outer region of the TBL leading to the formation of two-dimensional rollers typical of a Kelvin-Helmholtz instability which are captured by a spectral proper orthogonal decomposition (SPOD) analysis. The most energetic SPOD spatial modes of the tangential velocity show that streaks form along the airfoil suction side and, as the APG becomes stronger, they grow along the spanwise and wall-normal directions, having a spatial support along the entire boundary layer.

Molecular systems can perform discrete computations by converting between different steady-state probability distributions over system states. Rather than using equilibrium steady-states for computation, biology uses thermodynamic forces to access non-equilibrium steady-states, which require constant energy expenditure to maintain even when no computations are being performed. The information-theoretic advantage of this costly paradigm is poorly understood. Here, we derive a concise expression relating the thermodynamic force to the information gain achievable in any molecular system performing an arbitrary computational step, thereby quantifying the maximum advantage derived from non-equilibrium driving. Numerical sampling demonstrates the tightness of this universal bound, and as examples, we show that Ras signaling and microtubule assembly can closely approach it.

Imperfections and uncertainties in forecast models are often represented in ensemble prediction systems by stochastic perturbations of model equations. In this article, we present a new technique to generate model perturbations. The technique is termed Additive Model-uncertainty perturbations scaled by Physical Tendencies (AMPT). The generated perturbations are independent between different model variables and scaled by the local-area-averaged modulus of physical tendency. The previously developed Stochastic Pattern Generator is used to generate space and time-correlated pseudo-random fields. AMPT attempts to address some weak points of the popular model perturbation scheme known as Stochastically Perturbed Parametrization Tendencies (SPPT). Specifically, AMPT can produce non-zero perturbations even at grid points where the physical tendency is zero and avoids perfect correlations in the perturbation fields in the vertical and between different variables. Due to a non-local link from physical tendency to the local perturbation magnitude, AMPT can generate significantly greater perturbations than SPPT without causing instabilities. Relationships between biases and spreads caused by AMPT and SPPT were studied in an ensemble of forecasts. The non-hydrostatic, convection-permitting forecast model COSMO was used. In ensemble prediction experiments, AMPT perturbations led to statistically significant improvements (compared to SPPT) in probabilistic performance scores such as spread-skill relationship, CRPS, Brier Score, and ROC area for near-surface temperature. AMPT had similar but weaker effects on near-surface wind speed and mixed effects on precipitation.

Thin-film lithium niobate (TFLN) has a proven record of building high-performance electro-optical (EO) modulators. However, its CMOS incompatibility and the need for non-standard etching have consistently posed challenges in terms of scalability, standardization, and the complexity of integration. Heterogeneous integration comes to solve this key challenge. Micro-transfer printing of thin-film lithium niobate brings TFLN to well-established silicon ecosystem by easy "pick and place", which showcases immense potential in constructing high-density, cost-effective, highly versatile heterogeneous integrated circuits. Here, we demonstrated for the first time a micro-transfer-printed thin film lithium niobate (TFLN)-on-silicon ring modulator, which is an important step towards dense integration of performant lithium niobate modulators with compact and scalable silicon circuity. The presented device exhibits an insertion loss of -1.5dB, extinction ratio of -37dB, electro-optical bandwidth of 16GHz and modulation rates up to 45Gps.

A novel analytic approach for calculating the brightness of para-hydrogen moderators is introduced. Because brightness gain originates from a near-the-surface effect resulting from the prevailing single-collision process during thermal-to-cold neutron conversion, high-aspect ratio rectangular cold moderators offer a significant increase, up to a factor of 10 compared to classic voluminous moderators, in cold neutron brightness. The obtained results are in excellent agreement with MCNP calculations. The concept of "low-dimensionality" in moderators is explored, demonstrating that achieving a substantial increase in brightness necessitates moderators to be low-dimensional both geometrically, implying a high aspect ratio, and physically, requiring the moderator's smallest dimension to be smaller than the double mean free path of thermal neutrons in para-hydrogen. This explains why additional compression of the flat moderator, effectively giving it a tube-like shape, does not result in a brightness increase comparable to the flattening of moderator. Considering that the high brightness of such cold moderators is associated with neutron beams of reduced width and proportionally decreased intensity, we propose staircase and chessboard moderator assemblies. These configurations generate wider neutron beams with higher intensity while preserving the enhanced brightness of high-aspect ratio individual moderators. It is demonstrated that implementing a cold neutron source based on such assemblies can yield a significant gain both in brightness and in intensity -- up to a factor 2-2.5 compared to a source using a single flat moderator with the same width.

Modeling plasmas in terms of atoms or ions is theoretically appealing for several reasons. When it is relevant, the notion of atom or ion in a plasma provides us with an interpretation scheme of the plasma's internal functioning. From the standpoint of quantitative estimation of plasma properties, atomic models of plasma allow to extend many theoretical tools of atomic physics to plasmas. This notably includes the statistical approaches to the detailed accounting for excited states, or the collisional-radiative modeling of non-equilibrium plasmas, which is based on the notion of atomic processes. In this paper, we focus on the challenges raised by the atomic modeling of dense, non-ideal plasmas. First we make a brief, non-exhaustive review of atomic models of plasmas, from ideal plasmas to strongly-coupled and pressure-ionized plasmas. We discuss the limitations of these models and pinpoint some open problems in the field of atomic modeling of plasmas. We then address the peculiarities of atomic processes in dense plasmas and point out some specific issues relative to the calculation of their cross-sections. In particular, we discuss the modeling of fluctuations, the accounting for channel mixing and collective phenomena in the photoabsorption, or the impact of pressure ionization on collisional processes.

A formulation for the efficient calculation of the electromagnetic retarded potential generated by time-dependent electron density in the context of real-time time dependent density functional theory (RT-TDDFT) is presented. The electron density is considered to be spatially separable, which is suitable for systems that include several molecules or nano-particles. The formulation is based on splitting the domain of interest into sub-domains and calculating the time dependent retarded potentials from each sub-domain separately. The computations are accelerated by using the fast Fourier transform and parallelization. We demonstrate this formulation by solving the orbitals dynamics in systems of two molecules at varied distances. We first show that for small distances we get exactly the results that are expected from non-retarded potentials, we then show that for large distances between sub-domains we observe substantial retardation effects.

The Hefei Advanced Light Facility (HALF) will be a VUV and soft X-ray diffraction-limited storage ring (DLSR), and its high density of electron bunches makes the intra-beam scattering (IBS) effect very serious. In this paper, an IBS module used in the IMPACT code is developed, where the scattering process of IBS is described by the Piwinski model in Monte Carlo sampling. For benchmarking, the IMPACT code with IBS module is compared with the ELEGANT code and a semi-analytic code using Bane's model. Then, the results of IBS effect in the HALF storage ring studied by this new code are presented. With various countermeasures, the IBS impact can be controlled to a certain extent, and the expected beam emittance is approximately 59 pm.rad.

In this paper, we report on the implementation of CC2 and CC3 in the context of molecules in finite magnetic fields. The methods are applied to the investigation of atoms and molecules through spectroscopic predictions and geometry optimizations for the study of the atmospheres of highly-magnetized White Dwarfs (WDs). We show that ground-state finite-field (ff) CC2 is a reasonable alternative to CCSD for energies and, in particular for geometrical properties. For excited states ff-CC2 is shown to perform well for states with predominant single-excitation character. Yet, for cases in which the excited-state wavefunction has double-excitation character with respect to the reference, ff-CC2 can easily Ff-CC3, however, is shown to reproduce the CCSDT behaviour very well and enables the treatment of larger systems at a high accuracy.

We develop protocols to confine charged particles using time-varying magnetic fields and demonstrate the possible non-torus configuration resulting from the distribution of single-particle motion orbits. A two-step strategy is proposed to achieve this goal: preliminary protocols are contrived by solely considering the magnetic force; afterwards they are evaluated and selected through numerical solutions to the equation of motion, taking into account inductive electric fields. It is shown that a fine-tuned tangent-pulse protocol can maintain its centralized configuration even in the presence of associated electric fields, which illuminates an alternative approach to designing the confinement scenario for fusion plasmas.

The diversity of expressed genes plays a critical role in cellular specialization, adaptation to environmental changes, and overall cell functionality. This diversity varies dramatically across cell types and is orchestrated by intricate, dynamic, and cell type-specific gene regulatory networks (GRNs). Despite extensive research on GRNs, their governing principles, as well as the underlying forces that have shaped them, remain largely unknown. Here, we investigated whether there is a tradeoff between the diversity of expressed genes and the intensity of GRN interactions. We have developed a computational framework that evaluates GRN interaction intensity from scRNA-seq data and used it to analyze simulated and real scRNA-seq data collected from different tissues in humans, mice, fruit flies, and C. elegans. We find a significant tradeoff between diversity and interaction intensity, driven by stability constraints, where the GRN could be stable up to a critical level of complexity - a product of gene expression diversity and interaction intensity. Furthermore, we analyzed hematopoietic stem cell differentiation data and find that the overall complexity of unstable transition states cells is higher than that of stem cells and fully differentiated cells. Our results suggest that GRNs are shaped by stability constraints which limit the diversity of gene expression.

Electrochromic optical recording (ECORE) is a label-free method that utilizes electrochromism to optically detect electrical signals in biological cells with a high signal-to-noise ratio and is suitable for long-term recording. However, ECORE usually requires a large and intricate optical setup, making it relatively difficult to transport and to study specimens on a large scale. Here, we present a compact ECORE apparatus that drastically reduces the spatial footprint and complexity of the ECORE setup whilst maintaining high sensitivity. An autobalancing differential photodetector automates common-mode noise rejection, removing the need for manually adjustable optics, and a compact laser module conserves space compared to a typical laser mount. The result is a simple, easy-to-use, and relatively low cost system that achieves a sensitivity of 16.7 {\mu}V (within a factor of 5 of the shot noise limit), and reliably detects action potentials from Human-induced pluripotent stem cell (HiPSC) derived cardiomyocytes. This setup can be further improved to within 1.5 dB of the shot noise limit by filtering out power-line interference.

Most recently, a brand new optical phenomenon, collectively induced transparency (CIT) has already been proposed in the cavity quantum electrodynamics system, which comes from the coupling between the cavity and ions and the quantum interference of collective ions. Due to the equivalent analogue of quantum optics, metamaterial also is a good platform to realize collectively induced transparency (CIT) which can be useful for highly sensitive metamaterial sensors, optical switches and photo-memory. In this paper, we propose the coupling of bright mode and interference of dark modes, to realize the CIT in terahertz (THz) metamaterial system. We give the theoretical analysis, analytical solutions, simulations and experiments to demonstrate our idea.

The oxide-metal-oxide architecture is a promising approach for the development of the high-performance indium-free transparent electrode (TE), which is a key component of various optoelectronic applications such as solar cells, organic LEDs, and touchscreen panels. Here in this work, we have shown high-performance TE consisting of TiO2/Ag/TiO2 (TAT), with the incorporation of a copper seed layer. The seed layer increases the wettability and improves the adhesion of deposited Ag film on the bottom TiO2 layer. Before the experimental realization, optical modeling is performed by using MATLAB code based on the transfer matrix method. The optimum thickness obtained from the simulation is 30 nm for both undercoat and overcoat TiO2 with the average transmittance in the visible region >85% with the Ag thickness of 9nm. With inputs from the optical modeling, TEs were experimentally realized with and without the Cu seed layer. It has been found that the TE with an additional sputtered Cu (1 nm) seed layer is essential for the smooth growth of silver film and shows better electro-optical performance (sheet resistance < 10 and average transmittance in the visible spectral range > 80%) than TAT-TE without any seed layer. The electro-optical and morphological properties of the TiO2/Cu/Ag/TiO2 structure make it suitable for optoelectronic applications.

Noise efficiency factor (NEF) and power efficiency factor (PEF) are widely used as the figure of merit to quantify the performance of biopotential recording front-ends. NEF and PEF are discussed from the noise analysis to the trend survey. To provide a comprehensive performance comparison of the front-ends, the performance mapping is developed using the design parameters of the technology node, NEF, PEF, |PEF - NEF|, and supply voltage. Using |PEF - NEF| provides how well a front-end balances between current-noise efficiency and power-noise efficiency, in other words, how biased a front-end is between current- and power-noise efficiencies. Also, the performance mappings of different front-end architectures are presented.

Caustics occur in diverse physical systems, spanning the nano-scale in electron microscopy to astronomical-scale in gravitational lensing. As envelopes of rays, optical caustics result in sharp edges or extended networks. Caustics in structured light, characterized by complex-amplitude distributions, have innovated numerous applications including particle manipulation, high-resolution imaging techniques, and optical communication. However, these applications have encountered limitations due to a major challenge in engineering caustic fields with customizable propagation trajectories and in-plane intensity profiles. Here, we introduce the compensation phase via 3D-printed metasurfaces to shape caustic fields with curved trajectories in free space. The in-plane caustic patterns can be preserved or morphed from one structure to another during propagation. Large-scale fabrication of these metasurfaces is enabled by the fast-prototyping and cost-effective two-photon polymerization lithography. Our optical elements with the ultra-thin profile and sub-millimeter extension offer a compact solution to generating caustic structured light for beam shaping, high-resolution microscopy, and light-matter-interaction studies.

Photo-assisted tunneling in scanning tunneling microscopy has attracted considerable interest to combine sub-picosecond and sub-nanometer resolutions. The illumination of a junction with visible or infrared light, however, induces thermal expansion of the tip and the sample, which strongly affects the measurements. Employing free-space THz pulses instead of visible light has been proposed to solve these thermal issues while providing photo-induced currents of similar magnitude. Here we compared the impact of illuminating the same tunneling junction, reaching comparable photo-induced current, with red light and with THz radiations. Our data provide a clear and direct evidence of thermal expansion with red light-illumination, while such thermal effects are negligible with THz radiations.

Objective: Clinical applications of FLASH radiotherapy require a model to describe how the FLASH radiation features and other related factors determine the FLASH effect. Mathematical analysis of the models can connect the theoretical hypotheses with the radiobiological effect, which provides the foundation for establishing clinical application models. Moreover, experimental and clinical data can be used to explore the key factors through mathematical analysis. Approach: We abstract the complex models of the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis into concise equations. Then, the equations are solved to analyze how the radiation features and other factors influence the FLASH effect. Additionally, we show how to implement the hypotheses' models in clinical application with the example of fitting the experimental data and predicting the biological effects. Main results: The formulas linking the physical, chemical and biological factors to the FLASH effect are obtained through mathematical solutions and analysis of the equations. These formulas will enable the utilization of experimental and clinical data in clinical applications by fitting the data to the formulas. Based on this analysis, we propose suggestions for systematic experiments toward clinical FLASH radiotherapy. Significance: Our work derives the mathematical formulas that elucidate the relationship between factors in the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis, and the FLASH effect. These mathematical formulas provide the theoretical basis for developing the clinical application models for FLASH radiotherapy. Furthermore, the analysis of these hypotheses indicates the key factors of the FLASH effect and offers references for the design of systematic experiments toward clinical applications.

Rydberg atoms in optical lattices and tweezers is now a well established platform for simulating quantum spin systems. However, the role of the atoms' spatial wavefunction has not been examined in detail experimentally. Here, we show a strong spin-motion coupling emerging from the large variation of the interaction potential over the wavefunction spread. We observe its clear signature on the ultrafast, out-of-equilibrium, many-body dynamics of atoms excited to a Rydberg S state from an unity-filling atomic Mott-insulator. We also propose a novel approach to tune arbitrarily the strength of the spin-motion coupling relative to the motional energy scale set by trapping potentials. Our work provides a new direction for exploring the dynamics of strongly-correlated quantum systems by adding the motional degree of freedom to the Rydberg simulation toolbox.

Low-refractive-index (LRI) particles play significant roles in physics, drug delivery, biomedical science, and other fields. However, they have not attained sufficient utilization in active manipulation due to the repulsive effect of light. Here, we demonstrate the establishment of optical building blocks (OBBs) to fulfill the demands of versatile manipulation of LRI particles. The OBBs are generated by assembling generalized perfect optical vortices based on the free lens modulation (FLM) method, by which the beams shape, intensity, and position can be elaborately designed with size independent of topological charge. Using the OBBs with high quality and high efficiency, we realized rotating LRI particles along arbitrary trajectories with controllable speed and parallel manipulation of multiple LRI particles. Importantly, we further achieved the sorting of LRI particles by size with specially structured OBBs. With unprecedented flexibility and quality, OBBs provide tremendous potential in optical trapping, lithography, and biomedicine.

Non-diffracting beams, notable for their self-healing properties, high-localized intensity profiles over extended propagation distances, and resistance to diffraction, present significant utility across various fields. In this letter, we present a versatile and highly efficient non-diffracting beams (NDBs) generator predicated on the Fourier transformation of the focal plane fields produced through the free lens modulation. We demonstrate the experimental generation of high-quality Bessel beams, polymorphic generalized NDBs, tilted NDBs, asymmetric NDBs, and specially structured beams formed by the superposition of co-propagating beams. The versatile generalized NDBs generator is anticipated to find applications in laser processing, optical manipulation, and other fields.

Turbulent flow over permeable interface is omnipresent featuring complex flow topology. In this work, a data driven, end to end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have derived a non linear reduced order model with a deep convolution autoencoder network. This model can reduce highly resolved spatial dimensions, which is a prerequisite for direct numerical simulation. A downstream recurrent neural network has been trained to capture the temporal trend of reduced modes, thus it is able to provide future evolution of modes. We further evaluate the trained model s capability on a newer dataset with a different porosity. In such cases, fine tuning could reduce the efforts (up to two order of magnitude) to train a model with limited dataset and knowledge and still show a good agreement on the mean velocity profile. Leveraging the current model, we find that even quick fine tuning achieving an impressive order of magnitude reduction in training time by approximately still results in effective flow predictions. This promising discovery encourages the fast development of a substantial amount of data-driven models tailored for various types of porous media. The diminished training time substantially lowers the computational cost when dealing with changing porous topologies, making it feasible to systematically explore interface engineering with different types of porous media. Overall, the data driven model shows a good agreement, especially for the porous media which can aid the DNS and reduce the burden to resolve this complex domain during the simulations. The fine tuning is able to reduce the training cost significantly and maintain an acceptable accuracy when a new flow condition comes into play.

The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data. Therefore, this paper proposes a physics-fusion dynamic mode decomposition (PFDMD) method to address this issue. The proposed PFDMD method first obtains a data-driven model using DMD, then calculates the residual of the physical equations, and finally corrects the predicted results using Kalman filtering and gain coefficients. In this way, the PFDMD method can integrate the physics-informed equations with the data-driven model generated by DMD. Numerical experiments are conducted using the PFDMD, including the Allen-Cahn, advection-diffusion, and Burgers' equations. The results demonstrate that the proposed PFDMD method can significantly reduce the reconstruction and prediction errors by incorporating physics-informed equations, making it usable for translation and shock problems where the standard DMD method has failed.

Resonant enhancement of nonlinear photonic processes is critical for the scalability of applications such as long-distance entanglement generation. To implement nonlinear resonant enhancement, multiple resonator modes must be individually tuned onto a precise set of process wavelengths, which requires multiple linearly-independent tuning methods. Using coupled auxiliary resonators to indirectly tune modes in a multi-resonant nonlinear cavity is particularly attractive because it allows the extension of a single physical tuning mechanism, such as thermal tuning, to provide the required independent controls. Here we model and simulate the performance and tradeoffs of a coupled-resonator tuning scheme which uses auxiliary resonators to tune specific modes of a multi-resonant nonlinear process. Our analysis determines the tuning bandwidth for steady-state mode field intensity can significantly exceed the inter-cavity coupling rate if the total quality factor of the auxiliary resonator is higher than the multi-mode main resonator. Consequently, over-coupling a nonlinear resonator mode to improve the maximum efficiency of a frequency conversion process will simultaneously expand the auxiliary resonator tuning bandwidth for that mode, indicating a natural compatibility with this tuning scheme. We apply the model to an existing small-diameter triply-resonant ring resonator design and find that a tuning bandwidth of 136 GHz ~ 1.1 nm can be attained for a mode in the telecom band while limiting excess scattering losses to a quality factor of 10^6. Such range would span the distribution of inhomogeneously broadened quantum emitter ensembles as well as resonator fabrication variations, indicating the potential for the auxiliary resonators to enable not only low-loss telecom conversion but also the generation of indistinguishable photons in a quantum network.

Oblique plane microscopy is a method enabling light-sheet fluorescence imaging through a single microscope objective lens by focusing on a tilted plane within the sample. To focus the fluorescence emitted by the oblique plane on a camera, the light is imaged through a pair of remote objective lenses, facing each other at an angle. The aperture mismatch resulting from this configuration limits the effective numerical aperture of the system, reducing image resolution and signal intensity. This manuscript introduces an alternative method to capture the oblique plane on the camera. Instead of relying on angled objective lenses, an electrically tunable lens is employed. This lens adjusts the focal plane of the microscope synchronously with the rolling shutter of a scientific CMOS camera. In this configuration the entire aperture of the objective is effectively employed, increasing the resolution of the system. Moreover, a variety of objective lenses can be employed, enabling the acquisition of wider axial fields of view compared to conventional oblique plane microscopy.

Endoscopic optical coherence tomography (OCT) is a valuable tool for providing diagnostic images of internal organs and guiding interventions in real time. Miniaturized OCT endoscopes are essential for imaging small and convoluted luminal organs while minimizing invasiveness. However, current methods for fabricating miniature fiber probes have limited ability to correct optical aberrations, leading to suboptimal imaging performance. In this study, we introduce a new paradigm of liquid shaping technique for the rapid and scalable fabrication of ultrathin and high-performance OCT microendoscopes suitable for minimally invasive clinical applications. This technique enables the flexible customization of freeform microlenses with sub-nanometer optical surface roughness by regulating the minimum energy state of curable optical liquid on a wettability-modified substrate and precisely controlling the liquid volume and physical boundary on a substrate. Using this technique, we simultaneously fabricated 800-nm OCT microendoscopes with a diameter of approximately 0.6 mm and evaluated their ultrahigh-resolution imaging performance in the esophagus of rats and the aorta and brain of mice.

Particle transports in carriers with even-odd alternating dispersions (introduced in Part I) are investigated. For the third-order dispersion as in Korteweg-de-Vries (KdV), such alternating dispersion has the effects of not only regularizing the velocity from forming shock singularity (thus the attenuation of particle clustering strength) but also symmetrizing the oscillations (thus the corresponding skewness of the particle densities), among others, as demonstrated numerically. The analogy of such dispersion effects and consequences (on particle transports in particular) with those of helicity in Burgers turbulence, addressed in the context of astrophysics and cosmology, is made for illumination and promoting models. Among many details, a reward from the study of particle transports is the understanding of the (asymptotic) $k^0$-scaling (equipartition among the wavenumbers, $k$s), before large-$k$ exponential decay, of the power spectrum of KdV solitons (resulting in the statement that "a soliton is the derivative of the classical shock, just like the Dirac delta is the derivative of a step function"), motivated by the explanation of the the same scaling of the particle densities as the apparent approximation of the Dirac deltas due to particle clustering; while, the "shocliton" from the even-odd alternating dispersion in aKdV appears to be, indeed, $shock \oplus soliton$, accordingly the decomposition of the averaged odd-mode spectrum, from sinusoidal initial field, into a $k^{-2}$ part for the shock and a $k^0$-scaling part for the solitonic pulses, only the latter being contained in the averaged even-mode spectrum.

The reconstruction task in photoacoustic tomography can vary a lot depending on measured targets, geometry, and especially the quantity we want to recover. Specifically, as the signal is generated due to the coupling of light and sound by the photoacoustic effect, we have the possibility to recover acoustic as well as optical tissue parameters. This is referred to as quantitative imaging, i.e, correct recovery of physical parameters and not just a qualitative image. In this chapter, we aim to give an overview on established reconstruction techniques in photoacoustic tomography. We start with modelling of the optical and acoustic phenomena, necessary for a reliable recovery of quantitative values. Furthermore, we give an overview of approaches for the tomographic reconstruction problem with an emphasis on the recovery of quantitative values, from direct and fast analytic approaches to computationally involved optimisation based techniques and recent data-driven approaches.

We propose a joint experimental and theoretical approach to measure the self-diffusion in a laser-cooled trapped ion cloud where part of the ions are shelved in a long-lived dark state. The role of the self-diffusion coefficient in the spatial organisation of the ions is deciphered, following from the good agreement between the experimental observations and the theoretical predictions. This comparison furthermore allows to deduce the temperature of the sample. Protocols to measure the self-diffusion coefficient are discussed, in regard with the control that can be reached on the relevant time scales through the dressing of the atomic levels by laser fields.

Geometrical distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. Yet, agent-based studies supported by granular analysis of distances are limited. Here, we develop a complexity method that places the real physical world, represented by the actual geographical location of individual firms in Japan, at the epicentre of our research. By combining methods derived from network science (to evaluate the emerging properties of the agents) together with information theory measures (to capture the strength of interaction among these agents), we can systematically analyse a comprehensive dataset of Japanese inter-firm business transactions network and evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of distances between interacting firms show a power law like decay concomitant to the sizes of firms and regions, with slower decays in major cities. Furthermore, small firms would reach large distances to become a customer of large firms while trading between either only small firms, or only large firms, tends to be at smaller distances. However, a time evolution analysis suggests that a level of market optimisation occurs over time as a reduction in the overall average trading distances in last 20 years can be observed. Lastly, our analysis concerning the trading dynamics among prefectures indicate that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations, leading to the formation of three distinct types of regional geographical clusters.

Off-axis twisted waveguides possess unique optical properties such as circular and orbital angular momentum (OAM) birefringence, setting them apart from their straight counterparts. Analyzing mode formation in such helical waveguides relies on the use of specific coordinate frames that follow the twist of the structure, making the waveguide invariant along one of the new coordinates. In this study, the differences between modes forming in high-contrast off-axis twisted waveguides defined in the three most important coordinate systems - the Frenet-Serret, the helicoidal, or the Overfelt frame - are investigated through numerical simulations. We explore modal characteristics up to high twist rates (pitch: 50 $\mu$m) and clarify a transformation allowing to map the modal fields and the effective index back to the laboratory frame. In case the waveguide is single-mode, the fundamental modes of the three types of waveguides show significant differences in terms of birefringence, propagation loss, and polarization. Conversely, the modal characteristics of the investigated waveguides are comparable in the multimode domain. Furthermore, our study examines the impact of twisting on spatial mode properties with the results suggesting a potential influence of the photonic spin Hall and orbital Hall effects. Additionally, modes of single-mode helical waveguides were found to exhibit superchiral fields on their surfaces. Implementation approaches such as 3D-nanoprinting or fiber-preform twisting open the doors to potential applications of such highly twisted waveguides, including chip-integrated devices for broadband spin- and OAM-preserving optical signal transport, as well as applications in chiral spectroscopy or nonlinear frequency conversion.

Metasurfaces and metagratings offers new platforms for electromagnetic wave control with significant responses. However, metasurfaces based on abrupt phase change and resonant structures suffer from the drawback of high loss and face challenges when applied in water waves. Therefore, the application of metasurfaces in water wave control is not ideal due to the limitations associated with high loss and other challenges. We have discovered that non-resonant metagratings exhibit promising effects in water wave control. Leveraging the similarity between bridges and metagratings, we have successfully developed a water wave metagrating model inspired by the Luoyang Bridge in ancient China. We conducted theoretical calculations and simulations on the metagrating and derived the equivalent anisotropic model of the metagrating. This model provides evidence that the metagrating has the capability to control water waves and achieve unidirectional surface water wave. The accuracy of our theory is strongly supported by the clear observation of the unidirectional propagation phenomenon during simulation and experiments conducted using a reduced version of the metagrating. It is the first time that the unidirectional propagation of water waves has been seen in water wave metagrating experiment. Above all, we realize the water wave metagrating experiment for the first time. By combining complex gratings with real bridges, we explore the physics embedded in the ancient building-Luoyang Bridge, which are of great significance for the water wave metagrating design, as well as the development and preservation of ancient bridges.

Radiative, gravitational surface waves are investigated at the interface of high density and low temperature, magnetized incompressible electron-ion plasmas in the presence of dense radiation electromagnetic radiation pressure (DEMRP). The inhomogeneous embedded is reported at plasma-vacuum interface. The DEMRP is found to stabilize the surface waves, however for a specific case, it tends to enhance the growth rate of surface waves via the frictional instability. The group velocity of gravitational radiation is shown to be the function of wavelength. The obtained analytical results are presented both numerically and graphically as function of DEMRP. to show that the incorporation of DEMRP may introduce quite different dispersive properties of charged surface wave phenomena. It is shown numerically that the frequency of the obtained radiative gravitational waves in the presence of DEMRP is found to lie in the range of high frequency radio waves, while in case of rare laboratory plasma, the frequency of these waves is found to lie within the very low frequency radio waves of the electromagnetic radiation spectrum. This work may enhance the gravitational aspects of electromagnetic radiations in dense astrophysical systems such as neutron star and white dwarfs.

We propose a novel approach to detect the binding between proteins making use of the anomalous diffraction of natively present heavy elements inside the molecule 3D structure. In particular, we suggest considering sulfur atoms contained in protein structures at lower percentages than the other atomic species. Here, we run an extensive preliminary investigation to probe both the feasibility and the range of usage of the proposed protocol. In particular, we (i) analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus the differences in the patterns produced by bound proteins with respect to their non-bonded states can be exploited to rapidly assess protein complex formation. Next, we (ii) carried out analyses on the abundances of sulfurs in the different proteomes and molecular dynamics simulations on a representative set of protein structures to probe the typical motion of sulfur atoms. Finally, we (iii) suggest a possible experimental procedure to detect protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale even between other biological molecules, thus paving the way to the development of a novel field of research based on a synchrotron light source.

Numerous natural and engineering scenarios necessitate entrapment of air pockets or bubbles on submerged surfaces, e.g., aquatic insects, smartphones, and membranes for separation and purification. Current technologies for bubble entrapment rely heavily on perfluorocarbon coatings, which limits their sustainability and applications. Here, we investigate doubly reentrant cavities, a biomimetic microtexture capable of entrapping air under wetting liquids, under static and dynamic pressure cycling. The effects of positive, negative, and positive-negative cycles are studied across a range of pressure amplitudes, ramp rates, intercycle intervals, and water column heights. Remarkably, the fate of the trapped air under pressure cycling falls into the following three distinct regimes: the bubble (i) monotonically depletes, (ii) remains indefinitely stable, or (iii) starts growing. This hitherto unrealized richness of underwater bubble dynamics will guide the development of coating-free underwater technologies and provide clues into the curious lives of air-breather aquatic/marine insects.

In recent years the fluid mechanics community has been intensely focused on pursuing solutions to its long-standing open problems by exploiting the new machine learning, (ML), approaches. The exchange between ML and fluid mechanics is bringing important paybacks in both directions. The first is benefiting from new physics-inspired ML methods and a scientific playground to perform quantitative benchmarks, whilst the latter has been open to a large set of new tools inherently well suited to deal with big data, flexible in scope, and capable of revealing unknown correlations. A special case is the problem of modeling missing information of partially observable systems. The aim of this paper is to review some of the ML algorithms that are playing an important role in the current developments in this field, to uncover potential avenues, and to discuss the open challenges for applications to fluid mechanics.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. Although tuning these coefficients can produce significantly improved predictive accuracy, their default values are often used. We believe users do not calibrate RANS models for several reasons: there is no clearly recommended framework to optimize these coefficients; the average user does not have the expertise to implement such a framework; and, the optimization of the values of these coefficients can be a computationally expensive process. In this work, we address these issues by proposing a semi-automated calibration of these coefficients using a new framework based on Bayesian optimization. We introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse, or dense reference data. We demonstrate the computationally efficient performance of turbo-RANS for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. In the first two examples, we calibrate the $k$-$\omega$ shear stress transport (SST) turbulence model and, in the last example, we calibrate user-specified coefficients for the Generalized $k$-$\omega$ (GEKO) model in Ansys Fluent. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. Towards the goal of facilitating RANS turbulence closure model calibration, we provide an open-source implementation of the turbo-RANS framework that includes OpenFOAM, Ansys Fluent, and solver-agnostic templates for user application.

Geodynamic modelling and seismic studies have highlighted the possibility that a thin layer of low seismic velocities, potentially molten, may sit atop the core-mantle boundary but has thus far eluded detection. In this study we employ normal modes, an independent data type to body waves, to assess the visibility of a seismically slow layer atop the core-mantle boundary to normal mode centre frequencies. Using forward modelling and a dataset of 353 normal mode observations we find that some centre frequencies are sensitive to one-dimensional kilometre-scale structure at the core-mantle boundary. Furthermore, a global slow and dense layer 1 - 3 km thick is better-fitting than no layer. The well-fitting parameter space is broad with a wide range of possible seismic parameters, which precludes inferring a possible composition or phase. Our methodology cannot uniquely detect a layer in the Earth but one should be considered possible and accounted for in future studies.

Transition frequencies and fine-structure splittings of the $2\,{}^3\!S_1 \rightarrow 2\,{}^3\!P_{\!J}$ transitions in helium-like $^{12}\mathrm{C}^{4+}$ were measured by collinear laser spectroscopy on a 1-ppb level. Accuracy is increased by more than three orders of magnitude with respect to previous measurements, enabling tests of recent non-relativistic QED calculations including terms up to $m\alpha^7$. Deviations between the theoretical and experimental values are within theoretical uncertainties and are ascribed to $m\alpha^8$ and higher-order contributions in the series expansion of the NR-QED calculations. Finally, prospects for an all-optical charge radius determination of light isotopes are evaluated.

Reducing the form factor while retaining the radiation hardness and performance matrix is the goal of avionics. While a compromise between a transistor s size and its radiation hardness has reached consensus in micro-electronics, the size-performance balance for their optical counterparts has not been quested but eventually will limit the spaceborne photonic instruments capacity to weight ratio. Here we performed the first space experiments of photonic integrated circuits (PICs), revealing the critical roles of energetic charged particles. The year long cosmic radiation does not change carrier mobility but reduces free carrier lifetime, resulting in unchanged electro-optic modulation efficiency and well expanded optoelectronic bandwidth. The diversity and statistics of the tested PIC modulator indicate the minimal requirement of shielding for PIC transmitters with small footprint modulators and complexed routing waveguides, towards lightweight space terminals for terabits communications and inter-satellite ranging.

Displacement current is the last piece of the puzzle of electromagnetic theory. Its existence implies that electromagnetic disturbance can propagate at the speed of light and finally it led to the discovery of Hertzian waves. On the other hand, since magnetic fields can be calculated only with conduction currents using Biot-Savart's law, a popular belief that displacement current does not produce magnetic fields has started to circulate. But some people think if this is correct, what is the displacement current introduced for. The controversy over the meaning of displacement currents has been going on for more than hundred years. Such confusion is caused by forgetting the fact that in the case of non-stationary currents, neither magnetic fields created by conduction currents nor those created by displacement currents can be defined. It is also forgotten that the effect of displacement current is automatically incorporated in the magnetic field calculated by Biot-Savart's law. In this paper, mainly with the help of Helmholtz decomposition, we would like to clarify the confusion surrounding displacement currents and provide an opportunity to end the long standing controversy.

Pinning of liquid droplets on solid substrates is ubiquitous and plays an essential role in many applications, especially in various areas, such as microfluidics and biology. Although pinning can often reduce the efficiency of various applications, a deeper understanding of this phenomenon can actually offer possibilities for technological exploitation. Here, by means of molecular dynamics simulation, we identify the conditions that lead to droplet pinning or depinning and discuss the effects of key parameters in detail, such as the height of the physical pinning-barrier and the wettability of the substrates. Moreover, we describe the mechanism of the barrier crossing by the droplet upon depinning, identify the driving force of this process, and, also, elucidate the dynamics of the droplet. Not only does our work provide a detailed description of the pinning and depinning processes, but it also explicitly highlights how both processes can be exploited in nanotechnology applications to control droplet motion. Hence, we anticipate that our study will have significant implications for the nanoscale design of substrates in micro and nano-scale systems and will assist with assessing pinning effects in various applications.

Simulating spontaneous structural rearrangements in macromolecules with classical Molecular Dynamics (MD) is an outstanding challenge. Conventional supercomputers can access time intervals up to tens of $\mu$s, while many key events occur on exponentially longer time scales. Transition path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore diverse conformational regions remains an unsolved problem. We employ a path-sampling paradigm combining machine learning (ML) with quantum computing (QC) to address this issue. We use ML on a classical computer to perform a preliminary uncharted exploration of the conformational space. The data set generated in this exploration is then post-processed to obtain a network representation of the reactive kinetics. Quantum annealing machines can exploit quantum superposition to encode all the transition pathways in this network in the initial quantum state and ensure the generation of completely uncorrelated transition paths. In particular, we resort to the DWAVE quantum computer to perform an all-atom simulation of a protein conformational transition that occurs on the ms timescale. Our results match those of a special purpose supercomputer designed to perform MD simulations. These results highlight the role of biomolecular simulation as a ground for applying, testing, and advancing quantum technologies.

We present a novel cryogenic VUV spectrofluorometer designed for the characterization of wavelength shifters (WLS) crucial for experiments based on liquid argon (LAr) scintillation light detection. Wavelength shifters like 1,1,4,4-tetraphenyl-1,3-butadiene (TPB) or polyethylene naphthalate (PEN) are used in these experiments to shift the VUV scintillation light to the visible region. Precise knowledge of the optical properties of the WLS at liquid argon's temperature (87 K) and LAr scintillation wavelength (128 nm) is necessary to model and understand the detector response. The cryogenic VUV spectrofluorometer was commissioned to measure the emission spectra and relative wavelength shifting efficiency (WLSE) of samples between 300 K and 87 K for VUV (128 nm) and UV (310 nm) excitation. New mitigation techniques for surface effects on cold WLS were established. As part of this work, the TPB-based wavelength shifting reflector (WLSR) featured in the neutrinoless double-beta decay experiment LEGEND-200 was characterized. The wavelength shifting efficiency was observed to increase by (54 +/- 4)% from room temperature (RT) to 87 K. PEN installed in LEGEND-200 was also characterized and a first measurement of the relative wavelength shifting efficiency and emission spectrum at RT and 87 K is presented. Surface effects from cooling were corrected by normalizing the measurement at VUV excitation with the respective ones at UV excitation. The WLSE of amorphous PEN was found to be enhanced by (49 +/- 1)% at 87 K compared to RT.

Droplet nucleation and evaporation are ubiquitous in nature and many technological applications, such as phase-change cooling and boiling heat transfer. So far, the description of these phenomena at the molecular scale has posed challenges for modelling with most of the models being implemented on a lattice. Here, we propose an {off-lattice} Monte-Carlo approach combined with a grid that can be used for the investigation of droplet formation and evaporation. We provide the details of the model, its implementation as Python code, and results illustrating its dependence on various parameters. The method can be easily extended for any force-field ({e.g.,} coarse-grained, all-atom models, and external fields, such as gravity and electric field). Thus, we anticipate that the proposed model will offer opportunities for a wide range of studies in various research areas involving droplet formation and evaporation and will also form the basis for further method developments for the molecular modelling of such phenomena.

The study shows a potential to monitor functional recovery of a muscle by its natural frequency measurement. A novel approach is presented, in which vibrations of the muscle of interest are measured in a contactless manner using laser displacement sensor and the measured vibration signals are analyzed by an advanced technique of experimental modal analysis to extract fundamental natural frequency of the muscle with high accuracy. A case study of functional status recovery monitoring of a rectus femoris muscle, which become anthropic after ACL reconstruction, is presented.

Epsilon-near-zero (ENZ) materials, i.e., materials with vanishing real part of the permittivity, have become an increasingly desirable platform for exploring linear and nonlinear optical phenomena in nanophotonic and on-chip environments. ENZ materials inherently enhance electric fields for properly chosen interaction scenarios, host extreme nonlinear optical effects, and lead to other intriguing phenomena. To date, studies in the optical domain mainly focused on nanoscopically thin films of ENZ materials and their interaction with light and other nanostructured materials. Here, the optical response of individual nanostructures milled into an ENZ material are explored both experimentally and numerically. For the study, 3D structured light beams are employed, allowing for the full utilization of polarization-dependent field enhancements enabled by a tailored illumination and a vanishing permittivity. This study reveals the underlying intricate interaction mechanisms and showcase the polarization-dependent controllability, paving the way towards experiments in the nonlinear optical regime where the presented effects will enable polarization-controlled nonlinear refractive index based ultra-fast switching on the single nanostructure level.

Collinear laser spectroscopy has been performed on He-like C$^{4+}$ ions extracted from an electron beam ion source (EBIS). In order to determine the transition frequency with the highest-possible accuracy, the lineshape of the fluorescence response function was studied for pulsed and continuous ion extraction modes of the EBIS in order to optimize its symmetry and linewidth. We found that the best signal-to-noise ratio is obtained using the continuous beam mode for ion extraction. Applying frequency-comb-referenced collinear and anticollinear laser spectroscopy, we achieved a measurement accuracy of better than 2\,MHz including statistical and systematic uncertainties. The origin and size of systematic uncertainties, as well as further applications for other isotopes and elements are discussed.

LaBr3:Ce crystals have good scintillation properties for X-ray spectroscopy. Initially, they were introduced for radiation imaging in medical physics with either a photomultiplier or SiPM readout, and they found extensive applications in homeland security and gamma-ray astronomy. We used 1 inch round LaBr3:Ce crystals to realize compact detectors with the SiPM array readout. The aim was a good energy resolution and a fast time response to detect low-energy X-rays around 100 keV. A natural application was found inside the FAMU experiment, at RIKEN RAL. Its aim is a precise measurement of the proton Zemach radius with impinging muons, to contribute to the solution to the so-called proton radius puzzle. Signals to be detected are characteristic X-rays around 130 KeV. A limit for this type of detector, as compared to the ones with a photomultiplier readout, is its poorer timing characteristics due to the large capacity of the SiPM arrays used. In particular, long signal falltimes are a problem in experiments such as FAMU, where a prompt background component must be separated from a delayed one (after 600 ns) in the signal X-rays to be detected. Dedicated studies were pursued to improve the timing characteristics of the used detectors, starting from hybrid ganging of SiPM cells; then developing a suitable zero pole circuit with a parallel ganging, where an increased overvoltage for the SiPM array was used to compensate for the signal decrease; and finally designing ad hoc electronics to split the 1 inch detector SiPM array into four quadrants, thus reducing the involved capacitances. The aim was to improve the detectors timing characteristics, especially falltime, while keeping a good FWHM energy resolution for low-energy X-ray detection.

Surface acoustic waves (SAWs) have emerged as innovative and energy-efficient means to manipulate domain walls (DWs) and skyrmions in thin films with perpendicular magnetic anisotropy (PMA) owing to the magnetoelastic coupling effect. This thesis focuses on the complex interplay between SAWs and magnetic thin films. The effects of the standing SAWs on the magnetisation dynamics in a Ta/Pt/Co/Ir/Ta thin film with PMA were first investigated. SAWs with frequency of 93.35 MHz significantly reduced the coercivity of the thin film by 21% and enhanced the magnetisation reversal speed by 11-fold. Standing SAWs introduce a dynamic energy landscape with a unique spatial distribution, forming striped domain patterns in the thin film. The use of rf signals for generating SAWs inevitably causes a heating effect in the device. This heating effect was examined in situ in a SAW device featuring a Ta/Pt/Co/Ta thin film with PMA using an on-chip platinum thermometer. The temperature increased by 10 K in the presence of SAWs at centre frequency of 48 MHz. The DW velocity was significantly enhanced in the presence of the standing SAWs by a factor of 4 compared to that with temperature change alone owing to the magnetoelastic coupling effect. To understand the SAW-enhanced DW motion, comprehensive micromagnetic simulations were performed on thin films in the presence of travelling SAWs. The findings highlighted that SAW-induced vertical Bloch lines within DWs can simultaneously boost DW depinning and dissipate energy at the DW via spin rotation. The SAW-induced strain gradient can be exploited to control skyrmion motion in a current-free manner. Micromagnetic simulations revealed that the use of orthogonal SAWs, combining horizontal travelling and vertical standing waves, offered a promising approach to direct skyrmion motion along desired trajectories, avoiding undesirable Hall-like motion.

The synthesis of multiple narrow optical spectral lines, precisely and independently tuned across the near- to mid-infrared (IR) region, is a pivotal research area that enables selective and real-time detection of trace gas species within complex gas mixtures. However, existing methods for developing such light sources suffer from limited flexibility and very low pulse energy, particularly in the mid-IR domain. Here, we introduce a new concept based on the gas-filled anti-resonant hollow-core fiber (ARHCF) technology that enables the synthesis of multiple independently tunable spectral lines with high pulse energy of >1 {\mu}J and a few nanoseconds pulse width in the near- and mid-IR region. The number and wavelengths of the generated spectral lines can be dynamically reconfigured. A proof-of-concept laser beam synthesized of two narrow spectral lines at 3.99 {\mu}m and 4.25 {\mu}m wavelengths is demonstrated and combined with photoacoustic (PA) modality for real-time SO2 and CO2 detection. The proposed concept also constitutes a promising way for IR multispectral microscopic imaging.

We present a computational framework for modeling large-scale particle-laden flows in complex domains with the goal of enabling simulations in medical-image derived patient specific geometries. The framework is based on a volume-filtered Eulerian-Lagrangian method that uses a finite element method (FEM) to solve for the fluid phase coupled with a discrete element method (DEM) for the particle phase, with varying levels of coupling between the phases. The fluid phase is solved on a three-dimensional unstructured grid using a stabilized FEM. The particle phase is modeled as rigid spheres and their motion is calculated according to Newton's second law for translation and rotation. We propose an efficient and conservative particle-fluid coupling scheme compatible with the FEM basis that enables convergence under grid refinement of the two-way coupling terms. Efficient algorithms for neighbor detection for particle-particle collision and particle-wall collisions are adopted. The method is applied to a few different test cases and the results are analyzed qualitatively. The results demonstrate the capabilities of the implementation and the potential of the method for simulating large-scale particle-laden flows in complex geometries.

Wide-field imaging is widely adopted due to its fast acquisition, cost-effectiveness and ease of use. Its extension to direct volumetric applications, however, is burdened by the trade-off between resolution and depth of field (DOF), dictated by the numerical aperture of the system. We demonstrate that such trade-off is not intrinsic to wide-field imaging, but stems from the spatial incoherence of light: images obtained through spatially coherent illumination are shown to have resolution and DOF independent of the numerical aperture. This fundamental discovery enabled us to demonstrate an optimal combination of coherent resolution-DOF enhancement and incoherent tomographic sectioning for scanning-free, wide-field 3D microscopy on a multicolor histological section.

The Standard Model (SM) of particle physics is both incredibly successful and glaringly incomplete. Among the questions left open is the striking imbalance of matter and antimatter in the observable universe which inspires experiments to compare the fundamental properties of matter/antimatter conjugates with high precision. Our experiments deal with direct investigations of the fundamental properties of protons and antiprotons, performing spectroscopy in advanced cryogenic Penning-trap systems. For instance, we compared the proton/antiproton magnetic moments with 1.5 ppb fractional precision, which improved upon previous best measurements by a factor of >3000. Here we report on a new comparison of the proton/antiproton charge-to-mass ratios with a fractional uncertainty of 16ppt. Our result is based on the combination of four independent long term studies, recorded in a total time span of 1.5 years. We use different measurement methods and experimental setups incorporating different systematic effects. The final result, $-(q/m)_{\mathrm{p}}/(q/m)_{\bar{\mathrm{p}}}$ = $1.000\,000\,000\,003 (16)$, is consistent with the fundamental charge-parity-time (CPT) reversal invariance, and improves the precision of our previous best measurement by a factor of 4.3. The measurement tests the SM at an energy scale of $1.96\cdot10^{-27}\,$GeV (C$.$L$.$ 0.68), and improves 10 coefficients of the Standard Model Extension (SME). Our cyclotron-clock-study also constrains hypothetical interactions mediating violations of the clock weak equivalence principle (WEP$_\text{cc}$) for antimatter to a level of $|\alpha_{g}-1| < 1.8 \cdot 10^{-7}$, and enables the first differential test of the WEP$_\text{cc}$ using antiprotons \cite{hughes1991constraints}. From this interpretation we constrain the differential WEP$_\text{cc}$-violating coefficient to $|\alpha_{g,D}-1|<0.030$.

Integrated photonics has emerged as one of the most promising platforms for quantum applications. The performances of quantum photonic integrated circuits (QPIC) necessitate a demanding optimization to achieve enhanced properties and tailored characteristics with more stringent requirements with respect to their classical counterparts. In this study, we report on the simulation, fabrication, and characterization of a series of fundamental components for photons manipulation in QPIC based on silicon nitride. These include crossing waveguides, multimode-interferometer-based integrated beam splitters (MMIs), asymmetric integrated Mach-Zehnder interferometers (MZIs) based on MMIs, and micro-ring resonators. Our investigation revolves primarily around the Visible to Near-Infrared spectral region, as these devices are meticulously designed and tailored for optimal operation within this wavelength range. By advancing the development of these elementary building blocks, we aim to pave the way for significant improvements in QPIC in a spectral region only little explored so far.

The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack of labeled samples, as well as privacy concerns regarding data sharing. To address these issues, we previously developed (Bergen et al. 2022) a synthetic PET dataset for Head and Neck (H and N) cancer using the temporal generative adversarial network (TGAN) architecture and evaluated its performance segmenting lesions and identifying radiomics features in synthesized images. In this work, a two-alternative forced-choice (2AFC) observer study was performed to quantitatively evaluate the ability of human observers to distinguish between real and synthesized oncological PET images. In the study eight trained readers, including two board-certified nuclear medicine physicians, read 170 real/synthetic image pairs presented as 2D-transaxial using a dedicated web app. For each image pair, the observer was asked to identify the real image and input their confidence level with a 5-point Likert scale. P-values were computed using the binomial test and Wilcoxon signed-rank test. A heat map was used to compare the response accuracy distribution for the signed-rank test. Response accuracy for all observers ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers did not identify the real image with statistical significance, indicating that the synthetic dataset was reasonably representative of oncological PET images. Overall, this study adds validity to the realism of our simulated H&N cancer dataset, which may be implemented in the future to train AI algorithms while favoring patient confidentiality and privacy protection.

The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid dynamics problems due to its algorithmic potential for computational scalability. We introduce XLB framework, a Python-based differentiable LBM library which harnesses the capabilities of the JAX framework. The architecture of XLB is predicated upon ensuring accessibility, extensibility, and computational performance, enabling scaling effectively across CPU, multi-GPU, and distributed multi-GPU systems. The framework can be readily augmented with novel boundary conditions, collision models, or simulation capabilities. XLB offers the unique advantage of integration with JAX's extensive machine learning echosystem, and the ability to utilize automatic differentiation for tackling physics-based machine learning, optimization, and inverse problems. XLB has been successfully scaled to handle simulations with billions of cells, achieving giga-scale lattice updates per second. XLB is released under the permissive Apache-2.0 license and is available on GitHub at https://github.com/Autodesk/XLB.

Scaling down the GaN channel in a double heterostructure AlGaN/GaN/AlGaN High Electron Mobility Transistor (HEMT) to the thicknesses on the order of or even smaller than the Bohr radius confines electrons in the quantum well even at low sheet carrier densities. In contrast to the conventional designs, this Quantum Channel (QC) confinement is controlled by epilayer design and the polarization field and not by the electron sheet density. As a result, the breakdown field at low sheet carrier densities increases by approximately 36% or even more because the quantization leads to an effective increase in the energy gap. In addition, better confinement increases the electron mobility at low sheet carrier densities by approximately 50%. Another advantage is the possibility of increasing the aluminum molar fraction in the barrier layer because a very thin layer prevents material relaxation and the development of dislocation arrays. This makes the QC especially suitable for high-voltage, high-frequency, high-temperature, and radiation-hard applications.

In this paper we theoretically investigate application of a bistable Fabry-P\'{e}rot semiconductor laser under optical-injection as all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.

In exploring the light-induced dynamics within the linear response regime, this study investigates the induced orbital angular momentum on a wide variety of electronic structures. We derive a general expression for the torque induced by light on different electronic systems based on their characteristic dielectric tensor. We demonstrate that this phenomenon diverges from the inverse Faraday effect as it produces an orbital magnetization persistent post-illumination. Indeed, our results reveal that, while isotropic non-dissipative materials do not absorb orbital angular momentum from circularly polarized light, any symmetry-breaking arrangement of matter, be it spatial or temporal, introduces novel channels for the absorption of orbital angular momentum, or magnetization. Most notably, in dissipative materials, circularly polarized light imparts a torque corresponding to a change in orbital angular momentum of $\hbar$ per absorbed photon. The potential of these mechanisms to drive helicity-dependent magnetic phenomena paves the way for a deeper understanding of light-matter interactions. Notably, the application of pump-probe techniques in tandem with our findings allows experimentalists to quantitatively assess the amount of orbital angular momentum transferred to electrons in matter, thus hopefully enhancing our ability to steer ultrafast light-induced magnetization dynamics.

Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a promising candidate for the next-generation metrology solutions in the semiconductor industry, as it can image wafer samples in reflection geometry at the nanoscale. This technique has surged attention recently, owing to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterisation of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimising over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm heigh patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.

In recent years, there has been a growing demand for room-temperature visible single-photon emission from InGaN nanowire-quantum-dots (NWQDs) due to its potential in developing quantum computing, sensing, and communication technologies. Despite various approaches explored for growing InGaN quantum dots on top of nanowires (NWs), achieving the emission of a single photon at room temperature with sensible efficiency remains a challenge. This challenge is primarily attributed to difficulties in accomplishing the radial confinement limit and the inherent giant built-in potential of the NWQD. In this report, we have employed a novel Plasma Assisted Molecular Beam Epitaxy (PAMBE) growth approach to reduce the diameter of the QD to the excitonic Bohr radius of InGaN, thereby achieving strong lateral confinement. Additionally, we have successfully suppressed the strong built-in potential by reducing the QD diameter. Toward the end of the report, we have demonstrated single-photon emission (${\lambda}$ = 561 nm) at room-temperature from the NWQD and measured the second-order correlation function $g^{2}(0)$ as 0.11, which is notably low compared to other reported findings. Furthermore, the lifetime of carriers in the QD is determined to be 775 ps, inferring a high operational speed of the devices.

Tunnelling is a renowned concept in modern physics that highlights the peculiarity of non-classical dynamics. Despite its ubiquity questions remain. We focus on tunnelling through the barrier created by a strong laser field that illuminates an atomic target, which is essential to the creation of attosecond pulses and ultimately all attosecond processes. Here, we present an optical tunnelling event that, unexpectedly, happens at a time when the instantaneous electric field is zero and there is no barrier. We discover this strong-field ionisation event by introducing the colour-switchover technique $-$ the gradual replacement of a laser field with its second harmonic $-$ within which the zero-field tunnelling appears when the two amplitudes are equal. This event is a topologically stable feature and it appears at all Keldysh parameters. The tunnelling without a barrier highlights the disconnect between the standard intuition built on the picture of a quasi-static barrier, and the nonadiabatic nature of the process. Our findings provide a key ingredient to the understanding of strong-field processes, such as high-harmonic generation and laser-induced electron diffraction, driven by the increasingly accessible class of strongly polychromatic light fields.

We present the direct heteroepitaxial growth of high-quality InGaAs quantum dots on silicon, enabling scalable, cost-effective quantum photonics devices compatible with CMOS technology. GaAs heterostructures are grown on silicon via a GaP buffer and defect-reducing layers. These epitaxial quantum dots exhibit optical properties akin to those on traditional GaAs substrates, promising vast potential for the heteroepitaxy approach. They demonstrate strong multi-photon suppression with $g^{(2)}(\tau)=(3.7\pm 0.2) \times 10^{-2}$ and high photon indistinguishability $V=(66\pm 19)$% under non-resonance excitation. We achieve up to ($18\pm 1$)% photon extraction efficiency with a backside distributed Bragg mirror, marking a crucial step toward silicon-based quantum nanophotonics.

Despite the superiority in quantum properties, self-assembled semiconductor quantum dots face challenges in terms of scalable device integration because of their random growth positions, originating from the Stranski-Krastanov growth mode. Even with existing site-controlled growth techniques, for example, nanohole or buried stressor concepts, a further lithography and etching step with high spatial alignment requirements isnecessary to accurately integrate QDs into the nanophotonic devices. Here, we report on the fabrication and characterization of strain-induced site-controlled microcavities where site-controlled quantum dots are positioned at the antinode of the optical mode field in a self-aligned manner without the need of any further nano-processing. We show that the Q-factor, mode volume, height, and the ellipticity of site-controlled microcavities can be tailored by the size of an integrated AlAs/Al2O3 buried stressor, with an opening ranging from 1 to 4 $\mu$m. Lasing signatures, including super-linear input-output response, linewidth narrowing near threshold, and gain competition above threshold, are observed for a 3.6-$\mu$m self-aligned cavity with a Q-factor of 18000. Furthermore, by waiving the rather complex lateral nano-structuring usually performed during the fabrication process of micropillar lasers and vertical-cavity surface emitting lasers, quasi-planar site-controlled cavities exhibit no detrimental effects of excitation power induced heating and thermal rollover. Our straightforward deterministic nanofabrication concept of high-quality quantum dot microcavities integrates seamlessly with the industrial-matured manufacturing process and the buried-stressor techniques, paving the way for exceptional scalability and straightforward manufacturing of high-\b{eta} microlasers and bright quantum light sources.

The KM3NeT Collaboration is building an underwater neutrino observatory at the bottom of the Mediterranean Sea consisting of two neutrino telescopes, both composed of a three-dimensional array of light detectors, known as digital optical modules. Each digital optical module contains a set of 31 three inch photomultiplier tubes distributed over the surface of a 0.44 m diameter pressure-resistant glass sphere. The module includes also calibration instruments and electronics for power, readout and data acquisition. The power board was developed to supply power to all the elements of the digital optical module. The design of the power board began in 2013, and several prototypes were produced and tested. After an exhaustive validation process in various laboratories within the KM3NeT Collaboration, a mass production batch began, resulting in the construction of over 1200 power boards so far. These boards were integrated in the digital optical modules that have already been produced and deployed, 828 until October 2023. In 2017, an upgrade of the power board, to increase reliability and efficiency, was initiated. After the validation of a pre-production series, a production batch of 800 upgraded boards is currently underway. This paper describes the design, architecture, upgrade, validation, and production of the power board, including the reliability studies and tests conducted to ensure the safe operation at the bottom of the Mediterranean Sea throughout the observatory's lifespan

Triadic interactions are higher-order interactions that occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axon-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully-fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time-dependent as in neuroscience.

Non-Hermitian systems exhibit diverse graph patterns of energy spectra under open boundary conditions. Here we present an algebraic framework to comprehensively characterize the spectral geometry and graph topology of non-Bloch bands. Using a locally defined potential function, we unravel the spectral-collapse mechanism from Bloch to non-Bloch bands, delicately placing the spectral graph at the troughs of the potential landscape. The potential formalism deduces non-Bloch band theory and generates the density of states via Poisson equation. We further investigate the Euler-graph topology by classifying spectral vertices based on their multiplicities and projections onto the generalized Brillouin zone. Through concrete models, we identify three elementary graph-topology transitions (UVY, PT-like, and self-crossing), accompanied by the emergence of singularities in the generalized Brillouin zone. Lastly, we unveil how to generally account for isolated edge states outside the spectral graph. Our work lays the cornerstone for exploring the versatile spectral geometry and graph topology of non-Hermitian non-Bloch bands.

Craters formed by the impact of agglomerated materials are commonly observed in nature, such as asteroids colliding with planets and moons. In this paper, we investigate how the projectile spin and cohesion lead to different crater shapes. For that, we carried out DEM (discrete element method) computations of spinning granular projectiles impacting onto cohesionless grains, for different bonding stresses, initial spins and initial heights. We found that, as the bonding stresses decrease and the initial spin increases, the projectile's grains spread farther from the collision point, and, in consequence, the crater shape becomes flatter, with peaks around the rim and in the center of craters. Our results shed light on the dispersion of the projectile's material and the different shapes of craters found on Earth and other planetary environments.

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use US county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.

Mutualistic interactions, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -- the cycles of species' activity within a season -- to fully understand the effects of temporal variability on network architecture. In this paper, by using two empirical datasets together with a set of synthetic models, we propose a framework to characterize phenology on ecological networks and assess the effect of temporal variability. Analyses reveal that non-trivial information is missed when portraying the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species' activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

Relativistic dissipative fluid dynamics finds widespread applications in high-energy nuclear physics and astrophysics. However, formulating a causal and stable theory of relativistic dissipative fluid dynamics is far from trivial; efforts to accomplish this reach back more than 50 years. In this review, we give an overview of the field and attempt a comparative assessment of (at least most of) the theories for relativistic dissipative fluid dynamics proposed until today and used in applications.

The rheology of suspensions of non-Brownian, soft, and frictionless spheres is studied across jamming but also across the viscous and inertial regimes using a custom pressure- and volume-imposed rheometer. Using pressure-imposed rheometry, we show that the usual granular rheology found for suspensions of hard spheres can be generalized to a soft granular rheology by renormalizing the critical volume fraction and friction coefficient to pressure-dependent values and using the addition of stress scales. Using volume-imposed rheometry, we find same critical rheological behavior around jamming across flow regimes with same power laws of the distance to jamming as those found for soft colloids when using again stress additivity. These results support the conclusion that suspensions of soft particles across flow regimes can be described by a common framework around the jamming transition.

This paper introduces a probabilistic framework to quantify community vulnerability towards power losses due to extreme weather events. To analyze the impact of weather events on the power grid, the wind fields of historical hurricanes from 2000 to 2018 on the Texas coast are modeled using their available parameters, and probabilistic storm surge scenarios are constructed utilizing the hurricane characteristics. The vulnerability of hurricanes and storm surges is evaluated on a 2000 bus synthetic power grid model on the geographical footprint of Texas. The load losses, obtained via branch and substation outages, are then geographically represented at the county level and integrated with the publicly available Social Vulnerability Index to evaluate the Integrated Community Vulnerability Index (ICVI), which reflects the impacts of these extreme weather events on the socioeconomic and community power systems. The analysis concludes that the compounded impact of power outages due to extreme weather events can amplify the vulnerability of affected communities. Such analysis can help the system planners and operators make an informed decision.

In this study, we address the selection rules with respect to the polarization of the optical excitation of two colour centres in 4H-SiC and 6H-SiC with potential for applications in quantum technology, the divacancy and the nitrogen-vacancy pair. We show that the photoluminescence (PL) of the axial configurations of higher symmetry (C3v) than the basal ones (C1h) can be cancelled using any excitation (resonant or non-resonant) with polarization parallel to the crystal axis (EL||c). The polarization selection rules are determined using group-theoretical analysis and simple physical arguments showing that phonon-assisted absorption with EL||c is prohibited despite being formally allowed by group theory. A comparison with the selection rules for the silicon vacancy, another defect with C3v symmetry, is also carried out. Using the selection rules, we demonstrate selective excitation of only one basal divacancy configuration in 4H-SiC, the P3 line and discuss the higher contrast and increased Debye-Waller factor in the selectively excited spectrum.

High-pressure growth is a unique method to improve the sample quality and size. Here, we have used the high gas pressure and high-temperature synthesis (HP-HTS) method to grow CaKFe4As4 (1144) bulks and investigated their superconducting properties using structural, microstructural, transport, and magnetic studies. The microstructural analysis demonstrates that 1144 samples prepared by HP-HTS have improved the sample density and grain connectivity. The transition temperature (Tconset) of 1144 bulks prepared by HP-HTS is increased up to 35.2 K with a transition width ({\Delta}T) of 1 K, which is remarkably comparable to the reported 1144 single crystal. Additionally, the critical current density (Jc) is enhanced by almost one order of magnitude compared with the parent compound prepared by the conventional synthesis process at ambient pressure (CSP), which could be attributed to the improved sample density and effective pinning centers. Our study demonstrates that the sample quality and superconducting properties of various iron-based superconductors can be enhanced by applying the HP-HTS approach, and further research is demanded in this direction.

Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab initio} methods is considered challenging, given the charge transfers involved. Herein, this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/ SiO$_2$/ O system, based on the moment tensor potential (MTP) framework. This MTP is trained using a comprehensive database generated using density functional theory simulations, encompassing a wide range of crystal structures, point defects, extended defects, and disordered structure. Extensive testing of the MTP is performed, indicating it can describe static and dynamic features of very diverse Si, O, and SiO$_2$ atomic structures with a degree of fidelity approaching that of DFT

Microwave magnetometry is essential for the advancement of microwave technologies. We demonstrate a broadband microwave sensing protocol using the AC Zeeman effect with ensemble nitrogen-vacancy (NV) centers in diamond. A widefield microscope can visualize the frequency characteristics of the microwave resonator and the spatial distribution of off-resonant microwave amplitude. Furthermore, by combining this method with dynamical decoupling, we achieve the microwave amplitude sensitivity of $5.2 \, \mathrm{\mu T} / \sqrt{\mathrm{Hz}}$, which is 7.7 times better than $40.2 \, \mathrm{\mu T} / \sqrt{\mathrm{Hz}}$ obtained using the protocol in previous research over a sensing volume of $2.77 \, \mathrm{\mu m} \times 2.77 \, \mathrm{\mu m} \times 30 \, \mathrm{nm}$. Our achievement is a concrete step in adapting ensemble NV centers for wideband and widefield microwave imaging.

The main goal of this paper is to model the epidemic and flattening the infection curve of the social networks. Flattening the infection curve implies slowing down the spread of the disease and reducing the infection rate via social-distancing, isolation (quarantine) and vaccination. The nan-pharmaceutical methods are a much simpler and efficient way to control the spread of epidemic and infection rate. By specifying a target group with high centrality for isolation and quarantine one can reach a much flatter infection curve (related to Corona for example) without adding extra costs to health services. The aim of this research is, first, modeling the epidemic and, then, giving strategies and structural algorithms for targeted vaccination or targeted non-pharmaceutical methods for reducing the peak of the viral disease and flattening the infection curve. These methods are more efficient for nan-pharmaceutical interventions as finding the target quarantine group flattens the infection curve much easier. For this purpose, a few number of particular nodes with high centrality are isolated and the infection curve is analyzed. Our research shows meaningful results for flattening the infection curve only by isolating a few number of targeted nodes in the social network. The proposed methods are independent of the type of the disease and are effective for any viral disease, e.g., Covid-19.

This paper presents a modeling approach to infer scheduling and routing patterns from digital freight transport activity data for different freight markets. We provide a complete modeling framework including a new discrete-continuous decision tree approach for extracting rules from the freight transport data. We apply these models to collected tour data for the Netherlands to understand departure time patterns and tour strategies, also allowing us to evaluate the effectiveness of the proposed algorithm. We find that spatial and temporal characteristics are important to capture the types of tours and time-of-day patterns of freight activities. Also, the empirical evidence indicates that carriers in most of the transport markets are sensitive to the level of congestion. Many of them adjust the type of tour, departure time, and the number of stops per tour when facing a congested zone. The results can be used by practitioners to get more grip on transport markets and develop freight and traffic management measures.

We found that fluctuations in the number of emitters lead to a super-thermal photon statistics of small LEDs in a linear regime, with a strong emitter-field coupling and a bad cavity favorable for collective effects. A simple analytical expression for the second-order correlation function g_2 is found. g_2 increase up to g_2=6 in the two-level LED model is predicted. The super-thermal photon statistics is related to the population fluctuation increase of the spontaneous emission to the cavity mode.

Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging.

Purpose: To investigate the use of a Vision Transformer (ViT) to reconstruct/denoise GABA-edited magnetic resonance spectroscopy (MRS) from a quarter of the typically acquired number of transients using spectrograms. Theory and Methods: A quarter of the typically acquired number of transients collected in GABA-edited MRS scans are pre-processed and converted to a spectrogram image representation using the Short-Time Fourier Transform (STFT). The image representation of the data allows the adaptation of a pre-trained ViT for reconstructing GABA-edited MRS spectra (Spectro-ViT). The Spectro-ViT is fine-tuned and then tested using \textit{in vivo} GABA-edited MRS data. The Spectro-ViT performance is compared against other models in the literature using spectral quality metrics and estimated metabolite concentration values. Results: The Spectro-ViT model significantly outperformed all other models in four out of five quantitative metrics (mean squared error, shape score, GABA+/water fit error, and full width at half maximum). The metabolite concentrations estimated (GABA+/water, GABA+/Cr, and Glx/water) were consistent with the metabolite concentrations estimated using typical GABA-edited MRS scans reconstructed with the full amount of typically collected transients. Conclusion: The proposed Spectro-ViT model achieved state-of-the-art results in reconstructing GABA-edited MRS, and the results indicate these scans could be up to four times faster.

A slender object undergoing an axial compression will buckle to alleviate the stress. Typically the morphology of the deformed object depends on the bending stiffness for solids, or the viscoelastic properties for liquid threads. We study a chain of uniform sticky air bubbles in an aqueous bath that rise due to buoyancy. A buckling instability of the bubble chain, with a characteristic wavelength, is observed in the absence of a bending stiffness. If a chain of bubbles is produced faster than it is able to rise, the dominance of viscous drag over buoyancy results in a compressive stress that is alleviated by buckling the bubble chain. Using low Reynolds number hydrodynamics and geometric arguments, we predict the critical buckling speed, the terminal speed of a buckled chain and the amplitude of the buckles.

By positing a universe where all events are determined by initial conditions, superdeterminism as conceded by Bell frames correlations observed in quantum measurements as the consequence of an inherently predetermined cosmic order that shapes even our experimental choices. I use an axiomatic formulation of superdeterminism to demonstrate that Bell overstated the scope of determinism required. Assuming only the existence of a universe containing observers, I show that determinism in just the observer scope is sufficient. I then discuss how this sufficiency increases the theory's plausibility and suggest a path to its integration with results from other disciplines.

The acquisition of accurate rainfall distribution in space is an important task in hydrological analysis and natural disaster pre-warning. However, it is impossible to install rain gauges on every corner. Spatial interpolation is a common way to infer rainfall distribution based on available raingauge data. However, the existing works rely on some unrealistic pre-settings to capture spatial correlations, which limits their performance in real scenarios. To tackle this issue, we propose the SSIN, which is a novel data-driven self-supervised learning framework for rainfall spatial interpolation by mining latent spatial patterns from historical observation data. Inspired by the Cloze task and BERT, we fully consider the characteristics of spatial interpolation and design the SpaFormer model based on the Transformer architecture as the core of SSIN. Our main idea is: by constructing rich self-supervision signals via random masking, SpaFormer can learn informative embeddings for raw data and then adaptively model spatial correlations based on rainfall spatial context. Extensive experiments on two real-world raingauge datasets show that our method outperforms the state-of-the-art solutions. In addition, we take traffic spatial interpolation as another use case to further explore the performance of our method, and SpaFormer achieves the best performance on one large real-world traffic dataset, which further confirms the effectiveness and generality of our method.

Waves in a variety of fields in physics, such as mechanics, optics, spintronics, and nonlinear systems, obey generalized eigenvalue equations. To study non-Hermitian physics of those systems, in this work, we construct a non-Bloch band theory of generalized eigenvalue problems. Specifically, we show that eigenvalues of a transfer matrix lead to a certain condition imposed on the generalized Brillouin zone, which allows us to develop a theory to calculate the continuum bands. As a concrete example, we examine the non-Hermitian skin effect of photonic crystals composed of chiral metamaterials by invoking our theoretical framework. When the medium has circularly polarized eigenmodes, we find that each eigenmode localizes at either of edges depending on whether it is left- or right-circularly polarized. In contrast, when the medium only has linearly polarized eigenmodes, every eigenmode localizes to the edge of the same side independent of its polarization. We demonstrate that the localization lengths of those eigenmodes can be determined from the chiral parameters and eigenfrequencies of the photonic crystal.

We demonstrate how motional degrees of freedom in optical tweezers can be used as quantum information carriers. To this end, we first implement a species-agnostic cooling mechanism via conversion of motional excitations into erasures - errors with a known location - reminiscent of Maxwell's demon thought experiment. We find that this cooling mechanism fundamentally outperforms idealized traditional sideband cooling, which we experimentally demonstrate in specific scenarios. By coherently manipulating the motional state, we perform mid-circuit readout and mid-circuit erasure detection of an optical qubit via local shelving into motional superposition states. We finally entangle the motion of two atoms in separate tweezers, and utilize this to generate hyper-entanglement by preparing a simultaneous Bell state of motional and optical qubits. This work shows how controlling motion enriches the toolbox of quantum information processing with neutral atoms, and opens unique prospects for metrology enhanced by mid-circuit readout and a large class of quantum operations enabled via hyper-entanglement.

A key challenge in space weather forecasting is accurately predicting the magnetic field topology of interplanetary coronal mass ejections (ICMEs), specifically the north-south magnetic field component (Bz) for Earth-directed CMEs. Heliospheric MHD models typically use spheromaks to represent the magnetic structure of CMEs. However, when inserted into the ambient interplanetary magnetic field, spheromaks can experience a phenomenon reminiscent of the condition known as the "spheromak tilting instability", causing its magnetic axis to rotate. From the perspective of space weather forecasting, it is crucial to understand the effect of this rotation on predicting Bz at 1 au while implementing the spheromak model for realistic event studies. In this work, we study this by modelling a CME event on 2013 April 11 using the "EUropean Heliospheric FORecasting Information Asset" (EUHFORIA). Our results show that a significant spheromak rotation up to 90 degrees has occurred by the time it reaches 1 au, while the majority of this rotation occurs below 0.3 au. This total rotation resulted in poor predicted magnetic field topology of the ICME at 1 au. To address this issue, we further investigated the influence of spheromak density on mitigating rotation. The results show that the spheromak rotation is less for higher densities. Importantly, we observe a substantial reduction in the uncertainties associated with predicting Bz when there is minimal spheromak rotation. Therefore, we conclude that spheromak rotation adversely affects Bz prediction in the analyzed event, emphasizing the need for caution when employing spheromaks in global MHD models for space weather forecasting.

We identify more than ten steady sub-Alfv\'enic solar wind intervals from the measurements of the Parker Solar Probe (PSP) from encounter 8 to encounter 14. An analysis of these sub-Alfv\'enic intervals reveals similar properties and similar origins. In situ measurements show that these intervals feature a decreased radial Alfv\'en Mach number resulting from a reduced density and a relatively low velocity, and that switchbacks are suppressed in these intervals. Magnetic source tracing indicates that these sub-Alfv\'enic streams generally originate from the boundaries inside coronal holes, or narrow/small regions of open magnetic fields. Such properties and origins suggest that these streams are low Mach-number boundary layers (LMBLs), which is a special component of the pristine solar wind proposed by Liu et al. (2023). We find that the LMBL wind, the fast wind from deep inside coronal holes, and the slow streamer wind constitute three typical components of the young solar wind near the Sun. In these sub-Alfv\'enic intervals, the Alfv\'en radius varies between 15 and 25 solar radii, in contrast with a typical 12 radii for the Alfv\'en radius of the super-Alfv\'enic wind. These results give a self-consistent picture interpreting the PSP measurements in the vicinity of the Sun.

If two phases of a certain material exist at the same time, e.g., a gas and a liquid, they have the same temperature. This fundamental law of equilibrium physics is known to apply even to many non-equilibrium systems. However, recently, there has been much attention in the finding that inertial self-propelled particles like Janus colloids in a plasma, microflyers, or beetles at interfaces could self-organize into a hot gas-like phase that coexists with a colder liquid-like phase. Here, we show that a kinetic temperature difference across coexisting phases can occur even in equilibrium systems when adding generic (overdamped) self-propelled particles. In this case, surprisingly, we find that the dense phase (liquid) cannot only be colder but also hotter than the dilute phase (gas). This generic effect hinges on correlated events where active particles collectively push and heat up passive ones, which can overcompensate their collision-induced energy loss locally. Our results answer the fundamental question if a non-equilibrium gas can be colder than a coexisting liquid and create a route to equip matter with self-organized domains of different kinetic temperatures.

We propose tabular two-dimensional correlation analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to datasets of carbon nanotube (CNTs) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements like voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from 8 characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli) and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena like the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

Body tides reveal information about planetary interiors and affect their evolution. Most models to compute body tides rely on the assumption of a spherically-symmetric interior. However, several processes can lead to lateral variations of interior properties. We present a new spectral method to compute the tidal response of laterally-heterogeneous bodies. Compared to previous spectral methods, our approach is not limited to small-amplitude lateral variations; compared to finite element codes, the approach is more computationally-efficient. While the tidal response of a spherically-symmetric body has the same wave-length as the tidal force; lateral heterogeneities produce an additional tidal response with an spectra that depends on the spatial pattern of such variations. For Mercury, the Moon and Io the amplitude of this signal is as high as $1\%-10\%$ the main tidal response for long-wavelength shear modulus variations higher than $\sim 10\%$ the mean shear modulus. For Europa, Ganymede and Enceladus, shell-thickness variations of $50\%$ the mean shell thickness can cause an additional signal of $\sim 1\%$ and $\sim 10\%$ for the Jovian moons and Encelaudus, respectively. Future missions, such as $ \textit{BepiColombo}$ and $\textit{JUICE}$, might measure these signals. Lateral variations of viscosity affect the distribution of tidal heating. This can drive the thermal evolution of tidally-active bodies and affect the distribution of active regions.

The main goal of this paper is to explore the leapfrogging phenomenon in the inviscid planar flows. We show for 2d Euler equations that under suitable constraints, four concentrated vortex patches leapfrog for all time. When observed from a translating frame of reference, the evolution of these vortex patches can be described as a non-rigid time periodic motion. Our proof hinges upon two key components. First, we desingularize the symmetric four point vortex configuration, which leapfrogs in accordance with Love's result \cite{Love1893}, by concentrated vortex patches. Second, we borrow some tools from KAM theory to effectively tackle the small divisor problem and deal with the degeneracy in the time direction. Our approach is robust and flexible and solves a long-standing problem that has remained unresolved for many decades.

The study of thermodynamic properties of microscopic systems, such as a colloid in a fluid, has been of great interest to researchers since the discovery of the fluctuation theorem and associated laws of stochastic thermodynamics. However, most of these studies confine themselves to systems where effective fluctuations acting on the colloid are in the form of delta-correlated Gaussian white noise (GWN). In this study, instead, we look into the work distribution function when a colloid trapped in a harmonic potential moves from one position to another in a fluid medium with an elongational flow field where the effective fluctuations are given by the Ornstein-Uhlenbeck (OU) noise, a type of coloured noise. We use path integrals to calculate this distribution function and compare and contrast its properties to the case with GWN. We find that the work distribution function turns out to be non-Gaussian as a result of the elongational flow field, but continues to obey the fluctuation theorem in both types of noise. Further, we also look into the effects of the various system parameters on the behaviour of work fluctuations and find that although the distribution tends to broaden with increasing noise intensity, increased correlation in fluctuations acts to oppose this effect. Additionally, the system is found to consume heat from the surroundings at early times and dissipate it into the media at later times. This study, therefore, is a step towards gaining a better understanding of the thermodynamic properties of colloidal systems under non-linear complex flows that also display correlated fluctuations.

A unitary Fermi gas in an isotropic harmonic trap is predicted to show scale and conformal symmetry that have important consequences in its thermodynamic and dynamical properties. By experimentally realizing an isotropic harmonic trap, we study the expansion of a unitary Fermi gas and demonstrate its universal expansion dynamics along different directions and at different temperatures. We show that as a consequence of SO(2,1) symmetry, the measured release energy is equal to that of the trapping energy. In addition, away from resonance when scale invariance is broken, we determine the effective exponent $\gamma$ that relates the chemical potential and average density along the BEC-BCS crossover, which qualitatively agrees with the mean field predictions. This work opens the possibility of studying non-equilibrium dynamics in a conformal invariant system in the future.

Magnetic Resonance Imaging represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully sampled k-space data under motion in clinical scenarios such as abdominal, cardiac, and prostate imaging. In the absence of fully sampled acquisitions, which can serve as ground truth data, training deep learning algorithms in a supervised manner to predict the underlying ground truth image becomes an impossible task. To address this limitation, self-supervised methods have emerged as a viable alternative, leveraging available subsampled k-space data to train deep learning networks for MRI reconstruction. Nevertheless, these self-supervised approaches often fall short when compared to supervised methodologies. In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable. Our proposed method operates by simultaneously training a model in a self-supervised learning setting, using subsampled data from the target dataset(s), and in a supervised learning manner, utilizing data from other datasets, referred to as proxy datasets, where fully sampled k-space data is accessible. To demonstrate the efficacy of JSSL, we utilized subsampled prostate parallel MRI measurements as the target dataset, while employing fully sampled brain and knee k-space acquisitions as proxy datasets. Our results showcase a substantial improvement over conventional self-supervised training methods, thereby underscoring the effectiveness of our joint approach. We provide a theoretical motivation for JSSL and establish a practical "rule-of-thumb" for selecting the most appropriate training approach for deep MRI reconstruction.

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) has become a major tool in the analysis of adsorption of nanometric objects, such as proteins, viruses, liposomes and inorganic particles from the solution. While in vacuum extremely accurate mass measurements are possible, in a liquid phase the quantitative analysis is intricate due to the complex interplay of hydrodynamic and adhesion forces, varying with the physicochemical properties of adsorbent and the quartz resonator surfaces. In the present paper we dissect the role of hydrodynamics for the analytically tractable scenario of a stiff contact, whereas the adsorbed particles oscillate with the resonator as a whole without rotation. Under the assumption of the low surface coverage, we theoretically study the excess shear force exerted on the resonator due to presence of a single adsorbed particle. The excess shear force has two contributions: (i) the fluid-mediated force due to flow disturbance created by the particle and (ii) the viscous force exerted on the particle by the fluid and transmitted to the resonator via contact. We found that for small enough particles there is a mutual cancellation of the above dual components of the net excess shear force, reducing the overall effect of the hydrodynamics to that comparable in magnitude to the inertial force. These findings indicate that the accurate account of hydrodynamics in the analysis of QCM-D response is as important as the inertial mass of the adsorbents (determining the frequency shift in the Sauerbrey equation). The resulting dimensionless frequency and dissipation shifts and the corresponding acoustic ratio computed numerically, showing a fair agreement with previously published experimental results at low oscillation frequencies.

Complex dynamical systems are prevalent in various domains, but their analysis and prediction are hindered by their high dimensionality and nonlinearity. Dimensionality reduction techniques can simplify the system dynamics by reducing the number of variables, but most existing methods do not account for networked systems with separable coupling-dynamics, where the interaction between nodes can be decomposed into a function of the node state and a function of the neighbor state. Here, we present a novel dimensionality reduction framework that can effectively capture the global dynamics of these networks by projecting them onto a low-dimensional system. We derive the reduced system's equation and stability conditions, and propose an error metric to quantify the reduction accuracy. We demonstrate our framework on two examples of networked systems with separable coupling-dynamics: a modified susceptible-infected-susceptible model with direct infection and a modified Michaelis-Menten model with activation and inhibition. We conduct numerical experiments on synthetic and empirical networks to validate and evaluate our framework, and find a good agreement between the original and reduced systems. We also investigate the effects of different network structures and parameters on the system dynamics and the reduction error. Our framework offers a general and powerful tool for studying complex dynamical networks with separable coupling-dynamics.

Globalization has had undesirable effects on the labor standards embedded in the products we consume. This paper proposes an ex-ante evaluation of supply chain due diligence regulations, such as the EU Corporate Sustainable Due Diligence Directive (CSDDD). We construct a full-scale network model derived from structural business statistics of 30 million EU firms to quantify the likelihood of links to firms potentially involved in human rights abuses in the European supply chain. The 900 million supply links of these firms are modeled in a way that is consistent with multiregional input-output data, EU import data, and stylized facts of firm-level production networks. We find that this network exhibits a small world effect with three degrees of separation, meaning that most firms are no more than three steps away from each other in the network. Consequently we find that about 8.5% of EU companies are at risk of having child or forced labor in the first tier of their supply chains, about 82.4% are likely to have such offenders at the second tier and more than 99.1% have such offenders at the third tier. We also profile companies by country, sector, and size for the likelihood of having human rights violations or child and forced labor violations at a given tier in their supply chain, revealing considerable heterogeneity across EU companies. Our results show that supply chain due diligence regulations that focus on monitoring individual buyer-supplier links, as currently proposed in the CSDDD, are likely to be ineffective due to a high degree of redundancy and the fact that individual company value chains cannot be properly isolated from the global supply network. Rather, to maximize cost-effectiveness without compromising due diligence coverage, we suggest that regulations should focus on monitoring individual suppliers.

We propose, and theoretically analyze, a practical protocol for the creation of topological monopole configurations, quantum knots, and skyrmions in Bose--Einstein condensates by employing fictitious magnetic fields induced by the interaction of the atomic cloud with coherent light fields. It is observed that a single coherent field is not enough for this purpose, but instead we find incoherent superpositions of several coherent fields that introduce topological point charges. We numerically estimate the experimentally achievable strengths and gradients of the induced fictitious magnetic fields and find them to be adjustable at will to several orders of magnitude greater than those of the physical magnetic fields employed in previous experimental studies. This property together with ultrafast control of the optical fields paves the way for advanced engineering of topological defects in quantum gases.

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are a powerful tool for computing this critical parameter. Nevertheless, the accurate and efficient calculation of solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. In the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman-Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare earth elements immersed in ionic liquids, a scenario where the aforementioned challenges render standard approaches ineffective. The proposed method proves effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

Entangled photon-pair sources are at the core of quantum applications like quantum key distribution, sensing, and imaging. Operation in space-limited and adverse environments such as in satellite-based and mobile communication requires robust entanglement sources with minimal size and weight requirements. Here, we meet this challenge by realizing a cubic micrometer scale entangled photon-pair source in a 3R-stacked transition metal dichalcogenide crystal. Its crystal symmetry enables the generation of polarization-entangled Bell states without additional components and provides tunability by simple control of the pump polarization. Remarkably, generation rate and state tuning are decoupled, leading to equal generation efficiency and no loss of entanglement. Combining transition metal dichalcogenides with monolithic cavities and integrated photonic circuitry or using quasi-phasematching opens the gate towards ultrasmall and scalable quantum devices.

Wolfgang S.M. Werner, Florian Simperl, Felix Bloedorn, Julian Brunner, Johannes Kero, Alessandra Bellissimo and Olga Ridzel Spectroscopy of correlated electron pairs was employed to investigate the energy dissipation process as well as the transport and the emission of low energy electrons on a polymethylmetracylate (PMMA) surface, providing secondary electron (SE) spectra causally related to the energy loss of the primary electron. Two groups of electrons are identified in the cascade of slow electrons, corresponding to different stages in the energy dissipation process. For both groups, the characteristic lengths for attenuation due to collective excitations and momentum relaxation are quantified and are found to be distinctly different: l1=(12.0+/-2) Angstroem and l2=(61.5+/-11) Angstroem. The results strongly contradict the commonly employed model of exponential attenuation with the electron inelastic mean free path (IMFP) as characteristic length, but essentially agree with a theory used for decades in astrophysics and neutron transport, albeit with characteristic lengths expressed in units of Angstroems rather than lightyears.

In this paper, we introduce and study an age-structured epidemiological compartment model and its respective problem, applied but not limited to the COVID-19 pandemic, in order to investigate the role of the age of the individuals in the evolution of epidemiological phenomena. We investigate the well-posedness of the model, as well as the global dynamics of it in the sense of basic reproduction number via constructing Lyapunov functions.

We experimentally study the effects of salt concentration on the flowing dynamics of dense suspensions of micrometer-sized silica particles in microfluidic drums. In pure water, the particles are fully sedimented under their own weight, but do not touch each others due to their negative surface charges, which results in a "frictionless" dense colloidal suspension. When the pile is inclined above a critical angle $\theta_c \sim 5{\deg}$ a fast avalanche occurs, similar to what is expected for classical athermal granular media. When inclined below this angle, the pile slowly creeps until it reaches flatness. Adding ions in solution screens the repulsive forces between particles, and the flowing properties of the suspension are modified. We observe significant changes in the fast avalanche regime: a time delay appears before the onset of the avalanche and increases with the salt concentration, the whole dynamics becomes slower, and the critical angle $\theta_c$ increases from $\sim 5{\deg}$ to $\sim 20{\deg}$. In contrast, the slow creep regime does not seem to be heavily modified. These behaviors can be explained by considering an increase in both the initial packing fraction of the suspension $\Phi_0$, and the effective friction between the particles $\mu_p$. These observations are confirmed by confocal microscopy measurements to estimate the initial packing fraction of the suspensions, and AFM measurements to quantify the particles surface roughness and the repulsion forces, as a function of the ionic strength of the suspensions.

Defining microstructures and managing local crystallinity allow the implementation of several functionalities in thin film technology. The use of ultrashort Bessel beams for bulk crystallinity modification has garnered considerable attention as a versatile technique for semiconductor materials, dielectrics, or metal oxide substrates. The aim of this work is the quantitative evaluation of the crystalline changes induced by ultrafast laser micromachining on manganese oxide thin films using micro-Raman spectroscopy. Pulsed Bessel beams featured by a 1 micrometer-sized central core are used to define structures with high spatial precision. The dispersion relation of Mn3O4 optical phonons is determined by considering the conjunction between X-ray diffraction characterization and the phonon localization model. The asymmetries in Raman spectra indicate phonon localization and enable a quantitative tool to determine the crystallite size at micrometer resolution. The results indicate that laser-writing is effective in modifying the low-crystallinity films locally, increasing crystallite sizes from ~8 nm up to 12 nm, and thus highlighting an interesting approach to evaluate laser-induced structural modifications on metal oxide thin films.

We present a novel conceptual design for a magnet system that provides the magnetic field necessary for the analysis of tracks in a high-pressure gaseous argon TPC while simultaneously serving as a pressure vessel to contain the TPC gas volume. The magnet was developed within a Near Detector proposal for the Deep Underground Neutrino Experiment (DUNE). The high-pressure gaseous argon TPC is a component proposed to be one of the elements of an ensemble of near detectors that are needed for DUNE