Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis.

Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts. Additionally, we provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions. This dataset has the potential automate structure elucidation, streamlining the molecular discovery pipeline from synthesis to structure determination. The dataset and code for the benchmarks can be found at https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset.

We consider a generic higher order mass aggregation term for interactions between particles exhibiting oscillatory clumping and disaggregation behavior in the F ring of Saturn, using a novel predator-prey model that relates the mean mass aggregate (prey) and the square of the relative dispersion velocity (predator) of the interacting particles. The resulting cyclic dynamic behavior is demonstrated through time series plots, phase portraits and their stroboscopic phase maps. Employing an eigenvalue stability analysis of the Jacobian of the system, we find out that there are two distinct regimes depending on the exponent and the amplitude of the higher order interactions of the nonlinear mass term. In particular, the system exhibits a limit cycle oscillatory stable behavior for a range of values of these parameters and a non-cyclic behavior for another range, separated by a curve across which phase transitions would occur between the two regimes. This shows that the observed clumping dynamics in Saturn's F ring, corresponding to a limit cycle stability regime, can be systematically maintained in presence of physical higher order mass aggregation terms in the introduced model.

The Nab experiment will measure the electron-neutrino correlation and Fierz interference term in free neutron beta decay to test the Standard Model and probe Beyond the Standard Model Physics. Using National Instrument's PXIe-5171 Reconfigurable Oscilloscope module, we have developed a data acquisition system that is not only capable of meeting Nab's specifications, but flexible enough to be adapted in situ as the experimental environment dictates. The L1 and L2 trigger logic can be reconfigured to optimize the system for coincidence event detection at runtime through configuration files and LabVIEW controls. This system is capable of identifying L1 triggers at at least $1$ MHz, while reading out a peak signal rate of approximately $2$ GB/s. During commissioning, the system ran at a sustained readout rate of $400$ MB/s of signal data originating from roughly $6$ kHz L2 triggers, well within the peak performance of the system.

There is increased interest in the heterogeneous integration of various compound semiconductors with Si for a variety of electronic and photonic applications. This paper focuses on integrating GaAsSb (with absorption in the C-band at 1550nm) with silicon to fabricate photodiodes, leveraging epitaxial layer transfer (ELT) methods. Two ELT techniques, epitaxial lift-off (ELO) and macro-transfer printing (MTP), are compared for transferring GaAsSb films from InP substrates to Si, forming PIN diodes. Characterization through atomic force microscopy (AFM), and transmission electron microscopy (TEM) exhibits a high-quality, defect-free interface. Current-voltage (IV) measurements and capacitance-voltage (CV) analysis validate the quality and functionality of the heterostructures. Photocurrent measurements at room temperature and 200 K demonstrate the device's photo-response at 1550 nm, highlighting the presence of an active interface.

We propose In-like Pr10+ as a candidate for the development of a high-accuracy optical clock with high sensitivity to a time variation of the fine-structure constant, (\dot alpha}/alpha, as well as favorable experimental systematics. We calculate its low-lying energy levels by combining the configuration interaction and the coupled cluster method, achieving uncertainties as low as 0.1%, and improving previous work. We benchmark these results by comparing our calculations for the (5s^2 5p 2P_1/2) - (5s^2 5p 2P_3/2) transition in Pr10+ with a dedicated measurement and for Pr9+ with a recent experiment, respectively. In addition, we report calculated hyperfine-structure constants for the clock and logic states in Pr10+.

This study explores the mixed convection of CNT-water nanofluid within a square cavity containing heated cylinders under the influence of a magnetic field, focusing on three geometric configurations: a single heated cylinder, two heated cylinders, and two heated cylinders with a flow deflector. The impact of various parameters, including Reynolds number (Re), Richardson number (Ri), Hartmann number (Ha), wavy wall peaks (n), nanoparticle volume fraction ({\phi}), Hartmann angle ({\gamma}), rotational speed ({\omega}), and inclination angle ({\alpha}), on thermal and fluid dynamic behaviors is analyzed. Results reveal that MWCNT nanofluids consistently achieve higher Nusselt numbers than SWCNT nanofluids, indicating superior heat transfer capabilities. Introducing a second cylinder and a flow deflector enhances thermal interactions, while increasing Ha stabilizes the flow, improving thermal performance. Wavy wall peaks further enhance fluid mixing and heat transfer efficiency. Additionally, SWCNT nanofluids exhibit higher Bejan numbers, indicating a greater dominance of thermal entropy generation over fluid friction. These findings provide valuable insights for optimizing thermal management systems in engineering applications, highlighting the importance of selecting appropriate nanofluids, geometric configurations, and magnetic field parameters to achieve optimal thermal performance and fluid stability.

To advance our understanding of the magnetohydrodynamic (MHD) processes in liquid metals, in this paper we propose an approach combining the classical methods in the dynamo theory based on numerical simulations of the partial differential equations governing the evolution of the magnetic field with the statistical methods. In this study, we intend to answer the following ``optimization'' question: Can we find a statistical explanation what makes a flow to favour magnetic field generation in the linear regime (i.e. the kinematic dynamo is considered), where the Lorenz force is neglected? The flow is assumed to be steady and incompressible, and the magnetic field generation is governed by the magnetic induction equation. The behaviour of its solution is determined by the dominant (i.e. with the largest real part) eigenvalue of the magnetic induction operator. Considering an ensemble of 2193 randomly generated flows, we solved the kinematic dynamo problem and performed an attempt to find a correlation between the dominant eigenvalue and the standard quantities used in hydrodynamics -- vorticity and kinetic helicity. We have found that there is no visible relation between the property of the flow to be a kinematic dynamo and these quantities. This enables us to conclude that the problem requires a more elaborated approach to ``recognize'' if the flow is a dynamo or not; we plan to solve it using contemporary data-driven approach based on deep neural networks.

We investigate the perturbation dynamics in a supersonic shear layer using a combination of large-eddy simulations (LES) and linear-operator-based input-output analysis. The flow consists of two streams-a main stream (Mach 1.23) and a bypass stream (Mach 1.0)-separated by a splitter plate of non-negligible thickness. We employ spectral proper orthogonal decomposition to identify the most energetic coherent structures and bispectral mode decomposition to explore the nonlinear energy cascade within the turbulent shear layer flow. Structures at the dominant frequency are also obtained from a resolvent analysis of the mean flow. We observe higher gain at the dominant frequency in resolvent analysis, indicating the dominance of Kelvin-Helmholtz (KH) instability as the primary disturbance energy-amplification mechanism. To focus on realizable actuator placement locations, we further conduct an input-output analysis by restricting a state variable and spatial location of an input and output. Various combinations of inputs and output indicate that the splitter plate trailing surface is the most sensitive location for introducing a perturbation. For all combinations, the KH instability plays a key role in amplification, which reduces significantly as the input location is moved upstream relative to the splitter plate trailing edge. Furthermore, two-dimensional nonlinear simulations with unsteady input at the upper surface of the splitter plate show remarkable similarities between pressure modes obtained through dynamic mode decomposition and those predicted from linear input-output analysis at a given frequency. This study emphasizes the strength of linear analysis and demonstrates that predicted coherent structures remain active in highly nonlinear turbulent flow. The insights gained from the input-output analysis can be further leveraged to formulate practical flow control strategies.

Vacancies in the L1 shell of atoms and molecules can decay non-radiatively via Coster-Kronig decay whereby the vacancy is filled by an electron from the L2,3 shell while a second electron is emitted into the ionization continuum. This process is akin to Auger decay, but in contrast to Auger electrons, Coster-Kronig electrons have rather low kinetic energies of less than 50 eV. In the present work, we extend recently introduced methods for the construction of molecular Auger spectra that are based on complex-scaled equation-of-motion coupled-cluster theory to Coster-Kronig decay. We compute ionization energies as well as total and partial decay widths for the 2s-1 states of argon and hydrogen sulfide and construct the L1L2,3M Coster-Kronig and L1MM Auger spectra of these species. Whereas our final spectra are in good agreement with the available experimental and theoretical data, substantial disagreements are found for various branching ratios suggesting that spin-orbit coupling makes a major impact on Coster-Kronig decay already in the third period of the periodic table.

The Ehrenfest with collapse-to-a-block (TAB) molecular dynamics approach was recently introduced to allow accurate simulation of nonadiabatic dynamics on many electronic states. Previous benchmarking work has demonstrated it to be highly accurate for modeling dynamics in one-dimensional analytical models, but nonadiabatic dynamics often involves conical intersections, which are inherently two-dimensional. In this report, we assess the performance of TAB on two-dimensional models of cascades of conical intersections in dense manifolds of states. Several variants of TAB are considered, including TAB-w, which is based on the assumption of a Gaussian rather than exponential decay of the coherence, and TAB-DMS, which incorporates an efficient collapse procedure based on approximate eigenstates. Upon comparison to numerically exact quantum dynamics simulations, it is found that all TAB variants provide a suitable description of the dynamical passage through a cascade of conical intersections. The TAB-w approach is found to provide a somewhat more accurate description of population dynamics than the original TAB method, with final absolute population errors $\leq$0.013 in all cases. Even when only four approximate eigenstates are computed, the use of approximate eigenstates was found to introduce minimal additional error (absolute population error $\leq$0.018 in all models).

In this work we investigate the properties of macroscopic bulk acoustic wave (BAW) devices, manufactured from crystalline piezoelectric lithium niobate at both room temperature and 4 K. We identify the fundamental acoustic modes in the crystal samples structures as well as characterise their loss properties and non-linear effects at both room and cryogenic temperatures. We compare these results to similar resonators made from quartz and conclude that lithium niobate may be suitable for certain experiments due to its low-loss and strong piezoelectric coupling. Additionally, we provide evidence for the reduction of crystal lattice defect sites by thermal annealing. We report exceptional quality factors for bulk acoustic modes in lithium niobate, with a maximum recorded quality factor of 8.9 million with a corresponding quality factor $\times$ frequency product of 3.8 $\times 10^{14}$ Hz.

Utilizing a two-phase supersonic chemically reacting flow solver with the Eulerian-Lagrangian method implemented in OpenFOAM, this study computationally investigates the formation of liquid-fueled oblique detonation waves (ODWs) within a pre-injection oblique detonation wave engine operating at an altitude of 30 km and a velocity of Mach 9. The inflow undergoes two-stage compression, followed by uniform mixing with randomly distributed n-heptane droplets before entering the combustor. The study examines the effects of droplet breakup models, gas-liquid ratios, and on-wedge strips on the ODW formation. Results indicate that under the pure-droplet condition, the ODW fails to form within the combustor, irrespective of the breakup models used. However, increasing the proportion of n-heptane vapor in the fuel/air mixture facilitates the ODW formation, because the n-heptane vapor rapidly participates in the gaseous reactions, producing heat and accelerating the transition from low- to intermediate-temperature chemistry. Additionally, the presence of on-wedge strips enhances ODW formation by inducing a bow shock wave within the combustor, which significantly increases the temperature, directly triggering intermediate-temperature chemistry and subsequent heat-release reactions, thereby facilitating the formation of ODW.

Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries and corrosion. While \textit{ab initio} simulations have provided valuable insights into model systems, the high computational cost limits their use in tackling complex systems of relevance to practical applications. Machine learning potentials offer a solution, but their application in electrochemistry remains challenging due to the difficulty in treating the dielectric response of electronic conductors and insulators simultaneously. In this work, we propose a hybrid framework of machine learning potentials that is capable of simulating metal/electrolyte interfaces by unifying the interfacial dielectric response accounting for local electronic polarisation in electrolytes and non-local charge transfer in metal electrodes. We validate our method by reproducing the bell-shaped differential Helmholtz capacitance at the Pt(111)/electrolyte interface. Furthermore, we apply the machine learning potential to calculate the dielectric profile at the interface, providing new insights into electronic polarisation effects. Our work lays the foundation for atomistic modelling of complex, realistic electrochemical interfaces using machine learning potential at \textit{ab initio} accuracy.

We study opinion dynamics over a directed multilayer network. In particular, we consider networks in which the impact of neighbors of agents on their opinions is proportional to their in-degree. Agents update their opinions over time to coordinate with their neighbors. However, the frequency of agents' interactions with neighbors in different network layers differs. Consequently, the multilayer network's adjacency matrices are time-varying. We aim to characterize how the frequency of activation of different layers impacts the convergence of the opinion dynamics process.

The coldness of the universe is a thermodynamic resource that has largely remained untapped for renewable energy generation. Recently, a growing interest in this area has led to a number of studies with the aim to realize the potential of tapping this vast resource for energy generation. While the theoretical calculation based on thermodynamic principles places an upper limit in the power density at the level of 6000 mW/m$^2$, most experimental demonstrations so far result in much lower power density at the level of tens of mW/m$^2$. Here we demonstrate, through design optimization involving the tailoring of the thermal radiation spectrum, the minimization of parasitic heat leakage, and the maximum conversion of heat to electricity, an energy generation system harvesting electricity from the thermal radiation of the ambient heat to the cold universe that achieves a sustained power density of 350 mW/m$^2$. We further demonstrate a power density at the 1000 mW/m$^2$ level using an additional heat source or heat storage that provides access to heat at a temperature above ambient. Our work here shows that the coldness of the universe can be harvested to generate renewable energy at the power density level that approaches the established bound.

We report on two-terminal-pair and four-terminal-pair test measurements of a 10-kohm resistance standard by means of a commercial precision LCR meter at frequencies up to 2 MHz. In the case of a two-terminal-pair configuration, we demonstrate that the effect of 2.5 m long measuring cables, which are inevitable for some special applications, can be corrected in the whole frequency range up to 2 MHz with an uncertainty of 9E-6 (k = 1) relative to the dc resistance value. Furthermore, the systematic effects of the LCR meter were investigated. Though distinctly larger than the resolution of the LCR meter, these effects are accurately reproducible. As such it should be possible in the future to calibrate the LCR meter against a well-known calculable high-frequency resistance standard with an uncertainty close to the few-parts-per-million type-A uncertainty of the LCR meter.

This study investigates the characteristics of Silicon Photomultipliers (SiPMs) with different pixel densities, focusing on their response across a wide dynamic range. Using an experimental setup that combines laser source and photomultiplier tubes (PMTs) for accurate light intensity calibration, we evaluated SiPMs with pixel counts up to 244,719 and pixel sizes down to 6 micrometers. To complement the experimental findings, a "Toy Monte Carlo" was developed to replicate the SiPMs' reponses under different lighting conditions, incorporating essential parameters such as pixel density and photon detection efficiency. The simulations aligned well with the experimental results for laser light, demonstrating similar nonlinearity trends. For BGO scintillation light, the simulations, which included multi-firing effect of pixels, showed significantly higher photon counts compared to the laser simulations. Furthermore, the simulated response derived in this research offer a method to correct for SiPM saturation effect, enabling accurate measurements in high-energy events even with SiPMs having a limited number of pixels.

This paper presents the design and optimization of a LYSO crystal-based electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photon as potential dark force mediator. The ECAL design has been meticulously evaluated through comprehensive simulations, focusing on optimizing dimensions, material choices, and placement within the detector array to enhance sensitivity in search for dark photon signatures while balancing cost and performance. The concluded ECAL design, comprising 2.5$\times$2.5$\times$4 cm$^3$ LYSO crystals arranged in a 52.5$\times$52.5$\times$44 cm$^3$ structure, ensures high energy resolution and effective energy containment. The study also explored the energy distribution across different ECAL regions and established a dynamic range for energy measurements, with a 4 GeV limit per crystal deemed sufficient. Additionally, the radiation tolerance of ECAL components was assessed, confirming the sustainability of LYSO crystals and radiation-resistant silicon sensors.

We have developed novel impedance standards based on thin-film surface-mount-device (SMD) resistors. Due to the small dimensions of such resistors, the quantities determining its frequency dependence are very small and can be either measured or numerically calculated. A series connection of thin-film SMD resistors allows to further improve the dc and the ac properties. The nominal resistance value of our application is 12.906 kohm but other values are just as possible. At frequencies up to 2 MHz, the calculated frequency dependence amounts to only a few parts per million of the dc value, which is about four orders of magnitude smaller than for all conventional calculable ac-dc resistors having a similar nominal dc value. To measure the frequency dependence, we use a precision LCR meter for frequencies up to 2 MHz that has a reproducibility of a few parts per million but a systematic uncertainty which is specified by the manufacturer to increase from 300 parts per million in the lower frequency range to 3000 parts per million at 2 MHz. Measurements of two very different SMD-based resistance standards allows verification of the model calculation as well as the calibration of the precision LCR meter, both with a relative uncertainty of a few parts per million in the whole frequency range. This boost in precision by two to three orders of magnitude enables new applications such as the verification of conventional calculable resistance standards, traceable calibration of high-frequency standards, and future high-frequency measurements of the quantum Hall resistance.

The intensity of the turbulence in tokamaks and stellarators depends on its ability to excite and sustain zonal flows. Insight into this physics may be gained by studying the ''residual'', i.e. the late-time linear response of the system to an initial perturbation. We investigate this zonal-flow residual in the limit of a small magnetic mirror ratio, where we find that the typical quadratic approximation to RH (Rosenbluth & Hinton, 1998) breaks down. Barely passing particles are in this limit central in determining the resulting level of the residual, which we estimate analytically. The role played by the population with large orbit width provides valuable physical insight into the response of the residual beyond this limit. Applying this result to tokamak, quasi-symmetric and quasi-isodynamic equilibria, using a near-axis approximation, we identify the effect to be more relevant (although small) in the core of quasi-axisymmetric fields, where the residual is smallest. The analysis in the paper also clarifies the relationship between the residual and the geodesic acoustic mode, whose typical theoretical set-ups are similar.

A muon-flux measurement behind the beam dump of the J-PARC Hadron Experimental Facility was performed with a compact muon detector that can be inserted into a vertical observing hole which was dug underground with 81 mm in diameter. The flux of the muons penetrating the beam dump was scanned vertically at intervals of 0.5 m, showing a wide distribution with a maximum at the beam level. The muon flux was consistent with the expectation from a Monte-Carlo simulation at more than 1 m away from the beam axis, which is expected to be used for signal-loss evaluation in the future KOTO II experiment for measuring rare kaon decays. The data can also be used in improving the accuracy of shielding calculations in the radiation protection.

The DArk Matter In CCDs at Modane (DAMIC-M) experiment is designed to search for light dark matter (m$_{\chi}$<10\,GeV/c$^2$) at the Laboratoire Souterrain de Modane (LSM) in France. DAMIC-M will use skipper charge-coupled devices (CCDs) as a kg-scale active detector target. Its single-electron resolution will enable eV-scale energy thresholds and thus world-leading sensitivity to a range of hidden sector dark matter candidates. A DAMIC-M prototype, the Low Background Chamber (LBC), has been taking data at LSM since 2022. The LBC provides a low-background environment, which has been used to characterize skipper CCDs, study dark current, and measure radiopurity of materials planned for DAMIC-M. It also allows testing of various subsystems like readout electronics, data acquisition software, and slow control. This paper describes the technical design and performance of the LBC.

We provide a comprehensive technical analysis of the data acquisition process with oscillating delay lines for Terahertz time domain spectroscopy. The utilization of these rapid stages, particularly in high-repetition rate systems, permits an effective reduction of noise content through averaging. However, caution must be exercised to optimize the averaging process, with the goal of significantly optimizing the dynamic range (DR) and signal-to-noise ratio (SNR). A free and open-source program, called parrot (Processing All Rapidly & Reliably Obtained THz-traces), is provided alongside this publication to overcome the discussed pitfalls and facilitate the acceleration of experimental setups and data analysis, thereby enhancing signal fidelity and reproducibility.

Topological insulators offer unique opportunities for novel electronics and quantum phenomena. However, intrinsic material limitations often restrict their applications and practical implementation. Over a decade ago it was predicted that a time-periodic perturbation can generate out-of-equilibrium states known as Floquet topological insulators (FTIs), hosting topologically protected transport and anomalous Hall physics, and opening routes to optically tunable bandstructures and devices compatible with petahertz electronics. Although such states have not yet been directly observed, indirect signatures such as the light-induced anomalous Hall effect were recently measured. Thus far, much remained experimentally unclear and fundamentally unknown about solid-state FTI and whether they can be employed for electronics. Here we demonstrate coherent control of photocurrents in light-dressed graphene. Circularly-polarized laser pulses dress the graphene band structure to obtain an FTI, and phase-locked second harmonic pulses drive electrons in the FTI. This approach allows us to measure resulting all-optical anomalous Hall photocurrents, FTI-valley-polarized currents, and photocurrent circular dichroism, all phenomena that put FTIs on equal footing with equilibrium topological insulators. We further present an intuitive description for the sub-optical-cycle light-matter interaction, revealing dynamical symmetry selection rules for photocurrents. All measurements are supported by strong agreement with ab-initio and analytic theory. Remarkably, the photocurrents show a strong sub-cycle phase-sensitivity that can be employed for ultrafast control in topotronics and spectroscopy. Our work connects Floquet and topological physics with attoscience and valleytronics, and goes beyond band structure engineering by initiating lightwave-driven dynamics in FTI states.

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.

Equatorial Kelvin waves can be affected by subtropical Rossby wave dynamics. Previous research has demonstrated the Kelvin wave growth in response to subtropical forcing and the resonant growth due to eddy momentum flux convergence. However, the relative importance of the wave-mean flow and wave-wave interactions for the Kelvin wave growth compared to the direct wave excitation by the external forcing has not been made clear. This study demonstrates the resonant Kelvin wave excitation by interactions of subtropical Rossby waves and the mean flow using a spherical shallow-water model. The use of Hough harmonics as basis functions makes Rossby and Kelvin waves prognostic variables of the model and allows the quantification of terms contributing to their tendencies in physical and wave space. The simulations show that Kelvin waves are resonantly excited by interactions of Rossby waves and the balanced zonal mean flow in the subtropics, provided the Rossby and Kelvin wave frequencies, which are modified by the mean flow, match. The resonance mechanism is substantiated by analytical expressions. The Kelvin wave tendencies are caused by velocity and depth tendencies: The velocity tendencies due to the meridional advection of zonal mean velocity can be outweighed by the zonal advection of Rossby wave velocity or by the depth tendencies due to Rossby wave divergence. Identifying the resonant excitation mechanism in data should contribute to the quantification of Kelvin wave variability originating in the subtropics.

C points, characterized by circular polarization in momentum space, play crucial roles in chiral wave manipulations. However, conventional approaches of achieving intrinsic C points using photonic crystals with broken symmetries suffer from low Q factor and are highly sensitive to structural geometry, rendering them fragile and susceptible to perturbations and disorders. In this letter, we report the realization of magneto-optical (MO) bound states in the continuum (BICs) using a symmetry-preserved planar photonic crystal, achieving intrinsic at-{\Gamma} C points that are robust against variation in structural geometry and external magnetic field. MO coupling between two dipole modes induces Zeeman splitting of the eigenfrequencies, leading to MO BICs and quasi-BICs with circular eigenstates for high-Q chiral responses. Furthermore, switchable C point handedness and circular dichroism are enabled by reversing the magnetic field. These findings unveil a new type of BICs with circular eigenstates and on-demand control of C points, paving the way for advanced chiral wave manipulation with enhanced light-matter interaction.

We unveil a new scenario for the formation of dissipative localised structures in nonlinear systems. Commonly, the formation of such structures arises from the connection of a homogeneous steady state with either another homogeneous solution or a pattern. Both scenarios, typically found in cavities with normal and anomalous dispersion, respectively, exhibit unique fingerprints and particular features that characterise their behaviour. However, we show that the introduction of a periodic non-Hermitian modulation in Kerr cavities hybridises the two established soliton formation mechanisms, embodying the particular fingerprints of both. In the resulting novel scenario, the stationary states acquire a dual behaviour, playing the role that was unambiguously attributed to either homogeneous states or patterns. These fundamental findings have profound practical implications for frequency comb generation, introducing unprecedented reversible mechanisms for real-time manipulation.

Normal microwave (MW) electromagnetic field detectors convert microwave power into voltages, which results in the loss of the vector characteristics of the microwave field. In this work, we developed a MW magnetic field (h-field) vector detector based on the nonresonant spin rectification effect. By measuring and analyzing the angle dependence of the rectification voltages under nonresonant conditions, we can extract the three components of the h-field. As an initial test of this method, we obtained the h-field distributions at 5.4 GHz generated by a coplanar waveguide with sub-wavelength resolution. Compared to methods using ferromagnetic resonance, this technique offers a faster and more convenient way to determine the spatial distribution of the h-field, which can be used for MW integrated circuit optimization and fault diagnosis.

The continuous technological development of electronic devices and the introduction of new materials leads to ever greater demands on the fabrication of semiconductor heterostructures and their characterization. This work focuses on optimizing Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) depth profiles of semiconductor heterostructures aiming at a minimization of measurement-induced profile broadening. As model system, a state-of-the-art Molecular Beam Epitaxy (MBE) grown multilayer homostructure consisting of $^{\textit{nat}}$Si/$^{28}$Si bilayers with only 2 nm in thickness is investigated while varying the most relevant sputter parameters. Atomic concentration-depth profiles are determined and an error function based description model is used to quantify layer thicknesses as well as profile broadening. The optimization process leads to an excellent resolution of the multilayer homostructure. The results of this optimization guide to a ToF-SIMS analysis of another MBE grown heterostructure consisting of a strained and highly purified $^{28}$Si layer sandwiched between two Si$_{0.7}$Ge$_{0.3}$ layers. The sandwiched $^{28}$Si layer represents a quantum well that has proven to be an excellent host for the implementation of electron-spin qubits.

During recent years interest has been rising for applications of vector light beams towards magnetic field sensing. In particular, a series of experiments were performed to extract information about properties of static magnetic fields from absorption profiles of light passing through an atomic gas target. In the present work, we propose an extension to this method for oscillating magnetic fields. To investigate this scenario, we carried out theoretical analysis based on the time-dependent density matrix theory. We found that absorption profiles, even when averaged over typical observation times, are indeed sensitive to both strength and frequency of the time-dependent field, thus opening the prospect for a powerful diagnostic technique. To illustrate this sensitivity, we performed detailed calculations for the $5s \;\, {}^2S_{1/2}$ ($F=1$) $-$ $5p \;\, {}^2 P_{3/2}$ ($F=0$) transition in rubidium atoms, subject to a superposition of an oscillating (test) and a static (reference) magnetic field.

We establish a number of results concerning conditions for minimum energy dissipation and advective travel time in porous and fractured media. First, we establish a pair of converse results concerning fluid motion along a streamline between two points of fixed head: the minimal advective time is achieved under conditions of constant energy dissipation, and minimal energy dissipation is achieved under conditions of constant velocity along the streamline (implying homogeneous conductivity in the vicinity of the streamline). We also show directly by means of variational methods that minimum advection time along a streamline with a given average conductivity is achieved when the conductivity is constant. Finally, we turn our attention to minimum advection time and energy dissipation in parallel and sequential fracture systems governed by the cubic law: for which fracture cross-section and conductivity are intimately linked. We show that, as in porous domains, flow partitioning between different pathways always acts to minimize system energy dissipation. Finally, we consider minimum advection time as a function of aperture distribution in a sequence of fracture segments. We show that, for a fixed average aperture, a uniform-aperture system displays the shortest advection time. However, we also show that any sufficiently small small perturbations in aperture away from uniformity always act to reduce advection time.

The efficient and voidless filling of microcavities is of great importance for Lab-on-a-Chip applications. However, predicting whether microcavities will be filled or not under different circumstances is still difficult due to the local flow effects dominated by surface tension. In this work, a close-up study of the microcavity filling process is presented, shedding light on the mechanisms of the filling process using experimental insights accompanied by 3D numerical simulations. The movement of a fluid interface over a microcavity array is investigated optically under consideration of different fluids, capillary numbers, and cavity depths, revealing a regime map of different filling states. Moreover, the transient interface progression over the cavities is analyzed with attention to small-scale effects such as pinning. Besides the visual analysis of the image series, quantitative data of the dynamic contact angle and the interface progression is derived using an automated evaluation workflow. In addition to the experiments, 3D Volume-of-Fluid simulations are employed to further investigate the interface shape. It is shown that the simulations can not only predict the filling states in most cases, but also the transient movement and shape of the interface. The data and code associated with this work are publicly available at Bosch Research GitHub and at the TUDatalib data repository.

Scale-resolving flow simulations often feature several million [thousand] spatial [temporal] discrete degrees of freedom. When storing or re-using these data, e.g., to subsequently train some sort of data-based surrogate or compute consistent adjoint flow solutions, a brute-force storage approach is practically impossible. Therefore, -- mandatory incremental -- Reduced Order Modeling (ROM) approaches are an attractive alternative since only a specific time horizon is effectively stored. This bunched flow solution is then used to enhance the already computed ROM so that the allocated memory can be released and the procedure repeats. This paper utilizes an incremental truncated Singular Value Decomposition (itSVD) procedure to compress flow data resulting from scale-resolving flow simulations. To this end, two scenarios are considered, referring to an academic Large Eddy Simulation (LES) around a circular cylinder at Re=1.4E+05 as well as an industrial case that employs a hybrid filtered/averaged Detached Eddy Simulation (DES) on the flow around the superstructure of a full-scale feeder ship at Re=5E+08. The paper's central focus is on an aspect of practical relevance: how much information of the computed scale-resolving solution should be used by the ROM, i.e., how much redundancy occurs in the resolved turbulent fluctuations that favors ROM. In the course of the tSVD employed, this goes hand in hand with the question of "how many singular values of the snapshot-matrix should be neglected (or considered)" -- without (a) re-running the simulation several times in a try-and-error procedure and (b) still obtain compressed results below the model and discretization error. An adaptive strategy is utilized to obtain a fully adaptive data reduction of O(95) percent via a computational overhead of O(10) percent with a mean accuracy of reconstructed local and global flow data of O(0.1) percent.

The transport and distribution of organisms like larvae, seeds or litter in the ocean as well as particles in industrial flows is often approximated by a transport of tracer particles. We present a theoretical investigation to check the accuracy of this approximation by studying the transport of inertial particles between different islands embedded in an open hydrodynamic flow aiming at the construction of a Lagrangian flow network reflecting the connectivity between the islands. To this end we formulate a two-dimensional kinematic flow field which allows the placement of an arbitrary number of islands at arbitrary locations in a flow of prescribed direction. To account for the mixing in the flow we include a von K\'arm\'an vortex street in the wake of each island. We demonstrate that the transport probabilities of inertial particles making up the links of the Lagrangian flow network depend essentially on the properties of the particles, i.e. their Stokes number, the properties of the flow and the geometry of the setup of the islands. We find a strong segregation between aerosols and bubbles. Upon comparing the mobility of inertial particles to that of tracers or neutrally buoyant particles, it becomes apparent that the tracer approximation may not always accurately predict the probability of movement. This can lead to inconsistent forecasts regarding the fate of marine organisms, seeds, litter or particles in industrial flows.

In a companion paper, we put forth a thermodynamic model for complex formation via a chemical reaction involving multiple macromolecular species, which may subsequently undergo liquid-liquid phase separation and a further transition into a gel-like state. In the present work, we formulate a thermodynamically consistent kinetic framework to study the interplay between phase separation, chemical reaction and aging in spatially inhomogeneous macromolecular mixtures. A numerical algorithm is also proposed to simulate domain growth from collisions of liquid and gel domains via passive Brownian motion in both two and three spatial dimensions. Our results show that the coarsening behavior is significantly influenced by the degree of gelation and Brownian motion. The presence of a gel phase inside condensates strongly limits the diffusive transport processes, and Brownian motion coalescence controls the coarsening process in systems with high area/volume fractions of gel-like condensates, leading to formation of interconnected domains with atypical domain growth rates controlled by size-dependent translational and rotational diffusivities.

Integrated spectrometers hold great promise for in-situ, in vitro, and even in vivo near-infrared (NIR) sensing applications. However, practical NIR applications require fine resolution, high accuracy, and mostly importantly, ultra-wide observation window that spans multiple overtone regions, for capturing spectral fingerprints. In this paper, we present an integrated reconstructive spectrometer utilizing dispersion-engineered ring resonators, achieving an over 520 nm operational bandwidth together with a superior resolution of less than 8 pm. This translates into an over 65,000 bandwidth-to-resolution ratio that represents a significant record breakthrough for miniaturized spectrometers. Moreover, we showcase a packaged spectrometer chip with auxiliary electronics, creating a fully functional NIR spectroscopic sensor. Various bandwidth-demanding NIR sensing applications are examined, including the classification of different types of solid substances and the concentration test of aqueous and organic solutions, all achieving approximately 100% accuracy. Specifically, we test the sensor's detection limit using glucose solutions and successfully identify concentrations as low as 0.1% (i.e. 100 mg/dL), which matches that of the commercial benchtop counterparts and sets up a new benchmark for miniaturized spectroscopic sensors.

Non-reciprocal interactions are ubiquitous in nature, and in Physics this realization has started to push boundaries by making non-hermiticity a fundamental rather than exceptional property. Despite the prevalence of evanescent coupling in physics, little attention has been paid to wavefunction profiles, with reciprocity often assumed for practical reasons. Our work challenges this assumption by demonstrating the origin of non-reciprocity in a photonic platform. We experimentally validate non-reciprocal dynamics on directional couplers fabricated via femtosecond laser writing, and corroborate our findings through continuous simulations. Extending these insights, we observe the topological Non-Hermitian Skin Effect in a Rice-Mele lattice model. Our findings open new avenues of research using simple and scalable configurations, and potentially guiding future developments in Physics and beyond.

Birds, bats and many insects can tuck their wings against their bodies at rest and deploy them to power flight. Whereas birds and bats use well-developed pectoral and wing muscles and tendons, how insects control these movements remains unclear, as mechanisms of wing deployment and retraction vary among insect species. Beetles (Coleoptera) display one of the most complex wing mechanisms. For example, in rhinoceros beetles, the wing deployment initiates by fully opening the elytra and partially releasing the hindwings from the abdomen. Subsequently, the beetle starts flapping, elevates the hindwings at the bases, and unfolds the wingtips in an origami-like fashion. Whilst the origami-like fold have been extensively explored, limited attention has been given to the hindwing base deployment and retraction, which are believed to be driven by thoracic muscles. Using high-speed cameras and robotic flapping-wing models, here we demonstrate that rhinoceros beetles can effortlessly elevate the hindwings to flight position without the need for muscular activity. We show that opening the elytra triggers a spring-like partial release of the hindwings from the body, allowing the clearance needed for subsequent flapping motion that brings the hindwings into flight position. The results also show that after flight, beetles can leverage the elytra to push the hindwings back into the resting position, further strengthening the hypothesis of a passive deployment mechanism. Finally, we validate the hypothesis with a flapping microrobot that passively deploys its wings for stable controlled flight and retracts them neatly upon landing, which offers a simple yet effective approach to the design of insect-like flying micromachines.

We have experimentally demonstrated the effectiveness of beta-gallium oxide (beta-Ga2O3) ferroelectric fin field-effect transistors (Fe-FinFETs) for the first time. Atomic layer deposited (ALD) hafnium zirconium oxide (HZO) is used as the ferroelectric layer. The HZO/beta-Ga2O3 Fe-FinFETs have wider counterclockwise hysteresis loops in the transfer characteristics than that of conventional planar FET, achieving record-high memory window (MW) of 13.9 V in a single HZO layer. When normalized to the actual channel width, FinFETs show an improved ION/IOFF ratio of 2.3x10^7 and a subthreshold swing value of 110 mV/dec. The enhanced characteristics are attributed to the low-interface state density (Dit), showing good interface properties between the beta-Ga2O3 and HZO layer. The enhanced polarization due to larger electric fields across the entire ferroelectric layer in FinFETs is validated using Sentaurus TCAD. After 5x10^6 program/erase (PGM/ERS) cycles, the MW was maintained at 9.2 V, and the retention time was measured up to 3x10^4 s with low degradation. Therefore, the ultrawide bandgap (UWBG) Fe-FinFET was shown to be one of the promising candidates for high-density non-volatile memory devices.

The Low-Level RF (LLRF) control circuits of linear accelerators (LINACs) are conventionally realized with heterodyne based architectures, which have analog RF mixers for up and down conversion with discrete data converters. We have developed a new LLRF platform for C-band linear accelerator based on the Frequency System-on-Chip (RFSoC) device from AMD Xilinx. The integrated data converters in the RFSoC can directly sample the RF signals in C-band and perform the up and down mixing digitally. The programmable logic and processors required for signal processing for the LLRF control system are also included in a single RFSoC chip. With all the essential components integrated in a device, the RFSoC-based LLRF control platform can be implemented more cost-effectively and compactly, which can be applied to a broad range of accelerator applications. In this paper, the structure and configuration of the newly developed LLRF platform will be described. The LLRF prototype has been tested with high power test setup with a Cool Cooper Collider (C\(^3\)) accelerating structure. The LLRF and the solid state amplifier (SSA) loopback setup demonstrated phase jitter in 1 s as low as 115 fs, which is lower than the requirement of C\(^3\). The rf signals from the klystron forward and accelerating structure captured with peak power up to 16.45 MW will be presented and discussed.

Using high resolution Vlasov - Poisson simulations, evolution of driven ``cold" electron plasma wave (EPW) in the presence of stationary inhomogeneous background of ions is studied. Mode coupling dynamics between ``cold'' EPW with phase velocity $v_{\phi}$ greater than thermal velocity i.e $v_{\phi} \gg v_{thermal}$ and its inhomogeneity induced sidebands is illustrated as an initial value problem. In driven cases, formation of BGK like phase space structures corresponding to sideband modes due to energy exchange from primary mode to bulk particles via wave-wave and wave-particle interactions leading to particle trapping is demonstrated for inhomogeneous plasma. Qualitative comparison studies between initial value perturbation and driven problem is presented, which examines the relative difference in energy transfer time between the interacting modes. Effect of variation in background ion inhomogeneity amplitude as well as ion inhomogeneity scale length on the driven EPWs is reported.

We propose an algorithm to identify numerically periodic solutions of high-dimensional dynamical systems and their local stability properties. One of the most popular approaches is the Harmonic Balance Method (HBM), which expresses the cycle as a sum of Fourier modes and analyses its stability using the Hill's method. A drawback of Hill's method is that the relevant Floquet exponents have to be chosen from all the computed exponents. To overcome this problem the current work discusses the application of Chebyshev polynomials to the description of the time dependence of the periodic dynamics. The stability characteristics of the periodic orbit are directly extracted from the linearisation around the periodic orbit. The method is compared with the HBM with examples from Lorenz and Langford systems. The main advantage of the present method is that, unlike HBM, it allows for an unambiguous determination of the Floquet exponents. The method is applied to natural convection in a differentially heated cavity which demonstrates its potential for large scale problems arising from the discretisation of the incompressible Navier-Stokes equations.

One of the most viable renewable energies is solar power because of its versatility, reliability, and abundance.In the market, a majority of the solar panels are made from silicon wafers.These solar panels have an efficiency of 26.4 percent and can last more than 25 years.The perovskite solar cell is a relatively new type of solar technology that has a similar maximum efficiency and much cheaper costs, the only downside is that it is less stable and the most efficient type uses lead.The name perovskite refers to the crystal structure with an ABX3 formula of the perovskite layer of the cell.All materials possess a property called a band gap.The smaller the band gap the more conductive the material, but this does not necessarily mean that the smaller the band gap the better the solar cell.The Shockley-Queisser limit provides the optimal band gap in terms of efficiency for a single junction solar cell which is 1.34 eV for single junction cells.This research focuses on tuning the band gap of lead-free perovskites through B-site cation replacement. Through this investigation, the optical band gaps of tin and lead perovskites were re-established.However, the copper-based perovskite disagrees with existing DFT calculations.Additionally, the mixed tin and copper perovskite in this experiment contradicts the intuitive prediction.

This work tackles a significant challenge in dynamo theory: the possibility of long-term amplification and maintenance of an axisymmetric magnetic field. We introduce a novel model that allows for non-trivial axially-symmetric steady-state solutions for the magnetic field, particularly when the dynamo operates primarily within a "nearly-spherical" toroidal volume inside a fluid shell surrounding a solid core. In this model, Ohm's law is generalized to include the restoring friction force, which aligns the velocity of the shell with the rotational speed of the inner core and outer mantle. Our findings reveal that, in this context, Cowling's theorem and the neutral point argument are modified, leading to magnetic energy growth for a suitable choice of toroidal flow. The global equilibrium magnetic field that emerges from our model exhibits a dipolar character.

This paper presents a neural network-based methodology for the decomposition of transport-dominated fields using the shifted proper orthogonal decomposition (sPOD). Classical sPOD methods typically require an a priori knowledge of the transport operators to determine the co-moving fields. However, in many real-life problems, such knowledge is difficult or even impossible to obtain, limiting the applicability and benefits of the sPOD. To address this issue, our approach estimates both the transport and co-moving fields simultaneously using neural networks. This is achieved by training two sub-networks dedicated to learning the transports and the co-moving fields, respectively. Applications to synthetic data and a wildland fire model illustrate the capabilities and efficiency of this neural sPOD approach, demonstrating its ability to separate the different fields effectively.

We explore the impact of hydrodynamic interactions on the conformational and dynamical properties of wet tangentially-driven active polymers using multiparticle collision dynamics simulations. By analyzing active filaments with varying degrees of flexibility, we find that fluid-mediated interactions significantly influence both their conformation and dynamics. These interactions cause polymer conformations to shrink, especially for semiflexible polymers at high activity levels, where the average size of wet chains becomes nearly three times smaller, due to formation of helix-like structures. This hydrodynamic-induced shrinkage is a hallmark of active polymers, as fluid-mediated interactions have a minimal effect on the mean conformation of passive polymers. Furthermore, for tangentially-driven polymers where activity and conformation are coupled, hydrodynamic interactions significantly enhance the orientational and translational dynamics compared to their dry counterparts.

Future millimeter wavelength experiments aim to both increase aperture diameters and broaden bandwidths to increase the sensitivity of the receivers. These changes produce a challenging anti-reflection (AR) design problem for refracting and transmissive optics. The higher frequency plastic optics require consistently thin polymer coats across a wide area, while wider bandwidths require multilayer designs. We present multilayer AR coats for plastic optics of the high frequency BICEP Array receiver (200-300 GHz) utilizing an expanded polytetrafluoroethylene (ePTFE) membrane, layered and compressively heat-bonded to itself. This process allows for a range of densities (from 0.3g/cc to 1g/cc) and thicknesses (>0.05mm) over a wide radius (33cm), opening the parameter space of potential AR coats in interesting directions. The layered ePTFE membrane has been combined with other polymer layers to produce band average reflections between 0.2% and 0.6% on high density polyethylene and a thin high modulus polyethylene window, respectively.

Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) are the neural network of choice, and significant progress has been made in optimizing their training. Recently, Kolmogorov-Arnold Networks (KANs) were introduced as a viable alternative, with the potential of offering better interpretability and efficiency while requiring fewer parameters. In this paper, we present a fast JAX-based implementation of grid-dependent Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for solving PDEs. We propose an adaptive training scheme for PIKANs, incorporating known MLP-based PINN techniques, introducing an adaptive state transition scheme to avoid loss function peaks between grid updates, and proposing a methodology for designing PIKANs with alternative basis functions. Through comparative experiments we demonstrate that these adaptive features significantly enhance training efficiency and solution accuracy. Our results illustrate the effectiveness of PIKANs in improving performance for PDE solutions, highlighting their potential as a superior alternative in scientific and engineering applications.

Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to the lack of effective integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed using these context-aware representations. Our experiments demonstrate that CKG's configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model surpasses benchmark models, achieving an average MAE of $3.46\pm0.01$ and a MAPE of $14.76\pm0.09\%$ for traffic speed predictions from 10 to 120 minutes. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model's ability to prioritize recent time slots in prediction from the sequence-based view. The CKG framework's model-agnostic nature suggests its potential applicability in various applications of intelligent transportation systems. Overall, this study underscores the importance of incorporating domain-specific contexts into traffic forecasting and merging context-aware knowledge graphs with neural networks to enhance accuracy.

Exciton-polariton condensation in direct bandgap semiconductors strongly coupled to light enables a broad range of fundamental studies and applications like low-threshold and electrically driven lasing. Yet, materials hosting exciton-polariton condensation in ambient conditions are rare, with fabrication protocols that are often inefficient and non-scalable. Here, room-temperature exciton-polariton condensation and lasing is observed in a microcavity with embedded formamidiniumlead bromide (FAPbBr$_3$) perovskite film. This optically active material is spin-coated onto the microcavity mirror, which makes the whole device scalable up to large lateral sizes. The sub-$\mu$m granulation of the polycrystalline FAPbBr$_3$ film allows for observation of polariton lasing in a single quantum-confined mode of a polaritonic 'quantum dot'. Compared to random photon lasing, observed in bare FAPbBr$_3$ films, polariton lasing exhibits a lower threshold, narrower linewidth, and an order of magnitude longer coherence time. Both polariton and random photon lasing are observed under the conditions of pulsed optical pumping, and persist without significant degradation for up to 6 and 17 hours of a continuous experimental run, respectively. This study demonstrates the excellent potential of the FAPbBr$_3$ perovskite as a new material for room-temperature polaritonics, with the added value of efficient and scalable fabrication offered by the solution-based spin-coating process.

In this paper, we present a generic physics-informed generative model called MPDM that integrates multi-fidelity physics simulations with diffusion models. MPDM categorizes multi-fidelity physics simulations into inexpensive and expensive simulations, depending on computational costs. The inexpensive simulations, which can be obtained with low latency, directly inject contextual information into DDMs. Furthermore, when results from expensive simulations are available, MPDM refines the quality of generated samples via a guided diffusion process. This design separates the training of a denoising diffusion model from physics-informed conditional probability models, thus lending flexibility to practitioners. MPDM builds on Bayesian probabilistic models and is equipped with a theoretical guarantee that provides upper bounds on the Wasserstein distance between the sample and underlying true distribution. The probabilistic nature of MPDM also provides a convenient approach for uncertainty quantification in prediction. Our models excel in cases where physics simulations are imperfect and sometimes inaccessible. We use a numerical simulation in fluid dynamics and a case study in heat dynamics within laser-based metal powder deposition additive manufacturing to demonstrate how MPDM seamlessly integrates multi-idelity physics simulations and observations to obtain surrogates with superior predictive performance.

Physics-Informed Neural Networks (PINNs) are a machine learning technique for solving partial differential equations (PDEs) by incorporating PDEs as loss terms in neural networks and minimizing the loss function during training. Tomographic imaging, a method to reconstruct internal properties from external measurement data, is highly complex and ill-posed, making it an inverse problem. Recently, PINNs have shown significant potential in computational fluid dynamics (CFD) and have advantages in solving inverse problems. However, existing research has primarily focused on semi-inverse Electrical Impedance Tomography (EIT), where internal electric potentials are accessible. The practical full inverse EIT problem, where only boundary voltage measurements are available, remains challenging. To address this, we propose a two-stage hybrid learning framework combining Convolutional Neural Networks (CNNs) and PINNs to solve the full inverse EIT problem. This framework integrates data-driven and model-driven approaches, combines supervised and unsupervised learning, and decouples the forward and inverse problems within the PINN framework in EIT. Stage I: a U-Net constructs an end-to-end mapping from boundary voltage measurements to the internal potential distribution using supervised learning. Stage II: a Multilayer Perceptron (MLP)-based PINN takes the predicted internal potentials as input to solve for the conductivity distribution through unsupervised learning.

When an electric field of light with a frequency of{\hbar}{\Omega}is applied to a solid, Floquet states, consisting of sidebands with an interval of {\hbar}{\Omega} around an electronic state, are expected to be formed. However, only a few studies have experimentally detected such sidebands. Here, we apply mid-infrared pump near-infrared reflection probe spectroscopy to a one-dimensional Mott insulator, bis(ethylendithio)tetrathianfulvalence-difluorotetracyanoquinodimethane (ET-F2TCNQ), to detect the transient change in reflectivity R,{\Delta}R/R, due to the formation of excitonic Floquet states. Analyses, considering both odd- and even-parity excitons, demonstrate that the {\Delta}R/R spectrum reflects the formation of the first-order Floquet sidebands of excitons, and its spectral shape strongly depends on the widths of excitonic states. The experimental and analytical approach reported here is effective in demonstrating excitonic Floquet states in various solids.

Interactions of molecules with their environment influence the course and outcome of almost all chemical reactions. However, classical computers struggle to accurately simulate complicated molecule-environment interactions because of the steep growth of computational resources with both molecule size and environment complexity. Therefore, many quantum-chemical simulations are restricted to isolated molecules, whose dynamics can dramatically differ from what happens in an environment. Here, we show that analog quantum simulators can simulate open molecular systems by using the native dissipation of the simulator and injecting additional controllable dissipation. By exploiting the native dissipation to simulate the molecular dissipation -- rather than seeing it as a limitation -- our approach enables longer simulations of open systems than are possible for closed systems. In particular, we show that trapped-ion simulators using a mixed qudit-boson (MQB) encoding could simulate molecules in a wide range of condensed phases by implementing widely used dissipative processes within the Lindblad formalism, including pure dephasing and both electronic and vibrational relaxation. The MQB open-system simulations require significantly fewer additional quantum resources compared to both classical and digital quantum approaches.

Flow control is key to maximize energy efficiency in a wide range of applications. However, traditional flow-control methods face significant challenges in addressing non-linear systems and high-dimensional data, limiting their application in realistic energy systems. This study advances deep-reinforcement-learning (DRL) methods for flow control, particularly focusing on integrating group-invariant networks and positional encoding into DRL architectures. Our methods leverage multi-agent reinforcement learning (MARL) to exploit policy invariance in space, in combination with group-invariant networks to ensure local symmetry invariance. Additionally, a positional encoding inspired by the transformer architecture is incorporated to provide location information to the agents, mitigating action constraints from strict invariance. The proposed methods are verified using a case study of Rayleigh-B\'enard convection, where the goal is to minimize the Nusselt number Nu. The group-invariant neural networks (GI-NNs) show faster convergence compared to the base MARL, achieving better average policy performance. The GI-NNs not only cut DRL training time in half but also notably enhance learning reproducibility. Positional encoding further enhances these results, effectively reducing the minimum Nu and stabilizing convergence. Interestingly, group invariant networks specialize in improving learning speed and positional encoding specializes in improving learning quality. These results demonstrate that choosing a suitable feature-representation method according to the purpose as well as the characteristics of each control problem is essential. We believe that the results of this study will not only inspire novel DRL methods with invariant and unique representations, but also provide useful insights for industrial applications.

When objects are packed in a cluster, physical interactions are unavoidable. Such interactions emerge because of the objects geometric features; some of these features promote entanglement, while others create repulsion. When entanglement occurs, the cluster exhibits a global, complex behaviour, which arises from the stochastic interactions between objects. We hereby refer to such a cluster as an entangled granular metamaterial. We investigate the geometrical features of the objects which make up the cluster, henceforth referred to as grains, that maximise entanglement. We hypothesise that a cluster composed from grains with high propensity to tangle, will also show propensity to interact with a second cluster of tangled objects. To demonstrate this, we use the entangled granular metamaterials to perform complex robotic picking tasks, where conventional grippers struggle. We employ an electromagnet to attract the metamaterial (ferromagnetic) and drop it onto a second cluster of objects (targets, non-ferromagnetic). When the electromagnet is re-activated, the entanglement ensures that both the metamaterial and the targets are picked, with varying degrees of physical engagement that strongly depend on geometric features. Interestingly, although the metamaterials structural arrangement is random, it creates repeatable and consistent interactions with a second tangled media, enabling robust picking of the latter.

In this article, the Curie-Weiss type behavior and the appearance of an "interaction" or "ordering" temperature for a collection of magnetic nanoparticles is explored theoretically. We show that some systems where an interaction temperature is reported are too dilute for dipolar interactions to play a role unless at least some of the particles are clumped together. We then show using the most simple type of clumps (particle pairs) that positive and negative interaction temperatures are possible due to dipolar interactions. The clump orientation dramatically changes this result. Finally, we show that an apparent interaction temperature can be measured in magnetic nanoparticle systems that have no interactions between particles, due to some alignment of anisotropy easy axes. These results show that nanoscale physical structures affect the measured magnetic response of nanoparticles.

Solar wind modelling has become a crucial area of study due to the increased dependence of modern society on technology, navigation, and power systems. Accurate space weather forecasts can predict upcoming threats to Earth's geospace. In this study, we examine a novel full magnetohydrodynamic (MHD) chain from the Sun to Earth. The goal of this study is to demonstrate the capabilities of the full MHD modelling chain from the Sun to Earth by finalising the implementation of the full MHD coronal model into the COolfluid COroNa UnsTructured (COCONUT) model and coupling it to the MHD heliospheric model Icarus. The resulting coronal model has significant advantages compared to the pre-existing polytropic alternative, as it models a more realistic bi-modal wind, which is crucial for heliospheric studies. In this study, only thermal conduction, radiative losses, and approximated coronal heating function were considered in the energy equation. A realistic specific heat ratio was applied. The output of the coronal model was used to onset the 3D MHD heliospheric model Icarus. A minimum solar activity case was chosen as the first test case for the full MHD model. The numerically simulated data in the corona and the heliosphere were compared to observational products. We present a first attempt to obtain the full MHD chain from the Sun to Earth with COCONUT and Icarus. The coronal model has been upgraded to a full MHD model for a realistic bi-modal solar wind configuration. The approximated heating functions have modelled the wind reasonably well, but simple approximations are not enough to obtain a realistic density-speed balance or realistic features in the low corona and farther, near the outer boundary. The full MHD model was computed in 1.06 h on 180 cores of the Genius cluster of the Vlaams Supercomputing Center, which is only 1.8 times longer than the polytropic simulation.

High energy electrons carry much of a solar flare's energy. Therefore, understanding changes in electron beam distributions during their propagation is crucial. A key focus of this paper is how the co-spatial return current reduces the energy flux carried by these accelerated electrons. We systematically compute this reduction for various beam and plasma parameters relevant to solar flares. Our 1D model accounts for collisions between beam and plasma electrons, return current electric-field deceleration, thermalization in a warm target approximation, and runaway electron contributions. The results focus on the classical (Spitzer) regime, offering a valuable benchmark for energy flux reduction and its extent. Return current losses are only negligible for the lowest nonthermal fluxes. We calculate the conditions for return current losses to become significant and estimate the extent of the modification to the beam's energy flux density. We also calculate two additional conditions which occur for higher injected fluxes: (1) where runaway electrons become significant, and (2) where current-driven instabilities might become significant, requiring a model that self-consistently accounts for them. Condition (2) is relaxed and the energy flux losses are reduced in the presence of runaway electrons. All results are dependent on beam and co-spatial plasma parameters. We also examine the importance of the reflection of beam electrons by the return-current electric field. We show that the interpretation of a number of flares needs to be reviewed to account for the effects of return currents.

Nonlinear simulations of neutron star mergers are complicated by the need to represent turbulent dynamics. As we cannot (yet) perform simulations that resolve accurately both the gravitational-wave scale and the smallest scales at which magneto/hydrodynamic turbulence plays a role, we need to rely on approximations. Addressing this problem in the context of large-eddy models, we outline a coherent Lagrangian filtering framework that allows us to explore the many issues that arise, linking conceptual problems to practical implementations and the interpretation of the results. We develop understanding crucial for quantifying unavoidable uncertainties in current and future numerical relativity simulations and consider the implications for neutron-star parameter estimation and constraints on the equation of state of matter under extreme conditions.

Surface albedo is an important parameter in radiative transfer simulations of the Earth's system, as it is fundamental to correctly calculate the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance black-sky albedo dataset (MCD43D, version 6.1) gives detailed albedo maps in seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only in seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra, and to correctly retrieve atmospheric and cloud properties from Earth's remote sensing. We use a Principal Component Analysis (PCA) regression algorithm to generate hyperspectral albedo maps of Earth. Combining different datasets of hyperspectral reflectance laboratory measurements for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct the albedo maps in the entire wavelength range from 400 to 2500~nm. The PCA method is trained with a 10-years average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05{\deg} in latitude and longitude, a spectral resolution of 10~nm, and a temporal resolution of 1~day. Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities and icy surfaces, and study their seasonal variability. These albedo maps shall enable to refine calculations of Earth's energy budget, its seasonal variability, and improve climate simulations.

Employing video microscopy, we explore the cage dynamics for colloidal particles confined in quasi-two dimensions (q2D). Our experiments reveal that while ensemble-averaged dynamics of cages are isotropic in the laboratory frame, its evolution in the displacement frame of the caged particle is anisotropic and asymmetric. In turn, this leads to particles in specific regions of the cage contributing either to cage persistence or breaking, influencing the structural relaxation of the fluid. Our findings, thus, provide microscopic insights into cage evolution and dynamics for colloidal fluids in q2D, with direct potential implications for the flow of complex fluids, structural relaxation in dense suspensions, and collective motion in active matter in confined geometries.

Researchers face the trade-off between publishing mobility data along with their papers while simultaneously protecting the privacy of the individuals. In addition to the fundamental anonymization process, other techniques, such as spatial discretization and, in certain cases, location concealing or complete removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records or the number of unique users in a given spatial unit, reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. It was demonstrated that even without disclosing the exact location, the patterns of human mobility can still reveal the urban area from which the data was collected. The presented locating method was tested on other cities using different open data sets and against coarser spatial discretization units. While publishing mobility data is essential for research, it was demonstrated that concealing the observation area is insufficient to prevent the identification of the urban area. Furthermore, using larger discretization units alone is an ineffective solution to the problem of the observation area re-identification. Instead of obscuring the observation area, noise should be added to the trajectories to prevent user identification.

This study investigates the interpretability, classification, and segmentation of CT-scan images of rock samples, with a particular focus on the application of DINOv2 within Geosciences. We compared various segmentation techniques to evaluate their efficacy, efficiency, and adaptability in geological image analysis. The methods assessed include the Otsu thresholding method, clustering techniques (K-means and fuzzy C-means), a supervised machine learning approach (Random Forest), and deep learning methods (UNet and DINOv2). We tested these methods using ten binary sandstone datasets and three multi-class calcite datasets. To begin, we provide a thorough interpretability analysis of DINOv2's features in the geoscientific context, discussing its suitability and inherent ability to process CT-scanned rock data. In terms of classification, the out-of-the-box DINOv2 demonstrates an impressive capability to perfectly classify rock images, even when the CT scans are out of its original training set. Regarding segmentation, thresholding and unsupervised methods, while fast, perform poorly despite image preprocessing, whereas supervised methods show better results. We underscore the computational demands of deep learning but highlight its minimal intervention, superior generalization, and performance without additional image preprocessing. Additionally, we observe a lack of correlation between a network's depth or the number of parameters and its performance. Our results show that a LoRA fine-tuned DINOv2 excels in out-of-distribution segmentation and significantly outperforms other methods in multi-class segmentation. By systematically comparing these methods, we identify the most efficient strategy for meticulous and laborious segmentation tasks. DINOv2 proves advantageous, achieving segmentations that could be described as "better than ground-truth" against relatively small training sets.

We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic relationships -- in the form of ordinary differential equations -- while preserving critical system behaviors. This approach allows for expedited 'what if' studies and sensitivity analyses, essential for informed policy-making. Our findings, from a case study of Baltimore, MD, indicate that this machine learning-augmented coarse-grained model serves as a powerful instrument for deciphering the complex interactions between social factors, geography, and exogenous stressors, offering a valuable asset for system forecasting and resilience planning.

Spaced-based mirrors are a developing use-case for Additive Manufacturing (AM), the process that builds a part layer-by-layer. The increased geometric freedom results in novel and advantageous designs previously unachievable. Conventionally, mirror fabrication uses subtractive (milling & turning), formative (casting) and fabricative (bonding) manufacturing methods; however, an additive method can simplify an assembly by consolidating individual components into one, and incorporating lattice structures and function optimised geometries to reduce the mass of components, which are beneficial to space-based instrumentation as mass and volume are constrained. Attention must be given to the printability of the design - build orientation and powder/resin removal from lattices and internal cavities are challenges when designing for AM. This paper will describe the design, manufacture and metrology of mirror prototypes from the Active Deployable Optical Telescope (ADOT) 6U CubeSat project. The AM mirror is 52mm in diameter, 10mm deep, with a convex 100mm radius of curvature reflective surface and deploys telescopically on three booms. The objectives of the designs were to combine the boom mounting features into the mirror and to lightweight both prototypes by 50% and 70% using internal, thin-walled lattices. Four final lattice designs were downselected through simulation and prototype validation. Prototypes were printed in the aluminium alloy AlSi10Mg using powder bed fusion and fused silica using stereolithography. Aluminium mirrors were single point diamond turned and had surface roughness measurements taken. Fused silica designs were adapted from the aluminium designs and have completed printing.

Particle sedimentation through porous media is limited by the inability of passive material to overcome surface interactions and a tortuous network of pores. This limits transport, delivery, and effectiveness of chemicals used as reactants, nutrients, pesticides, or for waste remediation. This work develops magnetically responsive microrollers that navigate the complex interstitial network of porous matter. Rather than arresting on the upward facing surfaces of the pores, particles can roll and fall further, increasing transport by orders of magnitude. This work directly investigates Janus microrollers, activated by a rotating magnetic field, rolling and sedimenting though an index-matched porous medium. The mechanism of enhanced transport is determined, and the material flux is primarily a function of microroller concentration, rotation rate, and magnetic field strength. This mechanism is most efficient using a minimum number of rotations spaced out periodically in time to reduce the required energy input to greatly enhance transport. This general mechanism of transport enhancement can be broadly applied in numerous applications because the particles delivered within the porous matrix may be comprised of a wide variety of functional materials.

Throughout human history, epidemics have been a constant presence. Understanding their dynamics is essential to predict scenarios and make substantiated decisions. Mathematical models are powerful tools to describe an epidemic behavior. Among the most used, the compartmental ones stand out, dividing population into classes with well-defined characteristics. One of the most known is the $SIR$ model, based on a set of differential equations describing the rates of change of three categories over time. These equations take into account parameters such as the disease transmission rate and the recovery rate, which both change over time. However, classical models use constant parameters and can not describe the behavior of a disease over long periods. In this work, it is proposed a $SIR$ model with time-varying transmission rate parameter with a method to estimate this parameter based on an optimization problem, which minimizes the sum of the squares of the errors between the model and historical data. Additionally, based on the infection rates determined by the algorithm, the model's ability to predict disease activity in future scenarios was also investigated. Epidemic data released by the government of the State of Rio Grande do Sul in Brazil was used to evaluate the models, where the models shown a very good forecasting ability, resulting in errors for predicting the total number of accumulated infected persons of 0.13% for 7 days ahead and 0.6% for 14 days ahead.

Coronal mass ejections (CMEs) are powerful drivers of space weather, with magnetic flux ropes (MFRs) widely regarded as their primary precursors. However, the variation in reconnection flux during the evolution of MFR during CME eruptions remains poorly understood. In this paper, we develop a realistic 3D magneto-hydrodynamic model using which we explore the temporal evolution of reconnection flux during the MFR evolution using both numerical simulations and observational data. Our initial coronal configuration features an isothermal atmosphere and a potential arcade magnetic field beneath which an MFR emerges at the lower boundary. As the MFR rises, we observe significant stretching and compression of the overlying magnetic field beneath it. Magnetic reconnection begins with the gradual formation of a current sheet, eventually culminating with the impulsive expulsion of the flux rope. We analyze the temporal evolution of reconnection fluxes during two successive MFR eruptions while continuously emerging the twisted flux rope through the lower boundary. We also conduct a similar analysis using observational data from the Helioseismic and Magnetic Imager (HMI) and the Atmospheric Imaging Assembly (AIA) for an eruptive event. Comparing our MHD simulation with observational data, we find that reconnection flux play a crucial role in determination of CME speeds. From the onset to the eruption, the reconnection flux shows a strong linear correlation with the velocity. This nearly realistic simulation of a solar eruption provides important insights into the complex dynamics of CME initiation and progression.

Habituation - a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld - is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available. The combination of these facts creates the need for efficient and computationally performant pattern recognition algorithms that will be able to run in parallel and possibly on other hardware, such as GPUs, given that these become more and more available in LHC experiments and high-performance computing centres. Line Segment Tracking (LST) is a novel such algorithm which has been developed to be fully parallelizable and hardware agnostic. The latter is achieved through the usage of the Alpaka library. The LST algorithm has been tested with the CMS central software as an external package and has been used in the context of the CMS HL-LHC High Level Trigger (HLT). When employing LST for pattern recognition in the HLT tracking, the physics and timing performances are shown to improve with respect to the ones utilizing the current pattern recognition algorithms. The latest results on the usage of the LST algorithm within the CMS HL-LHC HLT are presented, along with prospects for further improvements of the algorithm and its CMS central software integration.