Long range flight by fixed-wing aircraft without propulsion systems can be accomplished by "soaring" -- exploiting randomly located updrafts to gain altitude which is expended in gliding flight. As the location of updrafts is uncertain and cannot be determined except through in situ observation, aircraft exploiting this energy source are at risk of failing to find a subsequent updraft. Determining when an updraft must be exploited to continue flight is essential to managing risk and optimizing speed. Graph percolation offers a theoretical explanation for this risk, and a framework for evaluating it using information available to the operator of a soaring aircraft in flight. The utility of graph percolation as a risk measure is examined by analyzing flight logs from human soaring pilots. This analysis indicates that in sport soaring pilots rarely operate in a condition which does not satisfy graph percolation, identifies an apparent desired minimum node degree, and shows that pilots accept reduced climb rates in order to maintain percolation.

The MORA experimental setup is designed to measure the triple-correlation D parameter in nuclear beta decay. The D coefficient is sensitive to possible violations of time-reversal invariance. The experimental configuration consists of a transparent Paul trap surrounded by a detection setup with alternating beta and recoil-ion detectors. The octagonal symmetry of the detection setup optimizes the sensitivity of positron-recoil-ion coincidence rates to the D correlation, while reducing systematic effects. MORA utilizes an innovative in-trap laser polarization technique. The design and performance of the ion trap, associated beamline elements, lasers and beta and recoil-ion detectors, are presented. Recent progress towards the polarization proof-of-principle is described.

This work frames the first three publications around the development of the Flight Physics Concept Inventory (FliP-CoIn), and elaborates on many aspects in more detail. FliP-CoIn is a multiple-choice conceptual assessment instrument for improving fluid dynamics learning and teaching. I give insights into why and how FliP-CoIn was developed and how it is best used for improving conceptual learning. Further, this work presents evidence for several dimensions of FliP-CoIn's validity and reliability. Finally, I discuss key insights from the development process, the data analysis, and give recommendations for future research. This is a pre print version of the following book: Florian Genz, The Flight Physics Concept Inventory, 2025, Springer Spektrum, published with permission of Springer Fachmedien Wiesbaden GmbH. The final authenticated version is available online at: this http URL and https://link.springer.com/book/9783658475147

In 1950, Chien-Shiung Wu and her student published a coincidence experiment on entangled photon pairs that were created in electron-positron annihilation. This experiment precisely verified the prediction of quantum electrodynamics. Additionally, it was also the first instance of a precisely controlled quantum entangled state of spatially separated particles, although Wu did not know about this at the time. In 1956, Wu initiated and led the so-called Wu experiment, which discovered parity nonconservation, becoming one of the greatest experiments of the 20th century. As Chen Ning Yang said, Wu's experiments were well known for their precision and accuracy. Experimental precision and accuracy manifested Wu's scientific spirit, which we investigate here in some detail. This paper is the translated transcript of the speech the author made at the International Symposium Commemorating the 110th Anniversary of the Birth of Chien-Shiung Wu, on May 31, 2022. The above abstract is the translation of the original abstract of the speech.

Applications such as augmented and virtual reality (AR/VR), optical atomic clocks, and quantum computing require photonic integration of (near-)visible laser sources to enable commercialization at scale. The heterogeneous integration of III-V optical gain materials with low-loss silicon nitride waveguides enables complex photonic circuits with low-noise lasers on a single chip. Previous such demonstrations are mostly geared towards telecommunication wavelengths. At shorter wavelengths, limited options exist for efficient light coupling between III-V and silicon nitride waveguides. Recent advances in wafer-bonded devices at these wavelengths require complex coupling structures and suffer from poor heat dissipation. Here, we overcome these challenges and demonstrate a wafer-scale micro-transfer printing method integrating functional III-V devices directly onto the silicon substrate of a commercial silicon nitride platform. We show butt-coupling of efficient GaAs-based amplifiers operating at 800 nm with integrated saturable absorbers to silicon nitride cavities. This resulted in extended-cavity continuous-wave and mode-locked lasers generating pulse trains with repetition rates ranging from 3.2 to 9.2 GHz and excellent passive stability with a fundamental radio-frequency linewidth of 519 Hz. These results show the potential to build complex, high-performance fully-integrated laser systems at 800 nm using scalable manufacturing, promising advances for AR/VR, nonlinear photonics, timekeeping, quantum computing, and beyond.

Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional inverse-design approaches--whether driven by human intuition, global optimization, or adjoint-based gradient methods--often involve intricate binarization and filtering steps, while recent deep learning strategies demand prohibitively large numbers of simulations (10^5 to 10^6). To overcome these limitations, we present AdjointDiffusion, a physics-guided framework that integrates adjoint sensitivity gradients into the sampling process of diffusion models. AdjointDiffusion begins by training a diffusion network on a synthetic, fabrication-aware dataset of binary masks. During inference, we compute the adjoint gradient of a candidate structure and inject this physics-based guidance at each denoising step, steering the generative process toward high figure-of-merit (FoM) solutions without additional post-processing. We demonstrate our method on two canonical photonic design problems--a bent waveguide and a CMOS image sensor color router--and show that our method consistently outperforms state-of-the-art nonlinear optimizers (such as MMA and SLSQP) in both efficiency and manufacturability, while using orders of magnitude fewer simulations (approximately 2 x 10^2) than pure deep learning approaches (approximately 10^5 to 10^6). By eliminating complex binarization schedules and minimizing simulation overhead, AdjointDiffusion offers a streamlined, simulation-efficient, and fabrication-aware pipeline for next-generation photonic device design. Our open-source implementation is available at https://github.com/dongjin-seo2020/AdjointDiffusion.

Criminal activities are predominantly due to males, with females exhibiting a significantly lower involvement, especially in serious offenses. This pattern extends to organized crime, where females are often perceived as less tolerant to illegal practices. However, the roles of males and females within corruption networks are less understood. Here, we analyze data from political scandals in Brazil and Spain to shed light on gender differences in corruption networks. Our findings reveal that females constitute 10% and 20% of all agents in the Brazilian and Spanish corruption networks, respectively, with these proportions remaining stable over time and across different scandal sizes. Despite this disparity in representation, centrality measures are comparable between genders, except among highly central individuals, for which males are further overrepresented. Additionally, gender has no significant impact on network resilience, whether through random dismantling or targeted attacks on the largest component. Males are more likely to be involved in multiple scandals than females, and scandals predominantly involving females are rare, though these differences are explained by a null network model in which gender is randomly assigned while maintaining gender proportions. Our results further reveal that the underrepresentation of females partially explains gender homophily in network associations, although in the Spanish network, male-to-male connections exceed expectations derived from a null model.

Quantum and molecular mechanics based electronic energy studies of weak H-bonded ammonium dimer show distinctive feature in energy profile when computed by different QM methods contrast to MM methods. MM based MMFF and SYBYL methods show smoothly varying dihedral energy profile for torsion angle variation around weak N1-H5 held by H-bond strength of around 13 KJ/mol. All the QM based methods HF, B3LYP and MP2 show noisy and unstable torsion dependent electronic energy profile for H-bonded ammonium dimer. Exploring energy surface beyond bond length shows singularities and discontinuities. QM-based computation of dipole moment shows several discreet values with jumps and discontinuities with torsion angle variation for ammonium dimer. Also repeated computations and reverse torsion energy profile show persistent singularity feature observed in all standard QM techniques. KEY WORDS: ammonium dimer, H-bond, quantum signature, anisotropic energy singularities

Computational physics is integrated throughout the current undergraduate physics curriculum, though there are surprisingly few resources for computational physics in the advanced lab courses. This is despite the fact that a comparison of numerical simulations to experimental results is common practice in modern physics research. In this paper we present a simple experiment in thermal diffusion in metal rods. An analytical solution exists for the transient heat conduction in an infinite rod with a delta function heat input, but no analytical solution exists for short rods or for long duration heat inputs. Our apparatus is a copper rod with a heater and thermometers attached to the rod. The temperature difference on the metal rods due to transient heat conduction can be modeled using a simple numerical simulation using the finite centered difference method. Using a 22 cm long copper rod with the ends thermally sunk in aluminum blocks, we show poor agreement between the experimental results and the infinite-rod analytical model, but excellent agreement between the experimental results and our numerical simulation. Repeating the experiment with only one end of the rod sunk into an aluminum block (the other floating), we get good qualitative agreement between the experimental results and the numerical model. This experiment shows the power of a numerical simulation but also the limitations of the chosen model, which can be used as motivation for further exploration.

This work presents new Gaussian single- and double-zeta basis sets optimized for stochastic density functional theory (sDFT) using real-space auxiliary grids. Previous studies showed standard basis sets like STO-3G and 6-31G are sub-optimal for this approach. Our basis-set's Gaussian-type orbitals (GTOs) resemble norm-conserving pseudo-orbitals for H, C, N, O, F, and Si, but minimize real-space and momentum-space support. These basis sets achieve accuracy comparable to established sets while offering improved efficiency for sDFT calculations with auxiliary grids.

Physical risks, such as droughts, floods, rising temperatures, earthquakes, infrastructure failures, and geopolitical conflicts, can ripple through global supply chains, raising costs, and constraining production across industries. Assessing these risks requires understanding not only their immediate effects, but also their cascading impacts. For example, a localized drought can disrupt the supply of critical raw materials such as cobalt or copper, affecting battery and electric vehicle production. Similarly, regional conflicts can impede cross-border trade, leading to broader economic consequences. Building on an existing model of simultaneous supply and demand shocks, we introduce a new propagation algorithm, Priority with Constraint, which modifies standard priority-based rationing by incorporating a minimum supply guarantee for all customers, regardless of their size or priority ranking. We also identify a buffer effect inherent in the Industry Proportional algorithm, which reflects real-world economic resilience. Finally, we extend the static shock propagation model to incorporate dynamic processes. We introduce mechanisms for gradual shock propagation, reflecting demand stickiness and the potential buffering role of inventories, and gradual recovery, modeling the simultaneous recovery of supply capacity and the inherent tendency for demand to return to pre-shock levels. Simulations demonstrate how the interplay between demand adjustment speed and supply recovery speed significantly influences the severity and duration of the economic impact after a shock.

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about XANES spectra structure. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15-20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method's effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments reducing the common errors of under- or over-sampling points in near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.

This study focuses on the development of reinforcement learning based techniques for the design of microelectronic components under multiphysics constraints. While traditional design approaches based on global optimization approaches are effective when dealing with a small number of design parameters, as the complexity of the solution space and of the constraints increases different techniques are needed. This is an important reason that makes the design and optimization of microelectronic components (characterized by large solution space and multiphysics constraints) very challenging for traditional methods. By taking as prototypical elements an application-specific integrated circuit (ASIC) and a heterogeneously integrated (HI) interposer, we develop and numerically test an optimization framework based on reinforcement learning (RL). More specifically, we consider the optimization of the bonded interconnect geometry for an ASIC chip as well as the placement of components on a HI interposer while satisfying thermoelastic and design constraints. This placement problem is particularly interesting because it features a high-dimensional solution space.

We demonstrate engineering of a narrow microwave transition between trappable states in radio-frequency-dressed $^{87}$rubidium, reducing the static field dependence. A single-frequency, off-resonant microwave field allows for the suppression of the differential Zeeman shift arising from the nuclear magnetic moment to at least first order. The field dependence can be suppressed further with additional dressing fields, which we demonstrate experimentally with two microwave frequencies. The engineered transition can thus be used in a range of cold atom schemes that rely on coherent state superpositions.

We have developed a physics-informed transformer model to suggest design parameters in wavelength-filtering ring resonator, that suit a given pair of resonant wavelengths with <6 nm errors. The model provides a versatile method for rapid and accurate design of resonators corresponding to various resonant wavelengths.

Depolarization of circularly polarized light due to multiple scattering in turbid media provides size distributions of scatterer. Applied it to the biological tissues as turbid media, scatters correspond to cell nuclei, which is abnormally grown in cancerous tissues. Therefore, the invasion depth of early-staged cancer can be estimated by comparisons of circular polarization of scattered light. In this study, we fabricated the optical phantoms made of resin and polystyrene beads to verify this technique by systematic experiments. The single-layered phantoms containing only one size of beads exhibits uniform monodispersed scattering media. Polarization images taken by a circular polarization imaging camera show systematically changes to the numerical density of scatters. Healthy and cancerous tissue phantoms exhibiting the lengths of mean free paths close to actual biological tissues were stacked to be bi-layered phantoms which imitates early-staged cancers. The averaged circular polarization values obtained from the images captured with the polarization camera are indicative of obvious changes depending on the thickness of cancerous layer of phantoms.

Background: The intensity of the electric field applied to a brain tumor by electric field therapy is influenced by the position of the electrode array, which should be optimized based on the patient's head shape and tumor characteristics. This study assessed the effects of varying electrode positions on electric field intensity in glioblastoma multiforme (GBM) patients. Methods: This study enrolled 13 GBM patients. The center of the MR slice corresponding to the center of the tumor was set as the reference point for the electrodes, creating pairs of electrode arrays in the top-rear and left-right positions. Based on this reference plan, four additional treatment plans were generated by rotating three of the four electrode arrays, all except the top electrode array, by 15$^\circ$ and 30$^\circ$ from their reference positions, resulting in a total of five treatment plans per patient. Electric field frequency was set at 200 kHz, and current density at 31 mArms/cm$^2$. The minimum and mean electric field intensities, homogeneity index (HI), and coverage index (CovI) were calculated and compared. Results: The optimal plans showed differences ranging from-0.39% to 24.20% for minimum intensity and -14.29% to 16.67% for mean intensity compared to reference plans. HI and CovI varied from 0.00% to 48.65% and 0.00% to 95.3%, respectively. The average improvements across all patients were 8.96% for minimum intensity, 5.11% for mean intensity, 15.65% for HI, and 17.84% for CovI. Conclusions: Optimizing electrode angle improves electric field therapy outcomes in GBM patients by maximizing field intensity and coverage. Keywords: electric field therapy; glioblastoma multiforme (GBM); treatment planning system (TPS); electrode array position; tumor coverage

Thermoacoustic instability (TAI) is a pressing problem in rocket combustors. TAI can cause significant damage to a combustor, resulting in mission failure. Therefore, stability analysis is crucial during the design and development phases of a rocket combustor. Stability analysis during the design phase can be substantially aided by the rocket combustor's large eddy simulation (LES). However, the computational cost of LES for full-scale rocket combustors is high. Therefore, using a small set of data from a large eddy simulation of a multi-element full-scale combustor, we investigated the effectiveness and computational needs of many data-driven and physics-driven tools for the classification of the stable and unstable regimes in the current study. Recurrence network analysis (RNA), reservoir computing (RC), and multi-scale permutation entropy (MPEA) analysis are the instruments employed in this study. The regime categorization task is unsuitable for RNA and MPEA, according to the results. With little input data, RC-based metrics may map the stable and unstable regimes and are thought to be computationally inexpensive and straightforward to use. In order to help with the design and development of rocket combustors, the combined LES-RC method to stability analysis is therefore anticipated to result in a notable decrease in processing needs.

In plasmas, bremsstrahlung includes electron-ion (e-i) bremsstrahlung and electron-electron (e-e) bremsstrahlung. Bremsstrahlung radiation power loss is one of the most significant losses in fusion plasmas, which is more pronounced in higher temperature fusion. The factors that affect bremsstrahlung power include the mean electron energy and the electron velocity distribution shape. In this study, we systematically study the influence of the electron velocity distribution shape on the bremsstrahlung power with fixed total electron energy. It was found that the existing electron velocity distribution shapes have little effect on the bremsstrahlung power. In addition, by analyzing the bounds of bremsstrahlung power, we have provided the theoretical upper and lower bounds of e-i radiation. Our analysis reveals that the e-i bremsstrahlung power depends critically on the degree of energy distribution concentration. Specifically, in non-relativistic regimes, concentrated energy distributions enhance the radiation power, whereas in high-temperature relativistic regimes, such concentration suppresses it. This discrepancy arises from the distinct contributions of high-energy electron populations to radiation power across different energy regimes. For e-e bremsstrahlung, a similar dependence on energy concentration is observed. Furthermore, e-e radiation power exhibits additional sensitivity to the anisotropy of the electron velocity distribution function. These rules could provide a basis for reducing bremsstrahlung power losses in fusion plasmas.

The SABRE Experiment is a direct detection dark matter experiment using a target composed of multiple NaI(Tl) crystals. The experiment aims to be an independent check of the DAMA/LIBRA results with a detector in the Northern (Laboratori Nazionali Del Gran Sasso, LNGS) and Southern (Stawell Underground Physics Laboratory, SUPL) hemispheres. The SABRE South photomultiplier tubes (PMTs) will be used near the low energy noise threshold and require a detailed calibration of their performance and contributions to the background in the NaI(Tl) dark matter search, prior to installation. We present the development of the pre-calibration procedures for the R11065-20 Hamamatsu PMTs. These PMTs are directly coupled to the NaI(Tl) crystals within the SABRE South experiment. In this paper we present methodologies to characterise the gain, dark rate, and timing properties of the PMTs. We develop a method for in-situ calibration without a light injection source. Additionally we explore the application of machine learning techniques using a Boosted Decision Tree (BDT) trained on the response of single PMTs to understand the information available for background rejection. Finally, we briefly present the simulation tool used to generate digitised PMT data from optical Monte Carlo simulations.

InAs/GaAs quantum dot (QD) distributed feedback (DFB) lasers are promising candidates for next-generation photonic integrated circuits. We present a design that incorporates an oxidized aperture confined surface grating (OASG) structure, which reduces non-radiative recombination losses and surface optical losses sustained in device fabricated by conventionally fabrication methods including etching and regrowth. The OASG-DFB laser eliminates the need for ridge waveguide etching and avoids instability in sidewall grating coupling. Experimental results show stable single-mode operation, a maximum output power of 15.1 mW, a side-mode suppression ratio (SMSR) of 44 dB, and a narrow linewidth of 1.79 MHz. This approach simplifies fabrication, reduces costs, and enhances the scalability of GaAs-based QD DFB lasers for applications in optical communication and photonic integration.

Dynamic and precise measurement of cerebral blood flow velocity is crucial in neuroscience and the diagnosis of cerebrovascular diseases. Traditional color Doppler ultrasound can only measure the velocity component along the ultrasound beam, which restricts its ability to accurately capture the complete blood flow vector in complex environments. To overcome these limitations, we propose an ultrafast pulse-coded vector Doppler (PC-UVD) imaging method, utilizing Hadamard matrix-based pulse encoding to improve velocity estimation accuracy under low signal-to-noise ratio (SNR) conditions. Our study encompasses spiral flow simulations and in vivo rat brain experiments, showing significantly enhanced measurement precision compared to conventional ultrafast vector Doppler (UVD). This innovative approach enables the measurement of dynamic cerebral blood flow velocity within a single cardiac cycle, offering insights into the characteristics of cerebrovascular resistivity. The proposed PC-UVD method employs Hadamard matrix encoding of plane waves, boosting SNR without compromising temporal or spatial resolution. Velocity vectors are subsequently estimated using a weighted least squares (WLS) approach, with iterative residual-based weight optimization improving robustness to noise and minimizing the impact of outliers. The effectiveness of this technique is confirmed through simulations with a spiral blood flow phantom, demonstrating a marked improvement in velocity estimation accuracy, particularly in deep imaging regions with significant signal attenuation. In vivo experiments on rat brains further confirm that the proposed method offers greater accuracy than existing UVD approaches, particularly for small vessels. Notably, our method can precisely differentiate arterial from venous flow by analyzing pulsatility and resistivity within the cerebral vascular network.

An isolated charge-neutral drop suspended in an unbounded medium does not migrate in a uniform DC electric field. A nearby wall breaks the symmetry and causes the drop to drift towards or away from the boundary, depending on the electric properties of the fluids and the wall. In the case of an electrically insulating wall and an electric field applied tangentially to the wall, the interaction of the drop with its electrostatic image gives rise to repulsion by the wall. However, the electrohydrodynamic flow causes either repulsion for a drop with $\mathrm{R/P}<1$, where $\mathrm{R}$ and $\mathrm{P}$ are the drop-to-medium ratios of conductivity and permittivity, respectively, or attraction for $\mathrm{R/P}>1$. We experimentally measure droplet trajectories and quantify the wall-induced electrohydrodynamic lift in the case $\mathrm{R/P}<1$. Numerical simulations using the boundary integral method agree well with the experiment and also explore the $\mathrm{R/P}>1$ case. The results show that the lateral migration of a drop in a uniform electric field applied parallel to an insulating wall is dominated by the long-range flow due to the image stresslet.

Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture for the reconstruction of the pulse arrival time and amplitude in individual scintillating crystals in electromagnetic calorimeters and other detectors. The network performance is discussed as well as the application of xAI methods for further investigation of the algorithm and improvement of the output accuracy.

Statistical properties of the pair dispersion of Lagrangian particles (tracers) in incompressible turbulent flows provide insights into transport and mixing. We explore the same in transonic to supersonic compressible turbulence of an isothermal ideal gas in two dimensions, driven by large-scale solenoidal and irrotational stirring forces, via direct numerical simulations. We find that the scaling exponents of the order-$p$ negative moments of the distribution of exit times -- in particular, the doubling and halving times of pair separations -- are nonlinear functions of $p$. Furthermore, the doubling and halving time statistics are different. The halving-time exponents are universal -- they satisfy their multifractal model-based prediction, irrespective of the nature of the stirring. However, the doubling-time exponents are not. In the solenoidally-stirred flows, the doubling time exponents can be expressed solely in terms of the multifractal scaling exponents obtained from the structure functions of the solenoidal component of the velocity. Moreover, they depend strongly on the Mach number, $\Ma$, as elongated patches of high vorticity emerge along shock fronts at high $\Ma$. In contrast, in the irrotationally-stirred flows, the doubling-time exponents do not satisfy any known multifractal model-based relation, and are independent of $\Ma$. Our findings are of potential relevance to astrophysical disks and molecular clouds wherein turbulent transport and mixing of gases often govern chemical kinetics and the rates of formation of stars and planetesimals.

In this study, we develop a Diffusion Transformer (referred as to DiT1D) for synthesizing realistic earthquake time histories. The DiT1D generates realistic broadband accelerograms (0-30 Hz resolution), constrained at low frequency by 3-dimensional (3D) elastodynamics numerical simulations, ensuring the fulfillment of the minimum observable physics. The DiT1D architecture, successfully adopted in super-resolution image generation, is trained on recorded single-station 3-components (3C) accelerograms. Thanks to Multi-Head Cross-Attention (MHCA) layers, we guide the DiT1D inference by enforcing the low-frequency part of the accelerogram spectrum into it. The DiT1D learns the low-to-high frequency map from the recorded accelerograms, duly normalized, and successfully transfer it to synthetic time histories. The latter are low-frequency by nature, because of the lack of knowledge on the underground structure of the Earth, demanded to fully calibrate the numerical model. We developed a CNN-LSTM lightweight network in conjunction with the DiT1D, so to predict the peak amplitude of the broadband signal from its low-pass-filtered counterpart, and rescale the normalized accelerograms rendered by the DiT1D. Despite the DiT1D being agnostic to any earthquake event peculiarities (magnitude, site conditions, etc.), it showcases remarkable zero-shot prediction realism when applied to the output of validated earthquake simulations. The generated time histories are viable input accelerograms for earthquake-resistant structural design and the pre-trained DiT1D holds a huge potential to integrate full-scale fault-to-structure digital twins of earthquake-prone regions.

Biocompatible vaterite microspheres, renowned for their porous structure, are promising carriers for magnetic nanoparticles (MNPs) in biomedical applications such as targeted drug delivery and diagnostic imaging. Precise control over the magnetic moment of individual microspheres is crucial for these applications. This study employs widefield quantum diamond microscopy to map the stray magnetic fields of individual vaterite microspheres (3-10 um) loaded with Fe3O4 MNPs of varying sizes (5 nm, 10 nm, and 20 nm). By analyzing over 35 microspheres under a 222 mT external magnetizing field, we measured peak-to-peak stray field amplitudes of 41 uT for 5 nm and 10 nm superparamagnetic MNPs, reflecting their comparable magnetic response, and 12 uT for 20 nm ferrimagnetic MNPs, due to distinct magnetization behavior. Finite-element simulations confirm variations in MNP distribution and magnetization uniformity within the vaterite matrix, with each microsphere encapsulating thousands of MNPs to generate its magnetization. This high-resolution magnetic imaging approach yields critical insights into MNP-loaded vaterite, enabling optimized synthesis and magnetically controlled systems for precision therapies and diagnostics.

Nuclear shielding factor is an important quantity to describe the response of an atom under the perturbation of an external field. In this work, we develop the sum-over-states numerical method and the Hylleraas variational perturbation approximation to calculate the multipole nuclear shielding factors for general one-electron systems and apply them to the model of the hydrogen atom confined by a spherical cavity. The generalized pseudospectral method is employed to solve the eigenstates of the unperturbed atom. The obtained dipole nuclear shielding factors are in good agreement with previous calculations and the higher-pole results are reported for the first time. The asymptotic behaviors of the multipole nuclear shielding factors in both the large- and small-confinement limits are analyzed with the assistance of variational perturbation theory. The free-atom values can be exactly reproduced by the second-order perturbation approximation and all multipole nuclear shielding factors in the small-confinement limit tend to zero by a linear law. The variational perturbation method manifests exponential convergence with increasing the order of approximation. The numerical and approximate methods developed in this work together pave the way for further investigation of the multipole nuclear shielding factors for general atomic systems.

This study proposes a novel design methodology for neutron beam shutters that integrates Monte Carlo simulations (MCNP) with machine learning techniques to enhance shielding performance and accelerate the design process. The target facility is a compact neutron science platform where neutrons are produced by proton beams from a cyclotron striking a neutron production target. The system includes both thermal and fast neutron beamlines. A beam shutter is installed on the thermal neutron line to reduce occupational radiation exposure during maintenance activities. In this work, 200 neutron shutter configurations with varying material sequences were simulated using MCNP. The resulting dataset was used to train a fully connected neural network to predict the neutron flux downstream of the shielding. The trained model was subsequently applied to 1,000 randomly generated shielding configurations for rapid flux prediction and performance ranking. The 20 designs with the lowest predicted flux were selected and further validated via MCNP simulations. Results show that the optimal design reduces the neutron flux from 5.61 x 10^9 n/cm2*s at the shutter entrance to 4.96 x 10^5 n/cm2*s at the exit, achieving a reduction of four orders of magnitude. These findings confirm that the integration of machine learning techniques can effectively reduce simulation costs and assist in identifying high-performance shielding configurations, demonstrating the strong potential of data driven approaches in neutron system design.

We present Dargana, a fine-tuned variant of the EarthPT time-series foundation model that achieves specialisation using <3% of its pre-training data volume and 5% of its pre-training compute. Dargana is fine-tuned to generate regularly updated classification of tree canopy cover at 10m resolution, distinguishing conifer and broadleaved tree types. Using Cornwall, UK, as a test case, the model achieves a pixel-level ROC-AUC of 0.98 and a PR-AUC of 0.83 on unseen satellite imagery. Dargana can identify fine structures like hedgerows and coppice below the training sample limit, and can track temporal changes to canopy cover such as new woodland establishment. Our results demonstrate how pre-trained Large Observation Models like EarthPT can be specialised for granular, dynamic land cover monitoring from space, providing a valuable, scalable tool for natural capital management and conservation.

We employ the two-band density-matrix equations to calculate high-order harmonic generation (HHG) and its ellipticity in gapped graphene irradiated by a femtosecond short-pulse laser under different orientation angles. The orientation-dependent harmonic spectra show obvious enhancement harmonics. We also focus on the ellipticity of these enhanced harmonics. Utilizing the recombination trajectory model, the enhanced harmonics are attributed to the caustic effect, whose orientation dependence originates from the inequivalence of $\textrm{K}$ points during the electron ionization process for different orientation angles. In addition, the harmonic ellipticity can be well understood by the phase difference of saddle-point currents. Based on our theory, we design a two-color field scheme to generate elliptically polarized attosecond pulses in gapped graphene. This work may shed light on the generation of elliptically polarized attosecond pulses in two-dimensional materials.

Continuous glucose monitoring (CGM) is vital for diabetes care, but current systems rely on invasive implants or electrochemical sensors that often cause discomfort and skin irritation. Non-invasive alternatives remain limited by low sensitivity and poor compatibility with complex sweat environments, highlighting the urgent need for a comfortable and reliable solution. Here, we report the development of a wearable optical sensor watch that integrates surface plasmon resonance (SPR) technology with a functionalized silver-coated silicon nanowire (Ag/SiNW) substrate for real-time, non-invasive glucose monitoring in sweat. The nanostructured sensor is functionalized with 4-mercaptophenylboronic acid (4-MPBA), enabling selective glucose capture and optical signal transduction through both Raman scattering and SPR shift. The dual-mode detection strategy was systematically optimized, and a miniaturized SPR system operating at 638 nm was successfully integrated into a wearable watch format with wireless data transmission to a mobile application. This wearable device demonstrated excellent sensitivity (LOD down to 0.12 mM) and high selectivity in detecting glucose within physiological sweat concentration ranges. Human subject trials confirmed its applicability in real-life scenarios. This study offers a promising non-invasive alternative to traditional CGM and highlights the potential of integrating nanophotonic sensors with wearable platforms for continuous health monitoring and personalized medicine.

Direct economic impacts of flooding are essential for flood mitigation policies, and are based upon multiple tools, among which 2D hydrodynamic models. These models are dependent on multiple parameters and data, such as topography, and can yield considerably different results depending on their values, ultimately changing the resultant damage estimation. To help in understanding some of the underlying drivers of this variation, this conference paper compares damage estimations issued by two different hydrodynamic models of the Garonne River, near Marmande, in France. The influence of a topo-bathymetry projection method, which aims at reducing some interpolation incoherencies, was also studied. The general conclusion is that these two factors, i.e., a change in the model parameters and the topo-bathymetry projection method, do matter but their influence on the total cost estimation is highly dependent on the description of a few high-vulnerability agricultural fields. Indeed, the latter often account for the majority of the total damage even if occupying a fraction of the total area. This paper then advocates for a more vulnerability-based modelling approach, with a focus on the few high-vulnerability areas.

Cell volume is controlled by osmotic regulation of the fluid content, via water efflux through the cell membrane. The rate of this process is controlled by the ability of the liquid to move through the meshwork of solid elements within the cell. While such dynamics have been interpreted in the frame of the poroelastic theory in the cytoplasm, the behavior of the nucleus remains unknown due to a lack of technique to probe it. Brillouin light scattering (BLS) allows to interrogate the sound velocity and attenuation of a sample in a non-contact manner, thus revealing the dynamic response of the material. In cells, such data were initially interpreted as the viscoelastic response of the actin meshwork, but later studies pointed out the importance of water content. To resolve this lack of consensus in the interpretation of the hypersonic data obtained from BLS spectra, and investigate the possible poroelastic nature of the nucleus, we vary the relative volume fraction of intracellular water and solid network by applying osmotic compressions to single cells. In the nucleus, we observe a non-linear increase in the sound velocity and attenuation with increasing osmotic pressure that we fit to a poroelastic model, providing an estimate of the friction coefficient between the water phase and the network. By comparing BLS data to volume measurements, our approach demonstrates clearly that Brillouin microscopy actually provides a measure of molecular concentration in living cells

The paper deals with the passive control of resonant systems using nonlinear energy sink (NES). The objective is to highlight the benefits of adding nonlinear geometrical damping in addition to the cubic stiffness nonlinearity. The behaviour of the system is investigated theoretically by using the mixed harmonic balance multiple scales method. Based on the obtained slow flow equations, a design procedure that maximizes the dynamic range of the NES is presented. Singularity theory is used to express conditions for the birth of detached resonance cure independently of the forcing frequency. It is shown that the presence of a detached resonance curve is not necessarily detrimental to the performance of the NES. Moreover, the detached resonance curve can be completely suppressed by adding nonlinear damping. The results of the design procedure are then compared to numerical simulations.

Under-display camera (UDC) systems enable full-screen displays in smartphones by embedding the camera beneath the display panel, eliminating the need for notches or punch holes. However, the periodic pixel structures of display panels introduce significant optical diffraction effects, leading to imaging artifacts and degraded visual quality. Conventional approaches to mitigate these distortions, such as deep learning-based image reconstruction, are often computationally expensive and unsuitable for real-time applications in consumer electronics. This work introduces an inverse-designed metasurface for wavefront restoration, addressing diffraction-induced distortions without relying on external software processing. The proposed metasurface effectively suppresses higher-order diffraction modes caused by the metallic pixel structures, restores the optical wavefront, and enhances imaging quality across multiple wavelengths. By eliminating the need for software-based post-processing, our approach establishes a scalable, real-time optical solution for diffraction management in UDC systems. This advancement paves the way to achieve software-free real-time image restoration frameworks for many industrial applications.

Full waveform inversion (FWI) is a high-resolution subsurface imaging technique, but its effectiveness is limited by challenges such as noise contamination, sparse acquisition, and artifacts from multiparameter coupling. To address these limitations, this study develops a deep reparameterized FWI (DR-FWI) framework, in which subsurface parameters are represented by a deep neural network. Instead of directly optimizing the parameters, DR-FWI optimizes the network weights to reconstruct them, thereby embedding structural priors and facilitating optimization. To provide benchmark guidelines for the design of DR-FWI, we conduct a comparative analysis of three representative architectures (U-Net, CNN, MLP) combined with two initial model embedding strategies: one pretraining the network to generate predefined initial models (pretraining-based), while the other directly adds network outputs to the initial models. Extensive ablation experiments show that combining CNN with pretraining-based initialization significantly enhances inversion accuracy, offering valuable insights into network design. To further understand the mechanism of DR-FWI, spectral bias analysis reveals that the network first captures low-frequency features and gradually reconstructs high-frequency details, enabling an adaptive multi-scale inversion strategy. Notably, the robustness of DR-FWI is validated under various noise levels and sparse acquisition scenarios, where its strong performance with limited shots and receivers demonstrates reduced reliance on dense observational data. Additionally, a backbone-branch structure is proposed to extend DR-FWI to multiparameter inversion, and its efficacy in mitigating cross-parameter interference is validated on a synthetic anomaly model and the Marmousi2 model. These results suggest a promising direction for joint inversion involving multiple parameters or multiphysics.

The acoustofluidic method holds great promise for manipulating microorganisms. When exposed to the steady vortex structures of acoustic streaming flow, these microorganisms exhibit intriguing dynamic behaviors, such as hydrodynamic trapping and aggregation. To uncover the mechanisms behind these behaviors, we investigate the swimming dynamics of both passive and active particles within a two-dimensional acoustic streaming flow. By employing a theoretically calculated streaming flow field, we demonstrate the existence of stable bounded orbits for particles. Additionally, we introduce rotational diffusion and examine the distribution of particles under varying flow strengths. Our findings reveal that active particles can laterally migrate across streamlines and become trapped in stable bounded orbits closer to the vortex center, whereas passive particles are confined to movement along the streamlines. We emphasize the influence of the flow field on the distribution and trapping of active particles, identifying a flow configuration that maximizes their aggregation. These insights contribute to the manipulation of microswimmers and the development of innovative biological microfluidic chips.

Foundation models, as a mainstream technology in artificial intelligence, have demonstrated immense potential across various domains in recent years, particularly in handling complex tasks and multimodal data. In the field of geophysics, although the application of foundation models is gradually expanding, there is currently a lack of comprehensive reviews discussing the full workflow of integrating foundation models with geophysical data. To address this gap, this paper presents a complete framework that systematically explores the entire process of developing foundation models in conjunction with geophysical data. From data collection and preprocessing to model architecture selection, pre-training strategies, and model deployment, we provide a detailed analysis of the key techniques and methodologies at each stage. In particular, considering the diversity, complexity, and physical consistency constraints of geophysical data, we discuss targeted solutions to address these challenges. Furthermore, we discuss how to leverage the transfer learning capabilities of foundation models to reduce reliance on labeled data, enhance computational efficiency, and incorporate physical constraints into model training, thereby improving physical consistency and interpretability. Through a comprehensive summary and analysis of the current technological landscape, this paper not only fills the gap in the geophysics domain regarding a full-process review of foundation models but also offers valuable practical guidance for their application in geophysical data analysis, driving innovation and advancement in the field.

We consider a type of hypothetical compound materials in which its refractive index in spatial distribution meet $n(-x)=-n^{*}(x)$, belonging to anti-parity-time (APT) symmetric structures. Additionally, we demand balanced real positive- and negative- permeabilities with $\mu(-x)=-\mu(x)$. By introducing parametrization into APT symmetric transfer matrix, together with reciprocity theorem, we propose a generic parametric space to display its associated scattering results including symmetry phase, exceptional point, and symmetry broken phase. The outcome is irrespective of any system complexity, geometries, materials, and operating frequency. With the parametric space, we find that APT symmetric system not only enables coherent perfect absorption or lasing occurred at an exceptional point, but also realize a simultaneous coherent perfect absorption-lasing. Since APT-symmetric system is constructed by balanced positive and negative index materials, the phase accumulated from optical path length is null, resulting in an assignment of mode order lost. To verify our analysis, several designed heterostructures are demonstrated to support our findings.

Finding the initial depth-to-water table (DTWT) configuration of a catchment is a critical challenge when simulating the hydrological cycle with integrated models, significantly impacting simulation outcomes. Traditionally, this involves iterative spin-up computations, where the model runs under constant atmospheric settings until steady-state is achieved. These so-called model spin-ups are computationally expensive, often requiring many years of simulated time, particularly when the initial DTWT configuration is far from steady state. To accelerate the model spin-up process we developed HydroStartML, a machine learning emulator trained on steady-state DTWT configurations across the contiguous United States. HydroStartML predicts, based on available data like conductivity and surface slopes, a DTWT configuration of the respective watershed, which can be used as an initial DTWT. Our results show that initializing spin-up computations with HydroStartML predictions leads to faster convergence than with other initial configurations like spatially constant DTWTs. The emulator accurately predicts configurations close to steady state, even for terrain configurations not seen in training, and allows especially significant reductions in computational spin-up effort in regions with deep DTWTs. This work opens the door for hybrid approaches that blend machine learning and traditional simulation, enhancing predictive accuracy and efficiency in hydrology for improving water resource management and understanding complex environmental interactions.

Two novel methods, including the scanning envelope synthesis (SES) method and the active reflection self-cancellation (ARC) method, are proposed to design wide-angle scanning heterogeneous element phased arrays. Heterogeneous strategy is efficient to extend scanning range but quantitatively characterization of the effect is critically needed to guide design for achieving desired performance. The proposed SES method derives theoretically the relationship between scanning range and the 3dB-beamwidth of the pattern envelope of one phased array, which is linear superposition of active radiation pattern (AEP) magnitude of each element. Therefore, the contribution of each kind of heterogeneity can be quantitatively analyzed for further enhancing the scanning range. As we see, a high active reflection coefficient of the phased array can directly reduce the realized gain. In this way, one ARC method is proposed to reduce the active reflection coefficient by counteracting the reflection component of active reflection coefficient with its transmission component, thereby keeping the realized gain efficiently even when the array scans at large angels. For verification, one 24.5-29.5GHz 4x4 phased array scanning in E-plane is designed and fabricated. Benefiting from the proposed SES method, the scanning range of the prototype is extended up to $\pm74\deg$, around 10{\deg} improvement over one traditional heterogeneous array. Meanwhile, the active reflection coefficient is reduced from -4dB to lower than -7.5dB by applying the ARC method.

We expand on a recently introduced alternate framework for $GW$ simulation of charged excitations [Scott et. al., J. Chem. Phys., 158, 124102 (2023)], based around the conservation of directly computed spectral moments of the GW self-energy. Featuring a number of desirable formal properties over other implementations, we also detail efficiency improvements and a parallelism strategy, resulting in an implementation with a demonstrable similar scaling to an established Hartree--Fock code, with only an order of magnitude increase in cost. We also detail the applicability of a range of self-consistent $GW$ variants within this framework, including a scheme for full self-consistency of all dynamical variables, whilst avoiding the Matsubara axis or analytic continuation, allowing formal convergence at zero temperature. By investigating a range of self-consistency protocols over the GW100 molecular test set, we find that a little-explored self-consistent variant based around a simpler coupled chemical potential and Fock matrix optimization to be the most accurate self-consistent $GW$ approach. Additionally, we validate recently observed evidence that Tamm--Dancoff based screening approximations within $GW$ lead to higher accuracy than traditional random phase approximation screening over these molecular test cases. Finally, we consider the Chlorophyll A molecule, finding agreement with experiment within the experimental uncertainty, and a description of the full-frequency spectrum of charged excitations.

We perform a series of calculations using simulated QPUs, accelerated by NVIDIA CUDA-Q platform, focusing on a molecular analog of an amine-functionalized metal-organic framework (MOF) -- a promising class of materials for CO2 capture. The variational quantum eigensolver (VQE) technique is employed, utilizing the unitary coupled-cluster method with singles and doubles (UCCSD) within active spaces extracted from the larger material system. We explore active spaces of (6e,6o), (10e,10o), and (12e,12o), corresponding to 12, 20, and 24 qubits, respectively, and simulate them using CUDA-Q's GPU-accelerated state-vector simulator. Notably, the 24-qubit simulations -- among the largest of their kind to date -- are enabled by gate fusion optimizations available in CUDA-Q. While these active space sizes are among the largest reported in the context of CO2 chemisorption, they remain insufficient for a fully accurate study of the system. This limitation arises from necessary simplifications and scalability challenges of VQE, particularly the barren plateau problem. Nonetheless, this work demonstrates the application of VQE to a novel material system using large-scale simulated QPUs and offers a blueprint for future quantum chemistry calculations.

Access to high-energy particle beams is key for testing high-energy physics (HEP) instruments. Accelerators for cancer treatment can serve as such a testing ground. However, HEP instrument tests typically require particle fluxes significantly lower than for cancer treatment. Thus, facilities need adaptations to fulfill both the requirements for cancer treatment and the requirements for HEP instrument testing. We report on the progress made in developing a beam monitor with a sufficient dynamic range to allow for the detection of single particles, while still being able to act as a monitor at the clinical particle rates of the MedAustron treatment facility. The beam monitor is designed for integration into existing accelerators.

One hallmark of expertise in physics is the ability to translate between different representations of knowledge and use the representations that make the problem-solving process easier. In quantum mechanics, students learn about several ways to represent quantum states, e.g., as state vectors in Dirac notation and as wavefunctions in position and momentum representation. Many advanced students in upper-level undergraduate and graduate quantum mechanics courses have difficulty translating state vectors in Dirac notation to wavefunctions in the position or momentum representation and vice versa. They also struggle when translating the wavefunction between the position and momentum representations. The research presented here describes the difficulties that students have with these issues and how research was used as a guide in the development, validation, and evaluation of a Quantum Interactive Learning Tutorial (QuILT) to help students develop a functional understanding of these concepts. The QuILT strives to help students with different representations of quantum states as state vectors in Dirac notation and as wavefunctions in position and momentum representation and with translating between these representations. We discuss the effectiveness of the QuILT from in-class implementation and evaluation.

The stress integration of critical soil model is usually based on implicit Euler algorithm, where the stress predictor is corrected by employing a return mapping algorithm. In the case of large load step, the solution of local nonlinear system to compute the plastic multiplier may not be attained. To overcome this problem, a sub-stepping scheme shall be used to improve the convergence of the local nonlin- ear system solution strategy. Nevertheless, the complexity of the tangent operator of the sub-stepping scheme is high. This complicates the use of Newton-Raphson algorithm to obtain global quadratic convergence. In this paper, a formulation for consistent tangent operator is developed for implicit sub-stepping integration for the modified Cam-Clay model and unified Clay and Sand model. This formulation is highly efficient and can be used with problem involving large load step, such as tun- nel simulation.

This paper aims to resolve the incompatibility between two extant gauge-invariant accounts of the Abelian Higgs mechanism: the first account uses global gauge symmetry breaking, and the second eliminates spontaneous symmetry breaking entirely. We resolve this incompatibility by using the constrained Hamiltonian formalism in symplectic geometry. First we argue that, unlike their local counterparts, global gauge symmetries are physical. The symmetries that are spontaneously broken by the Higgs mechanism are then the global ones. Second, we explain how the dressing field method singles out the Coulomb gauge as a preferred gauge for a gauge-invariant account of the Abelian Higgs mechanism. Based on the existence of this group of global gauge symmetries that are physical, we resolve the incompatibility between the two accounts by arguing that the correct way to carry out the second method is to eliminate only the redundant gauge symmetries, i.e. those local gauge symmetries which are not global. We extend our analysis to quantum field theory, where we show that the Abelian Higgs mechanism can be understood as spontaneous global $U(1)$ symmetry breaking in the $C^*$-algebraic sense.

This study uses plasma-profile data from the EUROfusion pedestal database, focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematic predictions of the electron-temperature pedestal, using the density profiles and engineering parameters of the pulses as inputs. We first present a machine-learning algorithm that, given more inputs than theory-based modelling and 80\% of the database as training data, can reconstruct the remaining 20\% of temperature profiles within 20\% of the experimental values, including accurate estimates of the pedestal width and location. The most important engineering parameters for these predictions are magnetic field strength, particle fuelling rate, plasma current, and strike-point configuration. This confirms the potential of accurate pedestal prediction using large databases. Next, we take a simple theoretical approach assuming a local power-law relationship between the gradients of density ($R/L_{n_e}$) and temperature ($R/L_{T_e}$): $R/L_{T_e}=A\left(R/L_{n_e}\right)^\alpha$ with $\alpha\approx 0.4$ fits well in the steep-gradient region. When $A$ and $\alpha$ are fit independently for each pedestal, a one-to-one correlation emerges, also valid for JET-C data. For $\alpha = 1$, $A \equiv \eta_e$, a known control parameter for turbulence in slab-ETG theory. Measured values of $\eta_e$ in the steep-gradient region lie well above the slab-ETG stability threshold, suggesting a nonlinear threshold shift or a supercritical turbulent state. Finally, we test heat-flux scalings motivated by gyrokinetic simulations, and we provide best-fit parameters for reconstructing JET-ILW pedestals. These models require additional experimental inputs to reach the accuracy of the machine-learning reconstructions.

We describe a study focusing on students' and faculty members' reasoning about problems of differing cognitive complexity related to the double-slit experiment (DSE) with single particles. In the first phase of the study, students in advanced quantum mechanics courses were asked these questions in written form. Additionally, individual interviews were conducted with ten students in which they were asked follow-up questions to make their thought processes explicit on the challenging problems. Students did well on the straightforward problem, showing they had some knowledge of the DSE after traditional instruction, but they struggled on the more complex ones. Even if explicitly asked to do so in interviews, students were often uncomfortable performing calculations or making approximations and simplifications, instead preferring to stick with their gut feeling. In the second phase of the study, the problems were broken down into more pointed questions to investigate whether students had knowledge of relevant concepts, whether they would do calculations as part of their solution approach if explicitly asked, and whether they explicitly noted using their gut feeling. While the faculty members' responses suggest that they could seamlessly move between conceptual and quantitative reasoning, most students were unable to combine concepts represented by different equations to solve the problems quantitatively. We conclude with instructional implications.

Amplitude analysis serves as a pivotal tool in hadron spectroscopy, fundamentally involving a series of likelihood fits to multi-dimensional experimental distributions. While numerous robust goodness-of-fit tests are available for low-dimensional scenarios, evaluating goodness-of-fit in amplitude analysis poses significant challenges. In this work, we introduce a powerful goodness-of-fit test leveraging a machine-learning-based anomaly detection method.

Optical frequency combs have become a key research topic in optics and photonics. A peculiar comb state is the harmonic frequency comb (HFC), where optical lines are spaced by integer multiples of the cavity's free-spectral range. The spontaneous formation of HFCs has recently been observed in semiconductor lasers with fast gain recovery, such as Quantum Cascade Lasers (QCLs), although the underlying physical mechanism remains unclear. In this work, we provide a physical interpretation for the formation of HFCs in QCLs, based on a resonance phenomenon between an effective Rabi frequency and a mode of the laser cavity. This is corroborated by the results of the numerical integration of the effective semiconductor Maxwell-Bloch equations used to describe the multimode laser dynamics, as well as by the linear stability analysis of the continuous wave emission at threshold.

On-demand feeder bus services present an innovative solution to urban mobility challenges, yet their success depends on thorough assessment and strategic planning. Despite their potential, a comprehensive framework for evaluating feasibility and identifying suitable service areas remains underdeveloped. Simulation Framework for Feeder Location Evaluation (SimFLEX) uses spatial, demographic, and transport-specific data to run microsimulations and compute key performance indicators (KPIs), including service attractiveness, waiting time reduction, and added value. SimFLEX employs multiple replications to estimate demand and mode choices and integrates OpenTripPlanner (OTP) for public transport routing and ExMAS for calculating shared trip attributes and KPIs. For each demand scenario, we model the traveler learning process using the method of successive averages (MSA), stabilizing the system. After stabilization, we calculate KPIs for comparative and sensitivity analyzes. We applied SimFLEX to compare two remote urban areas in Krakow, Poland - Bronowice and Skotniki - the candidates for service launch. Our analysis revealed notable differences between analyzed areas: Skotniki exhibited higher service attractiveness (up to 30%) and added value (up to 7%), while Bronowice showed greater potential for reducing waiting times (by nearly 77%). To assess the reliability of our model output, we conducted a sensitivity analysis across a range of alternative-specific constants (ASC). The results consistently confirmed Skotniki as the superior candidate for service implementation. SimFLEX can be instrumental for policymakers to estimate new service performance in the considered area, publicly available and applicable to various use cases. It can integrate alternative models and approaches, making it a versatile tool for policymakers and urban planners to enhance urban mobility.

Bright sources of high-energy X-rays and electrons are indispensable tools in advanced imaging. Yet, current laser-driven systems typically support only single-modality imaging, require complex infrastructure, or operate at low repetition rates. Here, we demonstrate a compact, table-top laser-plasma source capable of generating synchronized electron and X-ray pulses at 1 kHz using just 2 mJ per pulse. A structured methanol droplet target enables quasi-single-shot electron radiographs and broadband, energy-resolved X-ray images, facilitating bi-modal imaging of both metallic and biological specimens. We achieve resolutions of 13.6 um for electrons and 21 um for X-rays, and demonstrate tomographic reconstruction using 35 projections. This compact platform rivals large-scale petawatt systems in resolution and brightness, while remaining scalable and accessible for high-throughput imaging in materials science and biomedicine.

While nearly single cycle THz pulse is conventionally accepted as the stimulus for the fastest and the most energy efficient control of spins in magnets, all-dielectric metasurfaces have been recently demonstrated to be the least dissipative mean to enhance and control the coupling of light to spins. All-dielectric metasurfaces for the THz control of spins hold great potential in the field of spintronics and related technologies, pushing the boundaries of speed and energy efficiency in spin-based information processing. Here we demonstrate such a metasurface for an advanced THz control of spins in a ferrimagnetic film of iron garnet. Structuring a nonmagnetic substrate one can force a THz electromagnetic field, otherwise described by plane waves, to acquire an out-of-plane magnetic field and thus enable arbitrary direction of the torque acting on spins in all three dimensions. Hence, metaphotonics opens up a plethora of opportunities for advanced control of spins at THz rates in many hot fields of contemporary science, including spintronics, magnonics and quantum computing.

Hollow-core fibers (HCFs) with claddings composed of silica glass capillaries have recently attracted a great deal of attention following the demonstration of optical loss levels lower than those of conventional telecommunication fibers. It is well established already that optical losses in HCFs are highly sensitive to both the wavelength and the geometry of the cladding capillaries. The underlying physical mechanisms behind reducing loss with the change of HCF design parameters while keeping the same fiber structure are not yet fully understood. In this work, we investigate the relationship between light localization and corresponding decrease of losses in HCFs and the distribution of reverse energy fluxes in air-core modes. We show here that the shape of the capillaries plays a crucial role in controlling radial energy backflows that influence light confinement and the energy leakage from air-core modes of HCFs. Through numerical modeling, we demonstrate that optimizing the capillary geometry to tailor the distribution of reverse radial energy fluxes leads to a substantial reduction in transmission losses even in fibers with relatively simple cladding structures. Consideration of the energy flows and observed occurrences of vortex of the Poynting vector allows us to a draw an interesting interdisciplinary analogy with the hydrodynamical system with suppressed backward flow - Tesla valve. We believe that combination of singular optics and energy fluxes analysis provides valuable physical insight into the mechanisms governing waveguiding in HCFs offering a pathway toward novel designs with minimized leakage loss.

As part of the collaboration building a set of detectors for the new collider, our group was tasked with designing and building a large-scale cosmic ray detector, which was to complement the capabilities of the MPD (Dubna) detec-tor set. The detector was planned as a trigger for cosmic ray particles and to be used to calibrate and test other systems. Additional functions were to be the detection of pairs of high-energy muons originating from some parti-cle decay processes generated during collisions and con-tinuous observation of the cosmic muon stream in order to detect multi muons events. From the very beginning, the detector was designed as a scalable and universal device for many applications. The following work will present the basic features and parameters of the Modular COsmic Ray Detector (MCORD) and examples of its possible use in high energy physics, astrophysics and geology. Thanks to its universal nature, MCORD can be potential used as a fast trigger, neutron veto detector, muon detector and as a tool in muon tomography.

We introduce a new mean-field equation for modeling Fabry-Perot resonators filled with a dispersive medium exhibiting both Brillouin and Kerr nonlinearities, e.g. an optical fiber. This model is derived from a unified framework that accounts for Brillouin scattering and four-wave mixing. It involves two coupled nonlinear Schrodinger equations for the forward and backward propagating fields, alongside a single equation governing the acoustic oscillation. Under standard assumptions for mean-field models -such as high finesse, weak nonlinearity, and weak dispersion- we demonstrate that our equation closely matches the original system. The simplified and elegant mathematical structure of our model provides valuable physical insights. As a key example, we derive an expression for the growth rate of harmonic perturbations of steady states. Additionally, our model facilitates fast and accurate numerical simulations using standard Fourier split-step methods. We highlight the effectiveness of this approach by simulating frequency comb generation in state-of-the-art high-Q fiber Fabry-Perot resonators.

Atmospheric blocking exerts a profound influence on mid-latitude circulation, yet its predictability remains elusive due to intrinsic non-linearities and sensitivity to initial-conditions. While blocking dynamics have been extensively studied, the impact of geographical positioning on predictability remains largely unexplored. This study provides a comparative assessment of the predictability of Western and Eastern North Pacific blocking events, leveraging analogue-based diagnostics applied to CMIP6 MIROC6 simulations. Blocking structures are identified using geopotential height gradient reversal, with their temporal evolution analysed through trajectory tracking and error growth metrics. Results reveal that Eastern blocks exhibit lower predictability, characterized by rapid error divergence and heightened mean logarithmic growth rates, whereas Western blocks display dynamical stability. Persistence analysis gives no significant difference between eastern and western North Pacific blocking events. Sensitivity analyses across varying detection thresholds validate the robustness of these findings.

When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and magnetic field strength, making disruptions one of the key challenges for future devices. Unfortunately, disruptions are not fully understood, with many different underlying causes that are difficult to anticipate. Data-driven models have shown success in predicting them, but they only provide limited interpretability. On the other hand, large-scale statistical analyses have been a great asset to understanding disruptive patterns. In this paper, we leverage data-driven methods to find an interpretable representation of the plasma state for disruption characterization. Specifically, we use a latent variable model to represent diagnostic measurements as a low-dimensional, latent representation. We build upon the Variational Autoencoder (VAE) framework, and extend it for (1) continuous projections of plasma trajectories; (2) a multimodal structure to separate operating regimes; and (3) separation with respect to disruptive regimes. Subsequently, we can identify continuous indicators for the disruption rate and the disruptivity based on statistical properties of measurement data. The proposed method is demonstrated using a dataset of approximately 1600 TCV discharges, selecting for flat-top disruptions or regular terminations. We evaluate the method with respect to (1) the identified disruption risk and its correlation with other plasma properties; (2) the ability to distinguish different types of disruptions; and (3) downstream analyses. For the latter, we conduct a demonstrative study on identifying parameters connected to disruptions using counterfactual-like analysis. Overall, the method can adequately identify distinct operating regimes characterized by varying proximity to disruptions in an interpretable manner.

Hierarchy significantly shapes interactions in social structures by organizing individuals or groups based on status, power, or privilege. This study investigates how hierarchy affects structural balance as temperature variations, which measure an individual's average irrationality in society. To address this question, we develop a two-layer balance model, the \enquote{leader layer}, which maintains structural balance exclusively through intra-layer interactions. Conversely, the \enquote{follower layer} maintains structural equilibrium through both inter- and intra-layer interactions. The Hamiltonian of the leading layer is independent, while the follower layer depends on its parameters as well as those of the leading layer. Analytical results from the mean-field approximation and exact Monte Carlo simulations show that instability arises in the equilibrium states of the follower layer when the temperature is below the critical threshold ($T<T_c$), which is different from the structural Heider equilibrium. Furthermore, our findings indicate that the critical temperature is elevated in the follower layer.

In 2024, the Belle II experiment resumed data taking after the Long Shutdown 1, which was required to install a two-layer pixel detector and upgrade accelerator components. We describe the challenges of this shutdown and the operational experience thereafter. With new data, the silicon-strip vertex detector (SVD) confirmed the high hit efficiency, the large signal-to-noise ratio, and the excellent cluster position resolution. In the coming years, the SuperKEKB peak luminosity is expected to increase to its target value, resulting in a larger SVD occupancy caused by beam background. Considerable efforts have been made to improve SVD reconstruction software by exploiting the excellent SVD hit-time resolution to determine the collision time and reject off-time particle hits. A novel procedure to group SVD hits event-by-event, based on their time, has been developed using the grouping information during reconstruction, significantly reducing the fake rate while preserving the tracking efficiency. The front-end chip (APV25) is operated in the multi-peak mode, which reads six samples. A 3/6-mixed acquisition mode, based on the timing precision of the trigger, reduces background occupancy, trigger dead-time, and data size. Studies of the radiation damage show that the SVD performance will not seriously degrade during the lifetime of the detector, despite moderate radiation-induced increases in sensor current and strip noise.

Infrared thermography is a powerful tool for studying liquid-to-vapor phase change processes. However, its application has been limited in the study of vapor-to-liquid phase transitions due to the presence of complex liquid dynamics, multiple phases within the same field of view, and experimental difficulty. Here, we develop a calibration framework which is capable to studying one of the most complex two-phase heat transfer processes: dropwise condensation. The framework accounts for non-uniformities arising from dynamic two-phase interactions such as droplet nucleation, growth, coalescence, and departure, as well as substrate effects particularly observed on micro- and nanoengineered surfaces. This approach enables high-resolution temperature measurements with both spatial (12 $\mu$m) and temporal (5 ms) precision, leading to the discovery of local temperature phenomena unobservable using conventional approaches. These observed temperature variations are linked to droplet statistics, showing how different regions contribute to local condensation heat transfer. We extend the developed method to quantify local thermal parameters by fusing it with a generative machine learning model to map visual images into temperature fields. The model is informed of the physical parameter by incorporating vapor pressure embedding as the conditional parameter. This work represents a significant step toward simplifying local temperature measurements for vapor-to-liquid phase change phenomena by developing a methodology as well as a machine learning approach to map local thermal phenomena using only optical images as the input.

Energy partition between ions and electrons in collisionless shocks has long been an unsolved fundamental physical problem. We show that kinetic simulations of moderate Alfv\'enic Mach number, magnetized, collisionless shocks reveal rapid, faster-than-Coulomb, energy exchange between ions and electrons when the plasma is sufficiently magnetized. Using kinetic and multi-fluid models with counter-streaming ions, we identify resonances between electron whistler and ion magnetohydrodynamic waves that account for this rapid energy exchange.

Atom arrays with sub-wavelength lattice constant can exhibit fascinating optical properties. Up to now, much of our understanding of these systems focuses on the single-excitation regime. In one relevant example, the combination of multiple excited states and magnetic fields can yield topological band structures, albeit with dispersion relations that can exhibit divergences near the light cone. Here, we go beyond the single-excitation level to show that such systems can give rise to few-particle Laughlin-like states. In particular, we consider small honeycomb ``flakes,'' where the divergences can be smeared out by finite-size effects. By choosing an appropriate value of magnetic field we thereby obtain an energy spectrum and eigenstates resembling those of Landau levels. The native hard-core nature of atomic excitations then gives rise to multi-excitation Laughlin-like states. This phenomenon occurs not only in samples of tens of sites, but also in a minimal nanoring system of only six sites. Next, considering two-particle Laughlin-like states, we show that they can be driven by uniform light, and that correlations of the output light contain identifying fingerprints of these states. We believe that these results are a step towards new paradigms of engineering and understanding strongly-correlated many-body states in atom-light interfaces.

This paper demonstrates the feasibility of democratizing AI-driven global weather forecasting models among university research groups by leveraging Graphics Processing Units (GPUs) and freely available AI models, such as NVIDIA's FourCastNetv2. FourCastNetv2 is an NVIDIA's advanced neural network for weather prediction and is trained on a 73-channel subset of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset at single levels and different pressure levels. Although the training specifications for FourCastNetv2 are not released to the public, the training documentation of the model's first generation, FourCastNet, is available to all users. The training had 64 A100 GPUs and took 16 hours to complete. Although NVIDIA's models offer significant reductions in both time and cost compared to traditional Numerical Weather Prediction (NWP), reproducing published forecasting results presents ongoing challenges for resource-constrained university research groups with limited GPU availability. We demonstrate both (i) leveraging FourCastNetv2 to create predictions through the designated application programming interface (API) and (ii) utilizing NVIDIA hardware to train the original FourCastNet model. Further, this paper demonstrates the capabilities and limitations of NVIDIA A100's for resource-limited research groups in universities. We also explore data management, training efficiency, and model validation, highlighting the advantages and challenges of using limited high-performance computing resources. Consequently, this paper and its corresponding GitHub materials may serve as an initial guide for other university research groups and courses related to machine learning, climate science, and data science to develop research and education programs on AI weather forecasting, and hence help democratize the AI NWP in the digital economy.

In this thesis, I discuss several basic science studies in the field of energy materials using neutron scattering as a probe for the lattice dynamics. To enable understanding of neutron scattering spectra, I also use computational and theoretical methods. These methods and neutron scattering in general are discussed in detail in Chapter 2. It is assumed that the reader is familiar with basic quantum mechanics as well as with solid state physics topics including the band theory of electrons, harmonic lattice dynamics, and molecular dynamics. For the unfamiliar reader, the details of electronic structure theory and lattice dynamics that are needed to understand the methods in Chapter 2 are provided in Chapters 3 and 4. In the remaining chapters, these methods are applied to the study of several energy materials: cuprate La2CuO4,(hybrid) solar perovskite CH3NH3PbI3, and thermoelectric clathrate Ba8Ga16Ge30.

Accurate kinetic analysis of [$^{18}$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs). Traditionally, IDIFs are obtained from the aorta, neglecting anatomical variations and complex vascular contributions. This study proposes a multi-organ segmentation-based approach that integrates IDIFs from the aorta, portal vein, pulmonary artery, and ureters. Using high-resolution CT segmentations of the liver, lungs, kidneys, and bladder, we incorporate organ-specific blood supply sources to improve kinetic modelling. Our method was evaluated on dynamic [$^{18}$F]FDG PET data from nine patients, resulting in a mean squared error (MSE) reduction of $13.39\%$ for the liver and $10.42\%$ for the lungs. These initial results highlight the potential of multiple IDIFs in improving anatomical modelling and fully leveraging dynamic PET imaging. This approach could facilitate the integration of tracer kinetic modelling into clinical routine.

Broadband quantum-limited parametric amplifiers (PAs) are essential components in quantum information science and technology. Impedance-engineered resonator-based PAs and traveling-wave PAs are the primary approaches to overcome the gain-bandwidth constraint. While the former PAs are simpler to fabricate, the target characteristic impedance Z_\text{NR} of the nonlinear resonator has been restricted to be below 10 \Omega, requiring large capacitance. Moreover, these PAs have only been implemented with aluminum-based Josephson junctions (JJs), hindering their operation at high temperatures or strong magnetic fields. To address these issues, we propose a three-stage impedance-transformer scheme, showcased with a 20-nm-thick, 250-nm-wide high-kinetic-inductance niobium-titanium-nitride (NbTiN) film. Our scheme enables Z_\text{NR} up to several tens of ohms--a tenfold improvement over conventional designs, achieved through an additional quarter-wavelength transmission line with the characteristic impedance of 180 \Omega. Our kinetic-inductance impedance-engineered parametric amplifiers (KIMPA), featuring a 330-fF shunt capacitor, demonstrate a phase-preserving amplification with a 450-MHz bandwidth at 17-dB gain, and an added noise ranging from 0.5-1.3 quanta near the center frequency of 8.4 GHz. Due to the high critical current of the NbTiN nanowire, the KIMPA also achieves a saturation power of up to -68\pm3 dBm, approximately 30-dB higher than that of JJ-based PAs. This scheme also opens new possibilities for other three-wave-mixing building blocks.

Skin, the primary regulator of heat exchange, relies on sweat glands for thermoregulation. Alterations in sweat gland morphology play a crucial role in various pathological conditions and clinical diagnoses. Current methods for observing sweat gland morphology are limited by their two-dimensional, in vitro, and destructive nature, underscoring the urgent need for real-time, non-invasive, quantifiable technologies. We proposed a novel three-dimensional (3D) transformer-based multi-object segmentation framework, integrating a sliding window approach, joint spatial-channel attention mechanism, and architectural heterogeneity between shallow and deep layers. Our proposed network enables precise 3D sweat gland segmentation from skin volume data captured by optical coherence tomography (OCT). For the first time, subtle variations of sweat gland 3D morphology in response to temperature changes, have been visualized and quantified. Our approach establishes a benchmark for normal sweat gland morphology and provides a real-time, non-invasive tool for quantifying 3D structural parameters. This enables the study of individual variability and pathological changes in sweat gland structure, advancing dermatological research and clinical applications, including thermoregulation and bromhidrosis treatment.

The quantum nondemolition (QND) measurement is one of the most studied quantum measurement procedures. Usually, such process involves the coupling of a single system of interest, called signal, with a single probe system, so that the relevant information in the signal system is indirectly measured by observing the probe system. Here, we extend the concept of quantum nondemolition interaction to the cases in which the signal and the probe systems are each one multipartite continuous-variable systems. Specifically, we propose a general scheme that performs the multipartite QND interactions, relying on beam-splitter couplings among the signal and probe modes with ancillary modes prepared off-line in squeezed states. The scheme is also composed by homodyne detections and feedforward modulations. The ancillary modes are detected in the process, providing photocurrents for post-modulation of the output systems, as well as sufficient information to calculate genuine multipartite entanglement conditions of the input systems and to monitor similar conditions of the output systems.

We present a physics-embedded Bayesian neural network (PE-BNN) framework that integrates fission product yields (FPYs) with prior nuclear physics knowledge to predict energy-dependent FPY data with fine structure. By incorporating an energy-independent phenomenological shell factor as a single input feature, the PE-BNN captures both fine structures and global energy trends. The combination of this physics-informed input with hyperparameter optimization via the Watanabe-Akaike Information Criterion (WAIC) significantly enhances predictive performance. Our results demonstrate that the PE-BNN framework is well-suited for target observables with systematic features that can be embedded as model inputs, achieving close agreement with known shell effects and prompt neutron multiplicities.

The coherent manipulation of a quantum wave is at the core of quantum sensing. For instance, atom interferometers require linear splitting and recombination processes to map the accumulated phase shift into a measurable population signal. Although Bose Einstein condensates (BECs) are the archetype of coherent matter waves, their manipulation between trapped spatial modes has been limited by the strong interparticle collisions. Here, we overcome this problem by using BECs with tunable interaction trapped in an innovative array of double-well potentials and exploiting quantum tunneling to realize linear beam splitting. We operate several Mach-Zehnder interferometers in parallel, canceling common-mode potential instabilities by a differential analysis, thus demonstrating a trapped-atom gradiometer. Furthermore, by applying a spin-echo protocol, we suppress additional decoherence sources and approach unprecedented coherence times of one second. Our interferometer will find applications in precision measurements of forces with a high spatial resolution and in linear manipulation of quantum entangled states for sensing with sub shot-noise sensitivity.

We compute the moments of the nonlinear binary collision integral in the ultrarelativistic hard-sphere approximation for an arbitrary anisotropic distribution function in the local rest frame. This anisotropic distribution function has an angular asymmetry controlled by the parameter of anisotropy $\xi$, such that in the limit of a vanishing anisotropy $\lim_{\xi \rightarrow 0} \hat{f}_{0 \mathbf{k}} = f_{0 \mathbf{k}}$, approaches the spherically symmetric local equilibrium distribution function. The corresponding moments of the binary collision integral are obtained in terms of quadratic products of different moments of the anisotropic distribution function and couple to a well defined set of lower-order moments. To illustrate these results we compare the moments of the binary collision integral to the moments of the widely used relaxation-time approximation of Anderson and Witting in case of a spheroidal distribution function. We found that in an expanding system the nonlinear Boltzmann collision term leads to twice slower equilibration than the relaxation-time approximation. Furthermore we also show that including two dynamical moments helps to resolve the ambiguity which additional moment of the Boltzmann equation to choose to close the conservation laws.

The development of statistical physics in Africa is in its nascent stages, yet its application holds immense promise for advancing emerging research trends on the continent. This perspective paper, a product of a two-week workshop on biophysics in Morogoro (Tanzania), aims to illuminate the potential of statistical physics in regional scientific research. We employ in-silico atomistic molecular dynamics simulations to investigate the loading and delivery capabilities of lecithin nanolipids for niclosamide, a poorly water-soluble drug. Our simulations reveal that the loading capacity and interaction strength between lecithin nanolipids and niclosamide improve with increased lecithin concentrations. We perform a free-energy landscape analysis which uncovers two distinct metastable conformations of niclosamide within both the aqueous phase and the lecithin nanolipids. Over a simulation period of half a microsecond, lecithin nanolipids self-assemble into a spherical monolayer structure, providing detailed atomic-level insights into their interactions with niclosamide. These findings underscore the potential of lecithin nanolipids as efficient drug delivery systems.

Zeolites, as representative porous materials, possess intricate three-dimensional frameworks that endow them with high surface areas and remarkable catalytic properties. There are a few factors that give a huge influence on the catalytic properties, including the size and connectivity of these three-dimensional channels and atomic level defects. In additional to that, the surface morphology and thickness variation of zeolites particles are essential to their catalytic performances as well. However, it is a significant challenge to characterize these macroscopic properties of zeolites using conventional techniques due to their sensitivity to electron beams. In this study, we introduce surface-adaptive electron ptychography, an advanced approach based on multi-slice electron ptychography, which enables high-precision reconstruction of both local atomic configurations and global structural features in zeolite nanoparticles. By adaptively optimizing probe defocus and slice thickness during the reconstruction process, SAEP successfully resolves surface morphology, thickness variations and atomic structure simultaneously. This integrated framework facilitates a direct and intuitive correlation between zeolite channel structures and particle thickness. Our findings open new pathways for large-scale, comprehensive structure property analysis of beam-sensitive porous materials, deepening the understanding of their catalytic behavior.

Network sampling is a crucial technique for analyzing large or partially observable networks. However, the effectiveness of different sampling methods can vary significantly depending on the context. In this study, we empirically compare representative methods from three main categories: node-based, edge-based, and exploration-based sampling. We used two real-world datasets for our analysis: a scientific collaboration network and a temporal message-sending network. Our results indicate that no single sampling method consistently outperforms the others in both datasets. Although advanced methods tend to provide better accuracy on static networks, they often perform poorly on temporal networks, where simpler techniques can be more effective. These findings suggest that the best sampling strategy depends not only on the structural characteristics of the network but also on the specific metrics that need to be preserved or analyzed. Our work offers practical insights for researchers in choosing sampling approaches that are tailored to different types of networks and analytical objectives.

We present in depth study of p-type doping concentration of mid-wave infrared (MWIR) and long-wave infrared (LWIR) mercury cadmium Telluride (HgCdTe) thin films. Annealing time was changed under specific conditions to achieve a stable copper (Cu) doping concentration for HgCdTe thin films. Both MWIR and LWIR HgCdTe material were grown by molecular beam epitaxy (MBE), where different trends were observed between LWIR and MWIR HgCdTe thin films by increasing anneal time. We also report the impact of different thickness (4 micron, 6 micron and 9 micron) along with annealing time on doping level of LWIR HgCdTe thin films.

We present a theoretical model for a transversally driven Bose-Einstein condensate coupled to an optical cavity. We focus on the interplay between different coherent couplings, which can trigger a structural phase transition, known as the superradiant phase transition. Our approach, based on band structure theory and a mean-field description, enables a comprehensive analysis of the nature of the system's excited modes, precursing the phase transitions. By incorporating dissipative couplings, intrinsic to these systems, we find non-Hermitian phenomena such as the coalescence of crossing precursor modes and the emergence of exceptional points (EPs). The general formulation of our model allows us to explain the role of an angle between transverse pump and the cavity deviating from $90^\circ$. This offers us a unified perspective on the plethora of different implementations of such systems.

Driven-dissipative quantum systems can undergo transitions from stationary to dynamical phases, reflecting the emergence of collective non-equilibrium behavior. We study such a transition in a Bose-Einstein condensate coupled to an optical cavity and develop a cavity-assisted Bragg spectroscopy technique to resolve its collective modes. We observe dissipation-induced synchronization at the quasiparticle level, where two roton-like modes coalesce at an exceptional point. This reveals how dissipation microscopically drives collective dynamics and signals a precursor to a dynamical phase transition.

Modern edge devices, such as cameras, drones, and Internet-of-Things nodes, rely on deep learning to enable a wide range of intelligent applications, including object recognition, environment perception, and autonomous navigation. However, deploying deep learning models directly on the often resource-constrained edge devices demands significant memory footprints and computational power for real-time inference using traditional digital computing architectures. In this paper, we present WISE, a novel computing architecture for wireless edge networks designed to overcome energy constraints in deep learning inference. WISE achieves this goal through two key innovations: disaggregated model access via wireless broadcasting and in-physics computation of general complex-valued matrix-vector multiplications directly at radio frequency. Using a software-defined radio platform with wirelessly broadcast model weights over the air, we demonstrate that WISE achieves 95.7% image classification accuracy with ultra-low operation power of 6.0 fJ/MAC per client, corresponding to a computation efficiency of 165.8 TOPS/W. This approach enables energy-efficient deep learning inference on wirelessly connected edge devices, achieving more than two orders of magnitude improvement in efficiency compared to traditional digital computing.