Papers

Reconstructing time-resolved flow fields from temporally sparse velocimetry measurements is critical for characterizing many complex thermal-fluid systems. We introduce a machine learning framework for uncertainty-aware flow reconstruction using sparse variational Gaussian processes in the Kolmogorov-Arnold network topology (SVGP-KAN). This approach extends the classical foundations of Linear Stochastic Estimation (LSE) and Spectral Analysis Modal Methods (SAMM) while enabling principled epistemic uncertainty quantification. We perform a systematic comparison of our framework with the classical reconstruction methods as well as Kalman filtering. Using synthetic data from pulsed impingement jet flows, we assess performance across fractional PIV sampling rates ranging from 0.5% to 10%. Evaluation metrics include reconstruction error, generalization gap, structure preservation, and uncertainty calibration. Our SVGP-KAN methods achieve reconstruction accuracy comparable to established methods, while also providing well-calibrated uncertainty estimates that reliably indicate when and where predictions degrade. The results demonstrate a robust, data-driven framework for flow field reconstruction with meaningful uncertainty quantification and offer practical guidance for experimental design in periodic flows.

The Gannon Storm in May 2024 was the largest space weather event experienced in 20 years, generating auroras latitudes as low as 35{\deg}. Such activity can pose significant operational challenges for critical infrastructure operators, particularly those managing electricity transmission networks, satellite constellations, and aviation systems. Substantial progress has been made in understanding space weather and the potential exposure of infrastructure assets to severe events. We have seen few evaluations of the types of current mitigation strategies in use to reduce our shared vulnerability to this activity, motivating this study. Firstly, we fill an important literature gap by undertaking a systematic review of the range of space weather mitigation strategies for these three critical infrastructure sectors. Secondly, we contacted 303 critical infrastructure operators (50 power, 227 satellite, 26 aviation) for participation in an anonymous online survey or interview receiving 55 unique responses (18% response rate). To capture narratives of mitigation actions taken and impacts experienced over the solar maximum, qualitative interviews were then conducted with 33 operators. The results identified 91 potential mitigation actions within the sectors and found that 149 mitigation actions were enacted due to space weather forecasts and experienced impacts. This is one of the first exhaustive studies of space weather mitigation activities, and moves beyond the traditional focus on purely impacts.

We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknown heterogeneous parameter fields in a low-dimensional nonlinear manifold, improving numerical conditioning and enabling stable gradient-based optimization under sparse observations. The proposed framework integrates a latent diffusion model (LDM), trained in a compact latent space, with a differentiable finite-volume discretization of the forward PDE. Sensitivities are propagated through the discretization using adjoint-based gradients combined with reverse-mode automatic differentiation. Inversion is performed directly in latent space, which implicitly suppresses ill-conditioned degrees of freedom while preserving dominant structural modes, including sharp material interfaces. The effectiveness of LD-DIM is demonstrated using a representative inverse problem for flow in porous media, where heterogeneous conductivity fields are reconstructed from spatially sparse hydraulic head measurements. Numerical experiments assess convergence behavior and reconstruction quality for both Gaussian random fields and bimaterial coefficient distributions. The results show that LD-DIM achieves consistently improved numerical stability and reconstruction accuracy of both parameter fields and corresponding PDE solutions compared with physics-informed neural networks (PINNs) and physics-embedded variational autoencoder (VAE) baselines, while maintaining sharp discontinuities and reducing sensitivity to initialization.

The first digit (FD) phenomenon i.e., the significant digits of numbers in large data are often distributed according to a logarithmically decreasing function was first reported by S. Newcomb and then many decades later independently by F. Benford. After its century long neglect the last three decades have seen huge growth in the number of relevant publications. However, notwithstanding the rising popularity the two independent proponents of the phenomenon are not equally acknowledged an indication of which is disproportionate number of citations accumulated by Newcomb (1881) and Benford (1938). In the present study we use citation analysis to show that the formalization of the eponym Benford's law, a name questionable itself for overlooking Newcomb's contribution, by Raimi (1976) had a strong adverse effect on the future citations of Newcomb (1881). Furthermore, we identify the papers published over various decades of the developmental history of the FD phenomenon, which latter turned out to be amongst the most cited ones in the field. We find that lack of its consideration, intentional or occasionally out of ignorance for referencing by the prominent papers, is responsible for a far lesser number of citations of Newcomb (1881) in comparison to Benford (1938).

To address the imperative for covert underwater swarm coordination, this paper introduces "Hydrodynamic Whispering," a near-field silent communication paradigm utilizing Artificial Lateral Line (ALL) arrays. Grounded in potential flow theory, we model the transmitter as an oscillating dipole source. The resulting pressure field exhibits steep nearfield attenuation (scaling with 1/r^2, naturally delimiting a secure "communication bubble" with intrinsic Low Probability of Interception (LPI) properties. We propose a transceiver architecture featuring a Binary Phase Shift Keying (BPSK) modulation scheme adapted for mechanical actuator inertia, coupled with a bio-inspired 24-sensor conformal array. To mitigate low Signal-to-Noise Ratio (SNR) in turbulent environments,a Spatio-Temporal Joint Processing framework incorporating Spatial Matched-Field Beamforming is developed. Simulation results demonstrate that the system achieves an array gain of approximately 13.8 dB and maintains a near-zero Bit Error Rate (BER) within the effective range. This study validates the feasibility of utilizing localized hydrodynamic pressure fluctuations for reliable and secure short-range underwater networking.

The move towards electric school buses (ESBs) marks a critical step in creating a healthier and more sustainable future for students. To meet the ambitious goal of zero-emission school buses by 2035,this review focuses on the need assessment, practices, gaps, challenges, and way forward. We conducted a comprehensive assessment of more than 100 relevant sources, resulting in a final investigation. In-depth, systematic, and qualitative content analysis with SWOT analysis produced critical insights into school transport electrification. The results showed that 1.8% of the total buses in the US have already been converted to electric, where California alone owning 29% of the buses. Subsidies from various agencies and programs have contributed to the rapid growth of electrification. However, challenges in cost, technology, and policies must be mitigated through innovation and stakeholder partnerships. Policy support is boosting subsidies, industry investment and market readiness. Equitable policy is important to support underserved and disadvantaged populations, which can be addressed through four key dimensions of equity: procedural, recognition, distributive, and reparative equity. Furthermore, the traditional bus deployment model is still the most common, whereas Transportation-as-a-Service (TaaS) is an innovative ESB deployment model with the potential to accelerate ESB adoption by integrating vehicle-to-grid. SWOT analysis indicated that the achievement of the zero-emission goal, autonomous driving, and repowered vehicle technology are the greatest opportunities. Dynamic electrification strategies, V2G technology and system resiliency are yet to be discovered, which could be crucial for mass electrification.

We examine whether large language models (LLMs) hold systematic beliefs about environmental, social, and governance (ESG) issues and how these beliefs compare with-and potentially influence-those of human market participants. Based on established surveys originally administered to professional and retail investors, we show that major LLMs exhibit a strong pro-ESG orientation. Compared with human investors, LLMs assign greater financial relevance for ESG performance, expect larger return premia for high-ESG firms, and display a stronger willingness to sacrifice financial returns for ESG improvements. These preferences are highly uniform and values-driven, in contrast to heterogeneous human views. Using a large dataset of analyst reports, we further show that sell-side analysts become significantly more optimistic about high-ESG firms after adopting LLMs for research. Our findings reveal that LLMs embed distinct, coherent ESG beliefs and that these beliefs can shape human judgments, highlighting a new channel through which AI adoption may influence financial markets.

Silver iodide (AgI) thin films offer a compelling platform for studying nonlinear optical phenomena due to their intrinsic noncentrosymmetric lattice and direct band gap. Here, we investigate the nonlinear optical properties of AgI thin films grown by physical vapor deposition that selectively produce zincblende (\zbAgI) and wurtzite (\wzAgI) phases. Using a combination of polarization-resolved second harmonic generation (SHG) and two-photon photoluminescence (2PPL) spectroscopy, we identify clear phase- and morphology-dependent anisotropic nonlinear responses. Triangular \zbAgI $(111)$ flakes exhibit sixfold SHG symmetry and isotropic 2PPL emission, while rod-shaped \wzAgI $(101)$ samples display twofold-symmetric patterns in both SHG and 2PPL, which are explained by theories that integrate explicit polarization analysis using second- and third-order nonlinear susceptibilities. We estimate $\chi^{(2)}_\mathrm{eff}$ of 0.45 pm/V (\zbAgI) and 0.16 pm/V (\wzAgI), respectively, after correcting for multiple internal reflections and generation of SHG within the AgI film and optical interference effect in the dielectric layers. These results identify AgI as a useful single-composition halide platform for studying phase-dependent nonlinearity and establish a realistic methodology for evaluating nonlinear susceptibilities in layered materials or thin films supported by a substrate.

Artificial Intelligence (AI) is transforming domains from healthcare and agriculture to finance and industry. As progress on Earth meets growing constraints, the next frontier is outer space, where AI can enable autonomous, resilient operations under extreme uncertainty and limited human oversight. This paper introduces Space AI as a unified interdisciplinary field at the intersection of artificial intelligence and space science and technology. We consolidate historical developments and contemporary progress, and propose a systematic framework that organises Space AI into four mission contexts: 1 AI on Earth, covering intelligent mission planning, spacecraft design optimisation, simulation, and ground-based data analytics; 2 AI in Orbit, focusing on satellite and station autonomy, space robotics, on-board/near-real-time data processing, communication optimisation, and orbital safety; 3 AI in Deep Space, enabling autonomous navigation, adaptive scientific discovery, resource mapping, and long-duration human-AI collaboration under communication constraints; and 4 AI for Multi-Planetary Life, supporting in-situ resource utilisation, habitat and infrastructure construction, life-support and ecological management, and resilient interplanetary networks. Ultimately, Space AI can accelerate humanity's capability to explore and operate in space, while translating advances in sensing, robotics, optimisation, and trustworthy AI into broad societal impact on Earth.

Continuous fiber-reinforced composite manufactured by additive manufacturing (CFRC-AM) offers opportunities for printing lightweight materials with high specific strength. However, their performance is sensitive to the interaction of process and material parameters, making exhaustive experimental testing impractical. In this study, we introduce a data-efficient, multi-input, multi-target learning approach that integrates Latin Hypercube Sampling (LHS)-guided experimentation with a squeeze-and-excitation wide and deep neural network (SE-WDNN) to jointly predict multiple mechanical and manufacturing properties of CFRC-AMs based on different manufacturing parameters. We printed and tested 155 specimens selected from a design space of 4,320 combinations using a Markforged Mark Two 3D printer. The processed data formed the input-output set for our proposed model. We compared the results with those from commonly used machine learning models, including feedforward neural networks, Kolmogorov-Arnold networks, XGBoost, CatBoost, and random forests. Our model achieved the lowest overall test error (MAPE = 12.33%) and showed statistically significant improvements over the baseline wide and deep neural network for several target variables (paired t-tests, p <= 0.05). SHapley Additive exPlanations (SHAP) analysis revealed that reinforcement strategy was the major influence on mechanical performance. Overall, this study demonstrates that the integration of LHS and SE-WDNN enables interpretable and sample-efficient multi-target predictions, guiding parameter selection in CFRC-AM with a balance between mechanical behavior and manufacturing metrics.

This paper introduces temporal Bragg gratings as a new class of broadband, reconfigurable parametric amplifiers. We present a comprehensive investigation of power amplification in temporal Bragg gratings, spatially periodic structures with refractive index modulated near the Bragg frequency. Through systematic numerical simulations, we explore the effects of modulation location (high-index vs. low-index layers), frequency, and amplitude on gain spectra and field dynamics. Both layer types yield significant parametric amplification, with high-index modulation providing higher gain for comparable depths. Amplification is frequency-agile, with gain peaks tunable across a broad range, and exhibits strong asymmetry: the sub-Bragg regime requires substantially stronger modulation than supra-Bragg for comparable gain. In the extreme sub-Bragg limit, the system transitions from discrete sidebands to a broadband gain continuum via multi-phase-matching. These results establish a unified framework for designing reconfigurable optical amplifiers, tunable frequency converters, and broadband light sources using temporally modulated photonic crystals.

Environmental 'range boosts' in wireless links are often explained through radiation-pattern intuition, yet the underlying physics is more cleanly captured by two environment-controlled quantities: radiative damping of the radiator and channel coupling between transmitter and receiver. Building from a dyadic-Green-function current–field formulation, we introduce an operational two-factor description of Purcell-like behavior for classical antennas. A self factor quantifies environment-induced changes in radiative damping under an explicit excitation convention, while a transfer factor quantifies environment-induced changes in Tx–Rx coupling. We provide measurement-aware extraction workflows (VNA $S_{11}\!\rightarrow Z_{\mathrm{in}}$ with efficiency and realized-gain accounting; link-test normalization to isolate $F_{\mathrm{tr}}$) and falsification diagnostics that prevent conflating true radiative enhancement with mismatch or added absorption. Finally, we translate self/transfer modifications into link-budget and range scalings and illustrate the framework across practical environments from VHF to mmWave, including platforms/ground planes, body proximity, field-expedient environmental radiators, terrain and passive redirection, tunnel/canyon confinement, and engineered scattering environments such as reflectarrays, metasurfaces, and reconfigurable intelligent surfaces (RIS).

Maximising particle and energy confinement is crucial for achieving the sustained burning plasma conditions necessary to realise fusion energy. For stellarator reactors, one proposed strategy for avoiding destructive instabilities is to operate at high-field but low(er) plasma pressure. In this work, we investigate the accessibility of such a reactor-relevant low-beta regime in a reactor-scale quasi-axisymmetric stellarator using state-of-the-art high-fidelity macro- and microscopic simulation tools. We consider a configuration with a flattened core pressure profile and favourable properties from the macroscopic and neoclassical perspectives. By contrast, linear and nonlinear calculations with the GENE code show an abrupt transition to a regime of highly deleterious transport at low (local) plasma beta. We describe the characterisation of these transport regimes as well as the confinement transition. We discuss the implications broadly for stellarator optimisation and highlight the impact on quasi-symmetric stellarator design strategies.

A theoretical analysis of the Casimir interaction between an arc and plate is conducted, which remains unexplored despite its relevance to Micro-Electro-Mechanical Systems (MEMS) fabrication. The configuration consists of a rigid finite plate and a flexible curved nanomembrane, with radius 100 micrometers, initially concave toward the rigid plate. The maximum thickness is evaluated for which the nanomembrane undergoes a change in curvature: from concave to convex with respect to the plate, due to the Casimir interaction. The Casimir energy for a curved surface is derived using the Proximity Force Approximation (PFA) with next-to-leading-order (NTLO) corrections. Kirchhoff-Love theory for a thin isotropic plate of constant thickness is used to estimate the bending energy. Material-dependent effects on the Casimir interaction are evaluated by comparing Au and Ag plates. The maximum thickness is derived where U_Casimir > U_bending for distances in the range of 0.1-1 micrometers. Results show curvature reversal occurs for nanomembranes with nanoscale thicknesses at the studied distances. Silver nanomembranes tolerate greater thickness than gold nanomembranes due to material-dependent properties. Comparison between NTLO-corrected PFA and perturbative PFA confirms the accuracy of the NTLO approach. The Casimir arc-to-plate geometry in MEMS enables Casimir-based actuation, enhances devices reliability, and prevents stiction. These findings provide thickness constraints for MEMS design and performance, accounting for the Casimir force.

We demonstrate a three-dimensional magneto-optical trap (MOT) of a metal hydride molecule, CaH. We are able to scatter $\sim$$10^{4}$ photons with vibrational loss covered up to vibrational quantum number $\nu=2$. This allows us to laser slow the molecular beam near zero velocity with a "white-light" technique and subsequently load it into a radio-frequency MOT. The MOT contains $230(40)$ molecules, limited by beam source characteristics and predissociative loss of CaH. The temperature of the MOT is below one millikelvin. The predissociative loss mechanism could, in turn, facilitate controlled dissociation of the molecule, offering a possible route to optical trapping of hydrogen atoms for precision spectroscopy.

Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms. While prior research examines effects on banned communities or source platform health, the impact on alternative platforms that absorb displaced users remains understudied. We analyze four major Reddit ban waves (2015–2020) and their effects on generalist communities on Voat, asking how post-ban arrivals reshape community structure and through what mechanisms transformation occurs. Combining network analysis, toxicity detection, and dynamic reputation modeling, we identify two distinct regimes of migration impact: (1) Hostile Takeover (2015–2018), where post-ban arrival cohorts formed parallel social structures that bypassed existing community cores through sheer volume, and (2) Toxic Equilibrium (2018–2020), where the flattening of existing user hierarchy enabled newcomers to integrate into the now-dominant toxic community. Crucially, community transformation occurred through peripheral dynamics rather than hub capture: fewer than 5% of newcomers achieved central positions in most months, yet toxicity doubled. Migration structure also shaped outcomes: loosely organized communities dispersed into generalist spaces, while ideologically cohesive groups concentrated in dedicated enclaves. These findings suggest that receiving platforms face a narrow intervention window during the hostile takeover phase, after which toxic norms become self-sustaining.

The dome of the Roman Pantheon is coffered with ribs surrounding sunken lacunaria, thus forming a grid. How this is achieved given the curvature of the dome has long been a subject for study and speculation. Although detailed measurements now exist, thus far no single principle has emerged which fixes the overall geometry. Similar coffering occurs in Hawksmoor's design for the ceiling of the Buttery in All Souls College, Oxford. Both these examples are doubly curved, making their tiling with (almost) square coffers problematic. We address this using methods of differential geometry. Observing that the ribs intersect orthogonally, we hypothesize that they are images under a conformal (angle preserving) mapping of a uniform M x N tiling of a planar rectangle with squares. This is unique and is the inverse of Mercator's projection of the ceiling to the rectangle. It minimizes an energy functional which captures the sum of the elastic and gravitational energies in the long wavelength limit. Thus, any construction method which is stable to long wavelength deformations necessarily leads to a conformal coffering. This then gives quantitative predictions with no adjustable parameters for the relative sizes and locations of each coffer, which are in good agreement with photographic data, and consistent with direct measurements of the Pantheon. We suggest protocols by which Hawksmoor's ceiling and the Pantheon might have been constructed without advanced mathematics.

Traditional design principles for determining the optimal intake ramp or cone angles, for ensuring no flow spillage at the intake cowl under design conditions, and for the form of the terminal shock in the intake duct are revisited. We show that it is preferable to select the ramp or cone angles to be somewhat smaller than that suggested by the Oswatitsch criterion. An offset cowl lip that slightly violates the shock-on-lip condition is found to be beneficial; in fact, an offset cowl can be arranged for conical intakes with no flow spillage at the cowl lip at all. Improvements to the total pressure recovery are seen when the terminal normal shock is replaced by a strong form of the oblique shock for two-dimensional ramp-type intakes, and with a Lambda shock in case of conical intakes. The necessary design modifications are simple and virtually cost-free. These results rewrite the ground rules for the aerodynamic design of supersonic intakes.

In this paper, we study the ventilation airflow in a model classroom, where exhaust fans throw out the used air, to replace it with outdoor air through open door. Hybrid ventilation, or mechanically assisted natural ventilation, of this kind is used as a retrofit design to reduce infection risk from airborne transmission. The air stream entering the door forms a jet-like flow, driven by the suction effect of exhaust fans. We compute the jet velocity using Reynolds averaged Navier Stokes (RANS) method and compare with velocity field measured using particle image velocimetry. Different turbulence models are found to match experimental data near the door, but they over-predict the peak jet velocity further downstream. There is minimal variation between the results obtained using different turbulence models. The computational results are found to be sensitive to inlet boundary conditions, whether the door entry is specified as a pressure inlet or velocity inlet. The geometry of the space outside the door also has a significant effect on the jet velocity. Changing the boundary condition takes the computational results closer to the experimental data; the velocity profiles computed with the extended domain being the closest to the measured peak velocity. Interestingly, the centerline velocity decay computed with the extended domain aligns well with the experimental data. The other cases, irrespective of turbulence model, show much lower decay rate that seem to align with wall jet scaling. This suggests that geometry and boundary conditions at the door is critical to predict the airflow in hybrid ventilation.

Wall pressure fluctuations beneath the turbulent boundary layer of high-speed underwater vehicles are crucial for hydro-acoustics and acoustic stealth. However, a comprehensive understanding remains limited due to a lack of high-quality experimental data, particularly under realistic operational conditions. To address this gap, this study establishes the first high-fidelity experimental database of wall-pressure fluctuations on an axisymmetric hull at high Reynolds numbers. The dataset's primary innovation is its systematic inclusion of complex maneuvering (yaw and pitch) conditions, providing a benchmark for validating flow noise prediction models. Analysis of this dataset yields key physical insights. The study quantifies systematic Reynolds number effects, including a spectral energy shift toward lower frequencies, and spectral scaling laws by revealing the critical influence of pressure-gradient effects. These findings provide fundamental insights into non-equilibrium 3D turbulent flows and establish an essential dataset to support the design of quieter and more effective underwater vehicles.

We present an interacting model of neural network dynamics that incorporates key biological features, including multiple forms of inhibitory interactions. We develop a hierarchy of analytical mean-field approximations to characterize nonequilibrium phase transitions between ordered, disordered, and chaotic regimes, complemented by a detailed stability analysis. We show that inhibition generically enhances the stability of network dynamics. The model is consistent with the quasi-criticality hypothesis, exhibiting regions of maximal dynamical susceptibility and mutual information, modulated by the strength of external stimuli. We further demonstrate that, at the mean-field level, the critical transition belongs to the mean-field directed percolation universality class, in agreement with prior experimental and theoretical studies. More broadly, our framework may offer insights into neurological disorders, with the unstable regime exhibiting chaotic dynamics that may be associated with epileptic seizures.

Understanding pollutant meteorology interactions is essential for environmental risk assessment. This study develops an entropy-based statistical framework to analyze static and temporal dependencies between urban air pollutants and meteorological variables across multiple Indian cities. Dependence is quantified using complementary linear and nonlinear measures, including Pearson correlation, mutual information, and relative conditional entropy. A key methodological contribution is a PCA based composite indexing framework that integrates these heterogeneous metrics into a unified and interpretable correlation score. For each pollutant meteorological pair within a city, PCA is used to extract a joint variability index, while spatial variability is assessed by aggregating correlations across cities. These indices are further combined to derive a comprehensive city-level correlation score that represents overall pollutant meteorology coupling strength and enables classification of cities into distinct interaction regimes. Sensitivity analysis, performed by systematically excluding individual variable pairs, demonstrates the robustness of the framework, with no single pair exerting disproportionate influence. Temporal dependencies are examined using transfer entropy and time-delayed mutual information. Results indicate that relative humidity generally leads changes in pollutant concentrations, whereas ambient temperature tends to lag, highlighting contrasting causal influences. Mutual information peaks at zero lag and decays rapidly, indicating strong short term interactions with limited persistence. Overall, the proposed framework provides a unified and interpretable approach for assessing complex pollutant meteorology interactions across diverse locations and time.

Habitat loss and fragmentation have often been viewed as major threats to species interaction and global biodiversity conservation. However, habitat degradation can also give rise to positive ecological and behavioral responses, challenging the notion that its consequences are entirely detrimental. While controlling for the degree of total habitat loss, we studied the influence of habitat fragmentation and phenotypic diversity on the evolution of tag-based cooperation in structured populations with multiple strategies. We developed a spatially explicit agent-based model with empty sites in which phenotypically diverse artificial decision makers engaged into pairwise Snowdrift-game interactions and imitated strategies of their opponent co-players. We systematically varied the number of phenotypic features in the population, the clustering degree of empty sites unsuitable for habitation, as well as the cost-to-benefit ratio $r$, and we measured the resulting equilibrium densities of conditional and unconditional strategies. Our Monte Carlo simulations revealed a complex interplay between the three investigated factors, such that higher phenotypic diversity in combination with lower $r$ and low to intermediate clustering degrees of empty sites markedly suppressed ethnocentric cooperation but simultaneously boosted unconditional, pure altruism. This dominance of unconditional cooperation was remarkably robust to variation in the initial conditions, suggesting that heterogeneous fragmentation of empty sites in moderately degraded habitats can function as a potent cooperation-promoting mechanism even in the presence of initially more favorable strategies. Our study showcases anti-fragility of cooperators in spatially fragmented but phenotypically diverse populations, as they were also able to benefit from harsh environmental conditions emerging in sparsely connected habitat remnants.

Context: During the last decades, significant progress has been made in both numerical simulations of turbulent dynamo and theoretical understanding of turbulence. However, there is still lack of quantitative comparison between the simulations and the theory of the dynamo. Results: We study the critical magnetic Reynolds number ($Rm_c$) and the growth rate near the threshold both in the limit of very high and in the case of moderate Reynolds numbers. We argue that in Kazantsev equation for magnetic field generation, one should use the quasi-Lagrangian correlator of velocities instead of Eulerian, as usually implied when comparing theory and simulations. The theoretical results obtained with this correlator agree well with numerical results. We also propose the explanation of the decrease of $Rm_c$ as a function of Reynolds number ($Re$) at intermediate-high $Re$. It is probably due to Reynolds-dependent intermittency of the velocity structure function: we show that the scaling exponent of this function in the inertial range affects strongly the magnetic field generation, and it is known to be an increasing function of the Reynolds number. Conclusions: Use of quasi-Lagrangian correlator in the Kazantsev theory gives good accordance with numerical simulations. An ideal way to compare them should be to find the correlator substituted to the Kazantsev equation and the generation properties in the same simulation. At least one has to use universal parameters independent of the properties of pumping scale. Reynolds-dependent intermittency can explain recently observed decrease of the critical magnetic Reynolds number at small Prandtl numbers.

We commence our study with review of dispersion interactions in electrolytes. We then reflect on how background media change atom-atom excited-state systems. To highlight the impact of nonlocal media, such as salt solutions, we predict that a new contribution to the resonance interaction energy emerges in a form $\propto e^{-\kappa_{\rm D} \rho}/\rho$. Here $\kappa_{\rm D}$ is the Debye length and $\rho$ is the distance between the atoms. This contribution vanishes at zero temperature, where a new term proportional to $1/\rho^4$ (similar to free space) occurs. This new term is dampened by the electrolyte at large distances, causing it to decrease much faster, proportional to $1/\rho^7$. The long-range electrolyte-induced resonance interaction at finite temperature may, in addition to the dominating van der Waals attraction (which goes as $1/\rho^6$), take part in the molecular formation of biological fluids.

In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a photon-counting CT scanner. We validate the accuracy of stopping power ratio (SPR) calculations using a conventional stoichiometric calibration approach and a prototype software, TissueXplorer. A validated CT simulator (DukeSim) was used to generate photon-counting CT projections of a computational head model, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors with a single lateral beam angle. Ground truth treatment plan was made directly on the computational head model using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The mean percentage difference in estimating the stopping power ratio with TissueXplorer in all head tissues inside the scanned volume was 0.28%. Stopping power ratios obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head model. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information, such as TissueXplorer, hold promise for more accurate prediction of the stopping power ratio than the conventional stoichiometric approach.

We propose a rigorous, physically interpretable, and quantifiable definition of the photonic nanojet (PNJ). This framework resolves longstanding ambiguities in measuring PNJ dimensions and leverages an optimal mass transport-based metric to quantify PNJ quality. Building on this metric, we develop a PNJ steering methodology that requires no opto-mechanical intervention, relying solely on phase-only illumination modulation.

In this work, we investigate the relative importance of electronic and phononic energy dissipation during the molecular adsorption of CO on Cu(110). Initial sticking probabilities as a function of impact energy for CO impinging at normal incidence at a surface temperature of 90 K were computed using classical trajectory simulations. To this aim, we use a full-dimensional potential energy surface constructed using an atomistic neural network trained on density functional theory data obtained with the nonlocal vdW-DF2 exchange-correlation functional. Two models are compared: one allowing only energy transfer and dissipation from the molecule to lattice vibrations, and the other also incorporating the effect of molecular energy loss due to the excitation of electron-hole pairs, modeled within the local-density friction approximation. Our results reveal, firstly, that the molecule mainly transfers energy to lattice vibrations, and this channel determines the adsorption probabilities, with electronic friction playing a minor role. Secondly, once the molecule is trapped near the surface (where electronic density is higher), electron-hole pair excitations accelerate energy dissipation, significantly promoting CO thermalization. Still, the faster energy dissipation when electron-hole pair excitations are accounted for accelerates the accommodation of the adsorbed molecules in the chemisorption well but does not significantly alter their lateral displacements over the surface.

In this work we investigate the dissociation of CO$_2$ on Cu(110) by performing density functional theory calculations using the vdW-DF2 exchange-correlation functional, with a potential energy surface parameterized using artificial neural networks. We computed quasi-classical trajectory calculations of molecular and dissociative adsorption probabilities as a function of the initial impact energy of the molecules and surface temperature, by comparing our results with available supersonic molecular beam experimental data for normal incidence. Concerning the general dependence of the molecular and dissociative adsorption probabilities on the initial translational energy of the molecules, our theoretical results agree with experiments. Also in agreement with experiments, we have found that dissociative adsorption is not affected by surface temperature between 50 and 400 K, for impact energies for which the dissociation probability is larger than $\sim 10^{-3}$. We have investigated the influence of impact energy and surface temperature on the final state of the dissociation products by extending the time integration of the reactive trajectories up to 10 ps. We have found that above $\sim 2.5$ eV and close to or above room temperature, CO$_2$ dissociation induces strong surface distortions including final structures involving Cu adatoms. The creation of Cu vacancy-adatom pairs is stimulated by the presence of both CO$_{ads}$ and O$_{ads}$ which interact strongly with the Cu adatoms and even give rise to unexpected (O-Cu-CO)$_{ads}$ linear moieties anchored to the surface by the dissociated O atom and involving a Cu adatom almost detached from the surface. These surface distortions produced by dissociation products of high energy CO$_2$ molecules at and above room temperature might explain recent experiments that have found a greater saturation oxygen coverage for high energy molecules.

The simulation of fluid flow in real, multiscale porous media remains challenging due to the complexity of nanoscale phenomena and the difficulty of developing upscaling methodologies. In this study, we introduce a multiscale filtration framework based on quasi-static Pore Network Modelling, incorporating the effects of pore blockage resulting from capillary condensation of fluid in the nanoporous space. To accurately predict capillary condensation in nanoconfinement, we apply classical Density Functional Theory calculations considering capillary hysteresis. The pores blocked by condensate are excluded from the fluid flow, resulting in a decrease in permeability of the porous space. Our findings demonstrate that the resulting permeability is strongly dependent on the geometry of the porous space, including pore size distribution, throat size distribution, sample size, and the particular structure of the sample, as well as thermodynamic conditions and processes, specifically pressure growth or decrease. Overall, the presented research contributes valuable insights into multiscale transport phenomena and facilitates the advancement of upscaling techniques.