Papers

A combination of statistical inference and machine learning (ML) schemes has been utilized to create a thorough understanding of coarse experimental data based on Zernike variables characterizing optical aberrations in fluidic lenses. A classification of surplus-response variables through tolerance manipulation was included to unravel the dimensional aspect of the data. Similarly, the impact of the exclusion of supererogatory variables through the identification of clustering movements of constituents is examined. The method of constructing a spectrum of collaborative results through the application of similar techniques has been tested. To evaluate the suitability of each statistical method before its application on a large dataset, a selection of ML schemes has been proposed. The supervised learning tools principal component analysis (PCA), factor analysis (FA), and hierarchical clustering (HC) were employed to define the elemental characteristics of Zernike variables. PCA enabled to reduce the dimensionality of the system by identifying two principal components which collectively account for 95% of the total variance. The execution of FA indicated that a specific tolerance of independent variability of 0.005 could be used to reduce the dimensionality of the system without losing essential data information. A high cophenetic coefficient value of c=0.9629 validated an accurate clustering division of variables with similar characteristics. The current approach of mutually validating ML and statistical analysis methods will aid in laying the foundation for state-of-the-art (SOTA) analysis. The benefit of our approach can be assessed by considering that the associated SOTA will enhance the predictive accuracy between two comparable methods, in contrast to the SOTA analysis conducted between two arbitrary ML methods.

We propose a three-tier machine learning framework based on the next-generation Equation-Free algorithm for learning the spatio-temporal dynamics of mass-constrained complex systems with hidden states, whose dynamics can in principle be described by PDEs, but lack explicit models. In the first step, we employ Diffusion Maps (DMs), a nonlinear manifold learning algorithm, to extract low-dimensional latent representations of the complex spatio-temporal evolution. In the second step, we learn manifold-informed reduced-order models (ROMs) with Sparse Identification of Nonlinear Dynamics (SINDy) and standard linear Multivariate Autoregressive models (MVARs) to approximate the solution operator on the latent space. In the final step, the latent dynamics are lifted back to the original high-dimensional space by solving an (ill-posed) pre-image problem via a convex interpolation based on the k-NN algorithm. In doing so, the proposed framework reconstructs the solution operator of the unknown mass-constrained PDE, without explicitly identifying the PDE itself. For comparison purposes, we also evaluated the performance of the scheme for constructing ROMs based on Proper Orthogonal Decomposition (POD) and prove that both POD and the k-NN lifting operators preserve the mass. We illustrate the approach using two benchmark problems: (a) the Hughes model of crowd dynamics, which minimizes walking time while avoiding obstacles and high-density regions, and (b) a CFD problem involving the spatio-temporal evolution of a passive tracer advected by a periodic Navier–Stokes velocity field. We show that ROMs informed by DMs yield parsimonious models that consistently outperform the best POD-informed ROMs, yielding stable and accurate approximations of the solution operator in the latent space and, via reconstruction, in the original high-dimensional space over long time horizons.

Optically levitated particles have great potential to form the basis of novel quantum- enhanced sensors. These systems are very well suited for inertial sensing, as the particles are isolated from the environment when they are levitated at low pressures. However, there are many challenges in the experimental realization that may affect the performance of these systems. For example, optical aberrations in the wavefront of the trapping laser which arise from optical elements or misalignment have a great impact on the trapping potential. The detrimental effect of optical aberrations has not been thoroughly studied, and usually they are iteratively corrected, giving some conflicting results depending on the figures of merit that are used. In this work, we present a thorough study of the effects of structuring the wavefront of the trapping beams. We observe that clean beams, i.e. highly focused beams with unaberrated wavefronts, may be used to optimize the longitudinal frequencies, at the cost of the transversal ones. Our work is based in a combination of experimental studies using a complete basis of orthogonal polynomials (Zernike polynomials) to control the wavefront and a set of numerical calculations, which allow us to compare the impact of structured wavefronts on the quality of traps for optically levitated particles in vacuum. This will have direct applications in quantum sensing and fundamental studies of quantum mechanics, as it allows the reduction of optical backaction and thermal decoherence of the particles.

A method is presented for fabrication of femtosecond laser written waveguides in glass to remove the need for polishing of substrates after processing. It is shown that by amplitude masking the fabrication laser beam near the sample edge and increasing the pulse energy it is possible to write waveguides that are not affected by edge aberrations and display mode profiles well matched to single mode fiber. Results are presented for different depths in fused silica and borosilicate glass substrates. The transmission from fiber to photonic circuit is significantly improved for situations where it is not possible to polish glass substrates after laser writing, creating new opportunities in photonic packaging.

We investigate the dynamics of elastic capsules suspended in two-dimensional active nematic fluids using lattice Boltzmann simulations. The capsules, modeled as flexible membranes enclosing active internal regions, exhibit a rich variety of behaviors shaped by their geometry and the interplay between internal and external activity. Circular capsules with active interiors undergo persistent rotation driven by internally confined +1/2 topological defects. Axisymmetric capsules, such as boomerangs, develop directed motion along their axis of symmetry due to unbalanced active forces generated by defect distributions near their boundaries. We further show that capsule flexibility suppresses motility and rotation, as active stresses are dissipated into shape deformations. These findings reveal how shape, deformability, and defect dynamics cooperate to produce emergent motility in soft active matter, with potential applications in the design of microswimmers and drug delivery vehicles.

Penalty shootouts play a crucial role in the knockout stage of major football tournaments. Their importance has substantially increased from the 2021/22 season, when the Union of European Football Associations (UEFA) scrapped the away goals rule. Our paper examines whether the outcome of a penalty shootout can be predicted in UEFA club competitions. Based on all shootouts between 2000 and 2025, no evidence is found for the effect of the kicking order, the field of the match, or psychological momentum. In contrast to previous results, we do not detect any relationship between shootout success and relative team strength, quantified by differences in Elo ratings and the implied winning probability. Thus, the hypothesis that penalty shootouts are close to a coin toss in international competitions for European football clubs cannot be rejected.

Recent progress has been made in capturing spectral features of electronic states of double-excitation character in time-dependent density functional theory (TDDFT) through a frequency-dependent kernel. While it might appear that this development is limited to the perturbative regime, we show that when used within response-reformulated TDDFT, it accurately captures strong-field dynamics involving states of double-excitation character. More generally, this demonstrates how RR-TDDFT enables exchange-correlation functional developments in the response regime, which have so far been more successful than those in the non-linear regime, to be exploited for non-perturbative dynamics, thus significantly broadening their range of applications.

Numerical models of biomass particle pyrolysis focus on either the solid particle evolution or on the surrounding gas-phase dynamics, neglecting the coupled interactions between the two. This work addresses this limitation by proposing a single-grid model that fully resolves both phases without relying on sub-grid-scale correlations. The model adopts an Eulerian representation of the two-phase system, using a Volume-Of-Fluid (VOF) method to track the interface between the biomass and the surrounding gas phase. Solid-phase pyrolysis reactions are included, and a novel approach is proposed to capture the coupling between the evolution of biomass porosity and the particle shrinkage, combining different biomass conversion models into one unique framework. The anisotropic nature of the biomass particle is accounted for in this multidimensional framework. The resulting model is independent of the number and shape of the particle, and demonstrates mass conservation and numerical convergence. Extensive validation with experimental data, collected from wood particles in the centimetre scale and operating temperature between 400-700{\deg}C, shows excellent agreement in terms of mass and temperature profiles and correct volatiles trends. Predicted char yields fall within 2% error range. Shrinking profiles reveal correct trends, with a 10% average error in the final particle shape, but they also highlight the need for a better fundamental understanding of the evolution of the biomass structure. Overall, the model takes a step forward in aiding the development of sustainable pyrolysis processes. The code and simulation setups, developed within the open-source Basilisk framework, are made publicly available.

Gaussian macroscopic fluctuation theory underpins the understanding of noise in a broad class of nonequilibrium systems. We derive exact fluctuation-response relations linking the power spectral density of stationary fluctuations to the linear response of stable nonequilibrium steady states. Both of these can be determined experimentally and used to reconstruct the kernel of the linearized dynamics and the diffusion matrix, and thus any features of the Gaussian theory. We apply our theory to gene regulatory networks with negative feedback, and derive an explicit internal-external noise decomposition of the power spectral density for any networks, including cross-correlations.

Generative deep learning techniques have demonstrated an impressive capacity for tackling biomolecular design problems in recent years. Despite their high performance, however, they still suffer from a lack of interpretability and rigorous quantification of associated search spaces, which are necessary to unlock their full potential for scientific inquiry beyond efficient design. An area in which they are of particular interest is in the design of antimicrobial peptides, which are a promising class of therapeutics to treat bacterial infections. Discovering and designing such peptides is difficult because of the vast number of possible sequences and comparatively small amount of experimental information. In this work, we perform a theoretical investigation of latent Bayesian optimization for searching through peptide sequence spaces, with a focus on antimicrobial peptides. We investigate (1) whether searching through a dimensionally-reduced variant of the latent design space may facilitate optimization, (2) how organizing latent spaces by differing amounts of more and less relevant information may improve the efficiency of arriving at an optimal peptide design, and (3) the interpretability of the spaces. We find that employing a dimensionally-reduced version of the latent space is more interpretable and can be advantageous, while the use of less-relevant but more easily-computable physicochemical properties is advantageous to latent space organization in certain contexts and the use of more-relevant but sparser properties associated with the latent Bayesian objective function is advantageous in others. This work lays crucial groundwork for biophysically-motivated peptide design procedures, with an especial focus on antimicrobial peptides.

Interstellar neutral (ISN) atoms enable studies of the physical conditions in the local interstellar medium surrounding the heliosphere. ISN helium, which is the most abundant species at 1 au, is directly observed by space missions, such as Interstellar Boundary Explorer (IBEX). However, some of these atoms are ionized by solar ultraviolet radiation before reaching 1 au, producing pickup ions (PUIs). A recent analysis of IBEX data suggests that the helium photoionization rates predicted by models are underestimated by up to 40%. The Solar Wind Around Pluto (SWAP) instrument on board New Horizons enables the study of PUIs giving complementary insight into the other side of the ionization process. Our goal is to verify this increased helium ionization by determining the ionization rate of ISN helium in the heliosphere based on the SWAP observations of helium PUIs. For this purpose, we analyze SWAP data collected between 2012 and 2022, at distances 22 to 54 au from the Sun. We develop a new method for fitting model distribution functions to the observational data using the maximum likelihood method. Our approach accounts for the spacecraft's rotation and the SWAP response function, which depends on both energy and inflow direction. We estimate SWAP's efficiency for helium relative to that for hydrogen and determine the ISN helium ionization rate. We find that the photoionization rate obtained from the SWAP observations is 43% larger than the rates predicted by models, confirming the IBEX results.

This review systematically analyzes patent disclosures regarding plasmonic structures, devices, and integrated applications, highlighting the technology's capability to confine and manipulate electromagnetic energy at the nanoscale. Core materials rely on highly conductive noble metals (Gold, Silver), alongside innovative alternatives like Transparent Conducting Oxides (TCOs), Graphene, and 2D materials. These materials are engineered into diverse nanostructures that exploit Surface Plasmon Resonance (SPR) and Localized Surface Plasmon Resonance (LSPR). LSPR generates intense electromagnetic "hot spots" necessary for signal amplification (SERS) and provides extreme refractive index sensitivity for label-free detection. Key device elements include subwavelength plasmonic waveguides (hybrid and slot types) for optical signal routing. Dynamic control is achieved through active tuning mechanisms, utilizing electrical gating of charge carrier density (e.g., TCOs, Graphene) and solid-state phase change materials (e.g., VO2).

Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the need for repetitive retraining. To overcome these challenges, this paper proposes a novel framework for developing a versatile, zero-shot control policy from a large-scale offline dataset of historical PID-controlled discharges. Our approach synergistically combines Generative Adversarial Imitation Learning (GAIL) with Hilbert space representation learning to achieve dual objectives: mimicking the stable operational style of the PID data and constructing a geometrically structured latent space for efficient, goal-directed control. The resulting foundation policy can be deployed for diverse trajectory tracking tasks in a zero-shot manner without any task-specific fine-tuning. Evaluations on the HL-3 tokamak simulator demonstrate that the policy excels at precisely and stably tracking reference trajectories for key shape parameters across a range of plasma scenarios. This work presents a viable pathway toward developing highly flexible and data-efficient intelligent control systems for future fusion reactors.

Whether an antiferromagnet can host direction-robust spin-polarized transport without a conventional spin-selective band gap remains a central challenge in antiferromagnetic spintronics. Here we establish a gapless, direction-robust spin-filtering mechanism in a compensated two-dimensional altermagnetic Weyl semimetal that requires neither a spin-selective band gap nor a large velocity contrast between spin projections. Using Janus monolayer Ta$_2$TeSeO as a realistic platform, we combine symmetry analysis with first-principles calculations, full-Brillouin-zone Wannier interpolation, and semiclassical transport. Rotating the Néel vector removes a unitary-mirror constraint and shifts one Weyl-cone pair away from its parent high-symmetry line. For an in-plane Néel vector, the residual $C_{2z}\mathcal T$ symmetry forbids the independent $\sigma_y$ mass that would open a local gap, allowing the reconstructed cones to shift in momentum while remaining gapless. Breaking unitary $C_{2z}$ simultaneously lifts the energy equivalence of the remaining mirror-pinned Weyl cones. The resulting coexistence of a metallic spin-projected manifold and a low-DOS Weyl-derived manifold produces a predominantly DOS-driven conductance imbalance. At charge neutrality and 20~K, the longitudinal conductivity polarization for $\mathbf n\parallel x$ remains positive for every in-plane current direction and ranges from $76.4\%$ to $82.0\%$. The degenerate in-plane magnetic anisotropy facilitates reversible switching between symmetry-related spin-filtering states using strain or weak anisotropic fields. This Néel-vector-driven symmetry mechanism provides a general route to direction-robust gapless spin filtering in compensated altermagnets.

Parallel physical information neural networks (P-PINNs) have been widely used to solve systems with multiple coupled physical fields, such as the coupled Stokes-Darcy equations with Beavers-Joseph-Saffman (BJS) interface conditions. However, excessively high or low physical constants in partial differential equations (PDE) often lead to ill conditioned loss functions and can even cause the failure of training numerical solutions for PINNs. To solve this problem, we develop a new kind of enhanced parallel PINNs, MF-PINNs, in this article. Our MF-PINNs combines the velocity pressure form (VP) with the stream-vorticity form (SV) and add them with adjusted weights to the total loss functions. The results of numerical experiments show our MF-PINNs have successfully improved the accuracy of the streamline fields and the pressure fields when kinematic viscosity and permeability tensor range from 1e-4 to 1e4. Thus, our MF-PINNs hold promise for more chaotic PDE systems involving turbulent flows. Additionally, we also explore the best combination of the activation functions and their periodicity. And we also try to set the initial learning rate and design its decay strategies. The code and data associated with this paper are available at https://github.com/shxshx48716/MF-PINNs.git.

Results are presented for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of thin aluminum plate, as a result of pulsed, high-power laser beam excitation. The presented identification of boundary heat flux function is a part of newly-proposed laser beam profiling method and utilizes artificial neural networks trained on temperature distributions generated with the ANSYS Fluent solver. The paper focuses on the selection of the most effective neural network hyperparameters (Keras, Tensorflow) and compares the results of neural network identification with Levenberg-Marquardt method used earlier and discussed in our previous articles.

TianQin is a proposed space-based mission for gravitational wave detection, employing a constellation of three drag-free satellites in high Earth orbits to form a laser interferometric observatory. A critical technical challenge is mitigating tilt-to-length (TTL) coupling noise, which is expected to be the third dominant noise source after laser frequency and clock noises. This noise is unavoidable in the presence of the residual angular movement of satellites, movable optical subassemblies (MOSAs), and test masses (TMs), and needs to be subtracted after reducing the first two types of noises using time-delay interferometry (TDI). Previous works have shown that TTL coupling coefficients can be estimated from the null TDI channel $\zeta$ and used for noise subtraction in other combinations. However, it was found that correlated MOSA yaw jitters have a negative impact on the TTL calibration, and the effects of realistic residual angular jitters from drag-free and pointing control (DFPC) are yet to be investigated. In this paper, we use closed-loop DFPC simulations to generate more realistic jitters in the science mode and test TTL calibration capability. Our simulations reveal that rotating only one MOSA is more favorable, compared to symmetrically rotating two MOSAs, for enhancing the accuracy of TTL coefficient estimation, while employing only high-frequency data (0.1 - 1 Hz). Moreover, we propose two other methods to further improve estimation accuracy. Firstly, using different null channel combinations, such as $C_3^{14}$, enhances the least squares estimation accuracy even in the case of high correlations in MOSAs' yaw jitters. Secondly, injecting different sinusoidal artificial maneuvers to the six MOSAs also shows improvements. These methods can help TianQin to meet the 0.3 pm/Hz$^{1/2}$ requirement after the TTL noise subtraction.

High precision variational calculations in Hylleraas coordinates are presented for all singlet and triplet $P$-states of helium up to principal quantum number $n = 35$ with a uniform accuracy of 1 part in $10^{22}$ for the nonrelativistic energy. Mass polarization, relativistic and quantum electrodynamic effects are included to achieve a final accuracy of $\pm$1 kHz or better for the ionization energy of the Rydberg states of $^4$He in the range $24\le n \le 35$. The results are combined with 11 transition frequency measurements of Clausen et al. Phys. Rev. A 111, 012817 (2025) to obtain complementary measurements of the ionization energy of the $1s2s\;^3S_1$ state that do not depend on quantum defect extrapolations to the series limit. The result from the triplet spectrum yields an ionization energy of 1152 842 742.728(6) MHz, which agrees with but is larger than the experimental value by 14 $\pm$17 kHz. However, it confirms a much larger 9$\sigma$ discrepancy of $0.468\pm0.055$ MHz with the theoretical ionization energy of Patkóš et al. Phys. Rev. A 103, 042809 (2021). The results provide a test of the quantum defect extrapolation method at the level of $\pm$17 kHz. This revised version contains an additional table of spin-dependent matrix elements of the Breit interaction in the appendix for $24\le n\le 35$. (12 pages, 1 figure).

Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computations. Classical balance mechanisms require strong external inputs to sustain finite firing rates, raising concerns about their biological plausibility. Here, we investigate an alternative mechanism based on short-term synaptic depression (STD) acting on excitatory-excitatory synapses, which dynamically balances the network activity without the need of external inputs. By employing numerical simulations and theoretical investigations we characterize the dynamics of a massively coupled network made up of $N$ rate-neuron models. Depending on the synaptic strength $J_0$, the network exhibits two distinct regimes: at sufficiently small $J_0$, it converges to a homogeneous fixed point, while for sufficiently large $J_0$, it exhibits Rate Chaos. For finite networks, we observe several different routes to chaos depending on the network realization. The width of the transition region separating the homogeneous stable solution from Rate Chaos appears to shrink for increasing $N$ and eventually to vanish in the thermodynamic limit. The characterization of the Rate Chaos regime performed by employing Dynamical Mean Field approaches allow us on one side to confirm that this novel balancing mechanism is able to sustain finite irregular activity even in the thermodynamic limit, and on the other side to reveal that the balancing occurs via dynamic cancellation of the input correlations generated by the massive coupling. Our findings show that STD provides an intrinsic self-regulating mechanism for balanced networks, sustaining irregular yet stable activity without the need of biologically unrealistic inputs. This work extends the balanced network paradigm, offering insights into how cortical circuits could maintain robust dynamics via synaptic adaptation.

The optimization of neural wave functions in variational Monte Carlo crucially relies on a robust convergence criterion. While the energy variance is theoretically a definitive measure, its practical application as a primary convergence criterion has been underexplored. In this work, we develop a lightweight, general-purpose solver that utilizes the energy variance as a convergence criterion. We apply it to several systems-including the harmonic oscillator, hydrogen atom, and charmonium hadron-for validating the variance as a reliable diagnostic, and using a empirical threshold $10^{-3}$ as the energy variance convergence values for performing rapid parameter scans to enable preliminary physical verification. To clarify the scope of our approach, we derive an inequality that delineates the limitations of variance-based optimization in nodal systems. Despite these limitations, the energy variance proves to be a highly valuable tool, guiding our solver to efficient and reliable results across a range of quantum problems.

High costs and uncertainties make subsurface decision-making challenging, as acquiring new data is rarely scalable. Embedding geological knowledge directly into predictive models offers a valuable alternative. A joint approach enables just that: process-based models that mimic geological processes can help train generative models that make predictions more efficiently. This study explores whether a generative adversarial network (GAN) - a type of deep-learning algorithm for generative modeling - trained to produce fluvial deposits can be inverted to match well and seismic data. Four inversion approaches applied to three test samples with 4, 8, and 20 wells struggled to match these well data, especially as the well number increased or as the test sample diverged from the training data. The key bottleneck lies in the GAN's latent representation: it is entangled, so samples with similar sedimentological features are not necessarily close in the latent space. Label conditioning or latent overparameterization can partially disentangle the latent space during training, although not yet sufficiently for a successful inversion. Fine-tuning the GAN to restructure the latent space locally reduces mismatches to acceptable levels for all test cases, with and without seismic data. But this approach depends on an initial, partially successful inversion step, which influences the quality and diversity of the final samples. Overall, GANs can already handle the tasks required for their integration into geomodeling workflows. We still need to further assess their robustness, and how to best leverage them in support of geological interpretation.

Light-matter coupled Hamiltonians are central to cavity materials engineering and polaritonic chemistry, but are challenging to simulate with classical hardware due to the scaling of the Hilbert space with the number of quantum photon modes and matter complexity. Leveraging the fact that quantum computers naturally represent photonic modes efficiently, we present a novel approach to simulate quantum-electrodynamical (QED) systems on near-term quantum hardware. After developing the bosonic and mixed operators in the Qiskit Nature framework, we employ them to simulate a first-order Trotterized Hamiltonian for a spontaneous-emission problem of a two-level system in an optical cavity. We find that using a standing-waves photonic basis approach leads to fidelity issues due to hardware connectivity constraints and two-qubits gates errors. Hence, we propose using a localized photonic basis approach that enforces nearest-neighbor couplings, thanks to which we can map the Hamiltonian as a 1D qubit chain. We significantly reduce the noise and, by applying the zero-noise extrapolation error mitigation technique, we recover the accurate quantum dynamics. Finally, we also show that this approach is resilient when relaxing the 1D qubit chain approximation.

Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply Explainable Artificial Intelligence (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), to interpret the PhaseNet model's decisions and improve its reliability. Grad-CAM highlights that the network's attention aligns with P- and S-wave arrivals. SHAP values quantify feature contributions, confirming that vertical-component amplitudes drive P-phase picks while horizontal components dominate S-phase picks, consistent with geophysical principles. Leveraging these insights, we introduce a SHAP-gated inference scheme that combines the model's output with an explanation-based metric to reduce errors. On a test set of 9,000 waveforms, the SHAP-gated model achieved an F1-score of 0.98 (precision 0.99, recall 0.97), outperforming the baseline PhaseNet (F1-score 0.97) and demonstrating enhanced robustness to noise. These results show that XAI can not only interpret deep learning models but also directly enhance their performance, providing a template for building trust in automated seismic detectors. The implementation and scripts used in this study will be publicly available at https://github.com/ayratabd/xAI_PhaseNet.

Muscle force sharing is typically resolved by minimizing a specific objective function to approximate neural control strategies. An inverse optimal control approach was applied to identify the "best" objective function, among a positive linear combination of basis objective functions, associated with the gait of two post-stroke males, one high-functioning (subject S1) and one low-functioning (subject S2). It was found that the "best" objective function is subject- and leg-specific. No single function works universally well, yet the best options are usually differently weighted combinations of muscle activation- and power-minimization. Subject-specific inverse optimal control models performed best on their respective limbs (RMSE 178/213 N, CC 0.71/0.61 for non-paretic and paretic legs of S1; RMSE 205/165 N, CC 0.88/0.85 for respective legs of S2), but cross-subject generalization was poor, particularly for paretic legs. Moreover, minimizing the root mean square of muscle power emerged as important for paretic limbs, while minimizing activation-based functions dominated for non-paretic limbs. This may suggest different neural control strategies between affected and unaffected sides, possibly altered by the presence of spasticity. Among the 15 considered objective functions commonly used in inverse dynamics-based computations, the root mean square of muscle power was the only one explicitly incorporating muscle velocity, leading to a possible model for spasticity in the paretic limbs. Although this objective function has been rarely used, it may be relevant for modeling pathological gait, such as post-stroke gait.

Nanoporous membranes, leveraging their high-throughput characteristics, have been widely applied in fields such as molecular separation and energy conversion. Due to interpore interactions, besides the applied voltage and solution environment, the ion transport properties in porous membranes are influenced by the pore number and spacing. Here, to understand and control the transport properties of nanopore arrays, we systematically investigate the ion transport characteristics through membranes with different charge properties, pore numbers, and interpore distances. Using numerical simulations, we analyzed local ionic concentrations and electric potential in nanopore arrays containing nanopores with uniformly charged walls as well as unipolar diodes i.e., pores containing a junction between a charged zone and a neutral zone, and showed significant ion concentration polarization (ICP) for all studied cases. As the number of pores increased and the interpore spacing decreased, the enhanced interpore interactions through ICP led to a greater deviation of the total ionic current from the linear superposition of single-pore currents. Conversely, in bipolar nanopores whose walls contain a junction between positively and negatively charged zones ICP becomes negligible, and interpore interactions are substantially reduced. Furthermore, for membranes with various charge properties, the total current through nanopore arrays presents different quantitative dependence on the pore number under varying pore spacings. Our findings clarify the mechanism of interpore interactions in modulating ion transport through porous membranes, providing critical insights for designing nanofluidic devices based on nanopore arrays, such as nanopore-array sensors.

Sedimentary rocks often form the upper layers or the entire target rocks in impact events. Thermodynamic properties of sedimentary rocks related to porosity and water saturation affect the process of impact crater formation. The heterogeneous distribution of sedimentary facies can complicate the development and distribution of shock effects, especially in numerical modeling. This work focuses on the shock thermodynamic properties of carbonate rocks with differing porosity textures (e.g., isolated pores, interstitial porosity, elongated pores) and water saturation levels. Using mesoscale numerical modeling, we found that water saturation reduces shock temperatures compared to those in dry, porous carbonate rocks. The orientation of elongated pores and porosity lineations influences the shock temperature distribution and rock deformation at angles of 50-90{\deg} to the shock front. Additionally, due to complex shock wave interactions, interstitial porosity is key in creating temperature zonations around larger grains.

Ionic current rectification (ICR) induced by electroosmotic flow (EOF) under concentration gradients can find many applications in micro/nanofluidic sensing and ionic circuits. Here, we focused on the cases with micropores of moderate length-diameter ratios, through experimental research and systematical simulations, the EOF-induced ICR was found to exhibit voltage-dependent ratios. In the considered cases with a weak EOF or strong ionic diffusion, a large deviation appears between the ion concentration inside the micropore and the bulk value, which fails the prediction by solution conductivity gradients. Based on our simulation results, effective equations were developed for the theoretical description of ion concentration distributions along the micropore axis under coupled concentration gradient and electric field. With the predicted ion distributions inside micropores, the ICR ratio can be conveniently calculated with the derived electrical resistance of the microfluidic system, which applies to micropores of 200 to 1000 nm in diameter. Because the surface charge density is the only unknown input parameter, our developed equations can be used to evaluate the surface charge density of micropores with the measured EOF-induced ICR ratio under concentration gradients.

Nanofluidic memristors have demonstrated great potential for neuromorphic system applications with the advantages of low energy consumption and excellent biocompatibility. Here, an effective way is developed to regulate the memristive behavior of conical nanopores by leveraging the reversible formation and dissolution of nanoprecipitates induced by ion enrichment and depletion in nanopores under opposite voltages. Through the interplay between precipitation dynamics at the pore tip and the ion enrichment/depletion inside the nanopore, conical nanopores exhibit pronounced current hysteresis loops in the presence of CaHPO4, a slightly soluble inorganic salt. The memristive characteristics are found to be strongly dependent on the concentration of CaHPO4, besides the applied voltage amplitude and scan rate. Under the stimulation of pulse voltages, ionic current demonstrates stable learning and forgetting processes with robust switching stability and effective reset capability, which is similar to the short-term plasticity characteristics of biological synapses. Our research may provide a straightforward and tunable approach for the design of nanofluidic memristors.

The $O(N)$ stochastic propagation method, which relies on the numerical solution of the time-dependent Schrödinger equation using random initial states, is widely used in large-scale first-principles calculations. In this work, we eliminate the conventional sequential computation of intermediate states by introducing a concurrent strategy that minimizes information redundancy. The new method, in its state-, moment-, and energy-based implementations, not only surpasses the time step constraint of sequential propagation but also maintains precision within the framework of the Nyquist-Shannon sampling theorem. Systematic benchmarking on one billion atoms within the tight-binding model demonstrates that our new concurrent method achieves up to an order-of-magnitude speedup, enabling the rapid computation of a wide range of electronic, optical, and transport properties. This performance breakthrough offers valuable insights for enhancing other time-propagation algorithms, including those employed in large-scale stochastic density functional theory.

The following model is studied analytically and numerically: point particles with masses $m,\mu,m, \dots$ ($m\geq\mu$) are distributed over the positive half-axis. Their dynamics is initiated by giving a positive velocity to the particle located at the origin; in its course the particles undergo elastic collisions. We show that, for certain values of $m/\mu$, starting from the initial state where the particles are equidistant the system evolves in a hydrodynamic way: (i) the rightmost particle (blast front) moves as $t^{\delta}$ with $\delta < 1$; (ii) recoiled particles behind the front enter the negative half-axis; (iii) the splatter – the particles with locations $x\leq 0$ – moves in the ballistic way and eventually takes over the whole energy of the system. These results agree with those obtained in S. Chakraborti et al, SciPost Phys. 2022, 13, 074, for $m/\mu=2$ and random initial particle positions. At the same time, we explicitly found the collection of positive numbers $\{\mathcal{M}_i, i \in \mathbf{N} \}$ such that, for $m/\mu = \mathcal{M}_i$, $i\leq 700$, the following holds: (a) the splatter is absent; (b) the number of simultaneously moving particles is at most three; (c) the blast front moves in the ballistic way. However, if, similarly as in S. Chakraborti et al, the particle positions are sampled from a uniformly distributed ensemble, for $m/\mu = \mathcal{M}_i$ the system evolves in a hydrodynamic way.