Papers

This work presents a survey of mechanical models describing van der Waals interactions between 2D materials, encompassing both continuous elastomer-like materials and discrete (crystalline) 2D materials such as graphene. These interactions give rise to a range of physical phenomena, including contact instabilities, Moiré patterns, surface reconstructions, and superlubricity. The underlying contact forces follow from the variation of an interfacial interaction potential. The presentation first discusses normal contact models, and then tangential contact models. Both atomistic and continuum approaches are considered. In addition, the influence of external loading and changes in length scale on the ground state configuration and frictional contact behavior are analyzed. A particular emphasis is placed on discussing strategies that reduce computational cost in multiscale modeling.

The possibility that evolutionary forces – together with a few fundamental factors such as thermodynamic constraints, specific computational features enabling information processing, and ecological processes – might constrain the logic of living systems is tantalizing. However, it is often overlooked that any practical implementation of such a logic requires complementary circuitry that, in biological systems, happens through complex networks of genetic regulation, metabolic reactions, cellular signalling, communication, social and eusocial non-trivial organization. We review and discuss how circuitries are not merely passive structures, but active agents of change that, by means of hierarchical and modular organization, are able to enhance and catalyze the evolution of evolvability. Using statistical physics to analyze the role of non-trivial topologies in major evolutionary transitions, we show that biological innovations are related to deviation from trivial structures and (thermo)dynamic equilibria. We argue that sparse heterogeneous networks such as hierarchical modular, which are ubiquitously observed in nature, are favored in terms of the trade-off between energetic costs for redundancy, error-correction and maintainance. We identify three main features – namely, interconnectivity, plasticity and interdependency – pointing towards a unifying framework for modeling the phenomenology, discussing them in terms of dynamical systems theory, non-equilibrium thermodynamics and evolutionary dynamics. Within this unified picture, we also show that slow evolutionary dynamics is an emergent phenomenon governed by the replicator-mutator equation as the direct consequence of a constrained variational nonequilibrium process. Overall, this work highlights how dynamical systems theory and nonequilibrium thermodynamics provide powerful analytical techniques to study biological complexity.

We aim to answer the following question: is the complexity of numerically solving the Poisson equation increasing or decreasing for very large simulations of incompressible flows? Physical and numerical arguments are combined to derive power-law scalings at very high Reynolds numbers. A theoretical convergence analysis for both Jacobi and multigrid solvers defines a two-dimensional phase space divided into two regions depending on whether the number of solver iterations tends to decrease or increase with the Reynolds number. Numerical results indicate that, for Navier-Stokes turbulence, the complexity decreases with increasing Reynolds number, whereas for the one-dimensional Burgers equation it follows the opposite trend. The proposed theoretical framework thus provides a unified perspective on how solver convergence scales with the Reynolds number and offers valuable guidance for the development of next-generation preconditioning and multigrid strategies for extreme-scale simulations.

This work presents a novel combination of two well-established techniques: fringe projection profilometry (FPP) and particle image velocimetry (PIV). Despite seemingly conflicting requirements—FPP requires an opaque surface to project onto, while PIV requires a transparent fluid—both requirements are met by adding low concentrations (4–25 mg/L) of fluorescein dye to the water. This dye strongly absorbs the blue light projected onto the surface for FPP while remaining nearly transparent to the green PIV laser, achieving simultaneous opacity and transparency depending on wavelength. A set of three optical filters suppresses fluorescence-induced noise in the PIV images and specular reflections in the profilometry images, enabling clean simultaneous acquisition. Validated against point laser-induced fluorescence measurements of the surface, the method achieves a mean absolute error in surface elevation of 18 $\mu$m at a dye concentration of 12 mg/L, above which further increases in concentration yield little improvement. PIV correlation values remain robust up to 20 mg/L. The technique resolves surface features differing in amplitude by two orders of magnitude and is demonstrated on flow past a cylinder interacting with surface waves and on droplet impacts on a quiescent surface.

This study presents a comparative numerical analysis of Gold (Au) and high-index semiconductor nanoparticles for refractive index sensing in the visible range. While Au nanoparticles demonstrate high sensitivity ($\approx 150$ nm per refractive index unit), their performance is constrained by ohmic losses. In contrast, high-index dielectrics are shown to exhibit comparable extinction efficiencies driven exclusively by scattering, thereby minimizing thermal losses. Multipolar decomposition reveals that semiconductors support simultaneous electric and magnetic Mie resonances, the interference of which enables directional scattering phenomena unattainable in small metallic particles. These findings suggest that high-index nanostructures offer a robust, low-loss alternative to plasmonics for advanced sensing applications.

This study introduces amangkurat, an open-source Python library designed for the robust numerical simulation of relativistic scalar field dynamics governed by the nonlinear Klein-Gordon equation in $(1+1)$D spacetime. The software implements a hybrid computational strategy that couples Fourier pseudo-spectral spatial discretization with a symplectic Størmer-Verlet temporal integrator, ensuring both exponential spatial convergence for smooth solutions and long-term preservation of Hamiltonian structure. To optimize performance, the solver incorporates adaptive timestepping based on Courant-Friedrichs-Lewy (CFL) stability criteria and utilizes Just-In-Time (JIT) compilation for parallelized force computation. The library's capabilities are validated across four canonical physical regimes: dispersive linear wave propagation, static topological kink preservation in phi-fourth theory, integrable breather dynamics in the sine-Gordon model, and non-integrable kink-antikink collisions. Beyond standard numerical validation, this work establishes a multi-faceted analysis framework employing information-theoretic entropy metrics (Shannon, Rényi, and Tsallis), kernel density estimation, and phase space reconstruction to quantify the distinct phenomenological signatures of these regimes. Statistical hypothesis testing confirms that these scenarios represent statistically distinguishable dynamical populations. Benchmarks on standard workstation hardware demonstrate that the implementation achieves high computational efficiency, making it a viable platform for exploratory research and education in nonlinear field theory.

We present 1d-qt-ideal-solver, an open-source Python library for simulating one-dimensional quantum tunneling dynamics under idealized coherent conditions. The solver implements the split-operator method with second-order Trotter-Suzuki factorization, utilizing FFT-based spectral differentiation for the kinetic operator and complex absorbing potentials to eliminate boundary reflections. Numba just-in-time compilation achieves performance comparable to compiled languages while maintaining code accessibility. We validate the implementation through two canonical test cases: rectangular barriers modeling field emission through oxide layers and Gaussian barriers approximating scanning tunneling microscopy interactions. Both simulations achieve exceptional numerical fidelity with machine-precision energy conservation over femtosecond-scale propagation. Comparative analysis employing information-theoretic measures and nonparametric hypothesis tests reveals that rectangular barriers exhibit moderately higher transmission coefficients than Gaussian barriers in the over-barrier regime, though Jensen-Shannon divergence analysis indicates modest practical differences between geometries. Phase space analysis confirms complete decoherence when averaged over spatial-temporal domains. The library name reflects its scope: idealized signifies deliberate exclusion of dissipation, environmental coupling, and many-body interactions, limiting applicability to qualitative insights and pedagogical purposes rather than quantitative experimental predictions. Distributed under the MIT License, the library provides a deployable tool for teaching quantum mechanics and preliminary exploration of tunneling dynamics.

The neutral-current (NC) nonstandard interactions (NSIs) of neutrinos with fermions can modify the flavor oscillations of atmospheric neutrinos as they propagate through the Earth. We present constraints on the NC-NSI parameters $\varepsilon_{\mu\tau}$ and $\varepsilon_{\tau\tau}-\varepsilon_{\mu\mu}$ (one at a time) using a high-purity sample of $\nu_{\mu}$ charged-current atmospheric neutrino events collected by IceCube DeepCore over 7.5 years of livetime. These two parameters significantly affect the $\nu_\mu$ disappearance channel for which this golden event sample is optimized by IceCube Collaboration. The best fit to this dataset is consistent with no NSI hypothesis, and we place the stringent constraints $-\,0.0094 < \varepsilon_{\mu\tau} < 0.0079$ and $-\,0.031 < \varepsilon_{\tau\tau}-\varepsilon_{\mu\mu} < 0.029$ at the 90% confidence level, assuming normal mass ordering.

Since its inception, quantum theory has been the subject of fierce interpretive controversy, which persists to this day. Disputed topics include the basic ontology and dynamics of the theory, the role (if any) of measurement, the meaning of probability, and the issue of non-locality. But there is yet another problem that has been largely ignored: how the theory makes contact with observational data. The problem is endemic to physics, and was discussed by Einstein in several places. In this essay, I discuss Einstein's general approach, how it applied to some quantum-mechanical phenomena, and why a central aspect of the solution might lead to novel and important new predictions.

The fast-ion (FI) on turbulent transport is one of the key topics of magnetic confinement fusion. This work focus on the impact of FI pressure gradients on turbulence in a high-e̱ta} plasma scenario using gyrokinetic simulations. Linear analyses reveal that FIs strongly stabilize ion temperature gradient (ITG) modes via the thermal-ion dilution, while their influence on trapped electron modes (TEMs) is minimal. At elevated FI pressure gradients, a transition to a FI-driven BAE (FI-BAE) regime occurs, as evidenced by mode structure and frequency alignment within the Alfvénic gap. Electron e̱ta} scans further demonstrate the emergence of kinetic ballooning modes (KBMs) at higher e̱ta}, whereas an ITG-TEM hybrid turbulence dominates near experimental e̱ta} values. Nonlinear simulations show that moderate FI pressure suppresses transport via zonal flow (ZF) shear, whereas strong FI drive weakens ZFs and enhances transport by destabilizing FI-BAEs. These results highlight the dual role of FIs in regulating turbulence and offer insight into multiscale transport physics relevant for high-performance plasmas.

Most use cases of reconfigurable antennas require an accurate forward model mapping configuration to radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting such a model from a numerical simulation with multiple challenges. First, the cost of accurately simulating an intricate and electrically large DMA architecture might be prohibitive. Second, the model-reality mismatch due to fabrication inaccuracies might be substantial, especially at higher frequencies and for DMA architectures leveraging strong inter-element mutual coupling (MC) to maximize their tunability. These considerations motivate an experimental parameter estimation for DMA forward models. The main challenge lies in the forward model's non-linearity due to inter-element MC. Multiport network theory (MNT) can accurately capture MC but the MC parameters cannot be measured directly. In this article, we demonstrate the experimental estimation of a high-accuracy proxy MNT model for a 19-GHz DMA with 7 feeds and 96 elements, where all feeds and elements are strongly coupled via a chaotic cavity. For a given DMA configuration and excitation, our proxy MNT model predicts the reflected field at the feeds and the radiated field with accuracies of 40.3 dB and 37.7 dB, respectively. A simpler, MC-unaware benchmark model only achieves 2.6 dB and 3.3 dB, respectively. We systematically examine the influence of the number of feeds and measured DMA configurations on the model accuracy, motivating the inclusion of "auxiliary calibration feeds" to facilitate the parameter estimation when the intended DMA operation is limited to a single feed. Finally, we measure DMA configurations optimized based on our proxy MNT model.

Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generating diverse low-energy conformers from molecular graphs remains expensive with traditional physics-based pipelines. Existing learning-based approaches remain fragmented: generative models capture conformational diversity but often lack reliable energy calibration, whereas deterministic predictors focus on a single structure and fail to represent ensemble variability. Here we introduce EnFlow, to our knowledge, the first energy-guided generative framework that couples flow-based conformer generation with explicit energy landscape modeling for joint conformational ensemble generation and ground-state identification. By integrating generative dynamics with a learned energy model, EnFlow guides sampling toward low-energy regions of the conformational landscape, improving structural fidelity under extremely few sampling steps while enabling energy-based ranking of generated conformations. Experiments on GEOM-QM9 and GEOM-Drugs show that EnFlow achieves strong performance in conformer generation and ground-state identification while requiring only 1–2 ODE sampling steps. Single-point GFN2-xTB evaluations further show that the learned energy scores preserve physically meaningful energetic rankings of generated conformations. These results support explicit energy landscape modeling as an effective strategy for low-energy molecular structure discovery through joint modeling of conformational ensembles and their associated energies.

The present study investigates the attacker-defender (AD) model proposed by Brink et al. (2023), a motion model that describes the interactions between a ball carrier (attacker) and the nearest defender during ball possession. The model is based on the equations of motion for both players, incorporating resistance, goal-oriented force, and opponent-oriented force. It generates trajectories based on physically interpretable parameters. Although the AD model reproduces real dribbling trajectories well, previous studies have explored only a limited range of parameter values and relied on relatively small datasets. This study aims to (1) enhance parameter optimization by solving the AD model for one player with the opponent's actual trajectory fixed, (2) validate the model's applicability to a large dataset from 306 J1 League matches, and (3) demonstrate distinct playing styles of attackers and defenders based on the full range of optimized parameters.

Nanoplastics (NPs) are increasingly detected in human blood and organs at concentrations reaching hundreds to thousands of parts per million, yet no quantitative framework has linked short-term experimental uptake kinetics to long-term, organ-specific accumulation. Here we analytically review the most reliable uptake and depuration datasets available in teleost fish using a sequential two-compartment toxicokinetic model that distinguishes systemic circulation from tissue-level retention. While anomalous, non-Markovian transport is expected at microscopic scales, we show – through an explicit theoretic analysis on minimal information – that such formulations are not identifiable with existing data. Allowing unresolved early-time dynamics to be absorbed into effective, non-zero initial conditions yields an emergent Markovian description that is maximally informative and consistent across species, organs, particle sizes, and exposure levels. When expressed in normalized variables, uptake dynamics collapse onto a universal trajectory governed by a single dimensionless parameter, the systemic excretion capacity, which is generically small under experimental conditions. The resulting scale-free framework reveals systematic power-law dependencies of enrichment and retention times on ambient concentration, particle size, and body mass. Exploiting this structure, we examine the consistency of extrapolations to humans and show that reported organ burdens – particularly in the brain – are quantitatively compatible with inefficient systemic clearance and strong lipid-driven partitioning. At steady state, human tissue concentrations follow a robust approximate cubic scaling with lipid fraction, identifying lipid content as the dominant and mechanistically interpretable determinant of chronic nanoplastic accumulation.

We propose the feed-forward perturbation-based nonlinearity compensation method using the received signal, which outperforms conventional decision-based ones and eliminates the need for decision feedback. Additionally, combining half-half dispersion compensation with pre-/ post-compensation yields further performance gains.

Using the photon-ion merged-beams technique at the PETRA\,III synchrotron light source, we have measured cross sections for double and up to tenfold photoionization of La$^{+}$ ions by a single photon in the energy range 820–1400~eV, where resonances and thresholds occur that are associated with the excitation or ionization of one $M$-shell electron. These cross sections represent experimental benchmark data for the further development of quantum theoretical methods, which will have to provide the bulk of the atomic data required for the modeling of nonequilibrium plasmas such as kilonovae. In the present work, we have upgraded the Jena Atomic Calculator (JAC) and pushed the state-of-the-art of quantum calculations for heavy many-electron systems to new limits. In particular, we have performed large-scale calculations of the La$^+$ photoabsorption cross section and of the deexcitation cascades, which set in after the initial creation of a $3d$ hole. Our theoretical results largely agree with our experimental findings. However, our theoretical product-ion charge state distributions are somewhat narrower than the experimental ones, which is most probably due to the simplifications necessary to keep the cascade calculations tractable.

Solving interacting multi-particle systems is a central challenge in quantum chemistry and condensed matter physics. In this work, we investigate the computation of ground states and ground-state energies for the He-H+ and H2O molecules using quantum computing techniques. We employ the variational quantum eigensolver (VQE), implemented both on a quantum computer simulator and on an IBM quantum device. The resulting energies are benchmarked against exact ground-state energies obtained via classical methods. Simulations of the H2O molecule were performed on Nottingham's High Performance Computing (HPC) facilities.

Collisional gates in optical superlattices have recently achieved record fidelities, but their operation times are typically limited by tunneling. Here we propose and analyze an alternative route to a fast $\sqrt{\mathrm{SWAP}}$ gate for two fermionic atoms in an optical superlattice based on optimized, time-dependent control of the short and long lattice depths. The gate is implemented by transiently releasing the atoms into a quasi-harmonic confinement centered between the two sites. With an appropriately chosen contact interaction strength, a controlled collision accumulates the exchange phase required for $\sqrt{\mathrm{SWAP}}$ and generates entanglement. We employ a continuum, time-dependent Schrödinger-equation simulation that goes beyond a two-site Fermi–Hubbard description and benchmark it against experimentally implemented tunneling-based protocols, reproducing the observed single-particle tunneling and spin-exchange dynamics. For experimentally accessible lattice depths, we find that the proposed gate operates in $\sim 21\,\mu\mathrm{s}$, more than an order of magnitude faster than tunneling-based implementations, while achieving fidelities $\gtrsim 99\%$. We further analyze sensitivity to lattice-depth variations and show that a composite sequence improves robustness. Our results establish fast, collision-mediated entangling gates in superlattices as a promising building block for scalable neutral-atom quantum computation.

The phenomenal advances in large language models (LLMs) and other foundation models over the past few years have been based on optimizing large-scale transformer models on the surprisingly simple objective of minimizing next-token prediction loss, a form of predictive coding that is also the backbone of an increasingly popular model of brain function in neuroscience and cognitive science. However, current foundation models ignore three other important components of state-of-the-art predictive coding models: tight integration of actions with generative models, hierarchical compositional structure, and episodic memory. We propose that to achieve safe, interpretable, energy-efficient, and human-like AI, foundation models should integrate actions, at multiple scales of abstraction, with a compositional generative architecture and episodic memory. We present recent evidence from neuroscience and cognitive science on the importance of each of these components. We describe how the addition of these missing components to foundation models could help address some of their current deficiencies: hallucinations and superficial understanding of concepts due to lack of grounding, a missing sense of agency/responsibility due to lack of control, threats to safety and trustworthiness due to lack of interpretability, and energy inefficiency. We compare our proposal to current trends, such as adding chain-of-thought (CoT) reasoning and retrieval-augmented generation (RAG) to foundation models, and discuss new ways of augmenting these models with brain-inspired components. We conclude by arguing that a rekindling of the historically fruitful exchange of ideas between brain science and AI will help pave the way towards safe and interpretable human-centered AI.

Quantum computing holds potential for accelerating the simulation of fluid dynamics. However, hardware noise in the noisy intermediate-scale quantum era significantly distorts simulation accuracy. Although error magnitudes are frequently quantified, the specific physical effects of quantum noise on flow simulation results remain largely uncharacterized. We investigate the influence of gate noise on the quantum simulation of one-dimensional scalar convection. By employing a quantum spectral algorithm where ideal time advancement affects only Fourier phases, we isolate and analyze noise-induced artifacts in spectral magnitudes. We derive a theoretical transition matrix based on Hamming distances between computational basis states to predict spectral decay, and then validate this model against density-matrix simulations and experiments on a superconducting quantum processor. Furthermore, using data-driven sparse regression, we demonstrate that quantum noise manifests in the effective partial differential equation primarily as artificial diffusion and nonlinear source terms. These findings suggest that quantum errors can be modeled as deterministic physical terms rather than purely stochastic perturbations.

The intersection of two orthogonal cylinders represents a classical problem in computational geometry with direct applications to engineering design, manufacturing, and numerical simulation. While analytical solutions exist for the fully intersecting case, the Steinmetz solid, partial intersections with arbitrary depth ratios require numerical methods or approximations. This work presents general integral expressions for both the intersection volume and surface area as explicit functions of the intersection depth. Accompanying these exact formulations are empirical approximation functions, which provide closed-form evaluations with relative errors below 15% across the full range of intersection depth. Validation against Quasi-Monte Carlo simulation confirms the accuracy of both the analytical and approximate solutions.

This paper argues that, where deterministic and indeterministic formulations agree on empirically corroborated structure while differing in their completion of structure that remains empirically underdetermined, features invariant across these formulations warrant ontological commitment, whereas any warrant attaching to features that vary across them is categorically more provisional. Because deterministic and indeterministic completions are always formally available, while additional grounds for preferring particular completions are contingent and defeasible, empirically corroborated structure alone cannot determine whether reality is fundamentally deterministic or indeterministic, or whether that very distinction belongs only to specific representations of it. On this basis, I advance a model-invariance criterion according to which structural features are candidates for realist commitment insofar as they are empirically corroborated within a regime and preserved across the admissible formulations of that structure. I show how such invariant content can be characterised in mathematically intelligible ways, yielding a fallibilist form of structural realism grounded in model-invariance rather than in the specific form of any given mathematical representation. Features such as conservation relations, invariant measures, symmetries, and probability or correlation structures over observable histories may satisfy this criterion. By contrast, the localisation of modal selection—whether in fine-grained initial conditions, stochastic outcomes, or state-update and collapse mechanisms—may vary across empirically admissible formulations and therefore constitute a gauge-like representational freedom. This framework offers a realist ontology for modern physics that combines empirical openness with resistance to metaphysical overreach.

We investigate the Ferrofluid Drop Targeting (FDT) for the treatment of the Retinal Detachment (RD), considering, for the first time, the real 3D geometry of an eye and magnets configurations as well as the viscoelastic rheology of the medium, i.e., the Vitreous Humor (VH). A Front-Tracking Method (FTM) is extended to handle a general 3D unstructured Eulerian grid and strong wall effects. The challenges include the accuracy and robustness of the solver when the drop spreads on the retina under the effect of a magnetic field, which necessitates the design of a multi-region Eulerian grid and defining a threshold distance between the front and wall, along with the choice of an effective front smoothing and volume correction FTM sub-algorithms near the walls. After model validations, the effect of different design parameters on important objectives, such as the travel time, settling time, retinal coverage area, and impact compressive stress, are studied. The results reveal that, in addition to the magnetic Bond number, the ratio of the drop-to-VH magnetic permeabilities plays a key role in the terminal shape parameters, like the retinal coverage. Additionally, simultaneously increasing these two parameters, significantly increase the total FDT force, coverage area, and stress concentration, while decreasing the drop-VH surface tension can mitigate the stress concentration on the retina.

Quantum entanglement is commonly assumed to be fragile at ambient temperature and over macroscopic distances, where thermal noise and dissipation are expected to rapidly suppress nonclassical correlations. Here we show that this intuition fails for collective quantum modes whose dynamics is governed by reduced open-system channels rather than by microscopic thermal equilibrium. For two spatially separated collective modes, we derive an exact entanglement boundary based on the positivity of the partial transpose, valid in the symmetric resonant limit. From this result we obtain an explicit minimum collective fluctuation amplitude, expressed entirely in measurable noise, bandwidth, dissipation, and distance-dependent coupling parameters, required to sustain steady-state entanglement at finite temperature. We further show that large collective occupation suppresses but does not eliminate quantum phase diffusion, so the steady state remains phase symmetric and does not collapse to a classical mean-field despite macroscopic signal amplitudes. Stochastic simulations of the reduced open-system dynamics, together with matched classical correlated-noise null models analyzed through an identical pipeline, confirm that entanglement witnesses are violated only in the quantum regime. Our results establish a minimal, platform-independent framework connecting collective-mode dynamics, noise injection, distance, and operational certification of macroscopic entanglement.

Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity, subjectivity, and limited adaptability to emerging interdisciplinary fields. Data-driven alternatives based on citation networks show promise but lack rigorous, external validation against the semantic content of scientific literature. Here, we propose a novel quantitative framework that leverages classification tasks to evaluate the effectiveness of journal classification schemes. Using over 23 million paper abstracts, we demonstrate that labels derived from k-means clustering on Periodical2Vec (P2V)–a periodical embedding learned from paper-level citations–yield significantly higher classification performance than both Scopus and other data-driven baselines (e.g., citation, co-citation, and Node2Vec variants). By comparing journal partitions across classification schemes, two structural patterns emerge on the map of science: (1) the reorganization of disciplinary boundaries–splitting overly broad categories (e.g., "Medicine" into "Oncology", "Cardiology", and other specialties) while merging artificially fragmented ones (e.g., "Chemistry" and "Chemical Engineering"); and (2) the identification of coherent interdisciplinary clusters–such as "Biomedical Engineering", "Medical Ethics", and "Information Management"–that are dispersed across multiple categories but unified in citation space. These findings underscore that citation-derived periodical embeddings not only outperform traditional taxonomies in predictive validity but also offer a dynamic, fine-grained map of science that better reflects both the specialization and interdisciplinarity inherent in contemporary research.

Traditionally, during the monsoon season, more rainfall is received along the Western Ghats, the Northern Gangetic plains, the central belt, and northeast India. However, recently, there has been a shift in this canonical monsoon rainfall pattern on the monthly to seasonal scale. In this study, we quantify an east-west asymmetric trend in monthly to seasonal rainfall due to the increased rainfall over the northwestern part of the country. An Empirical Orthogonal Function (EOF) analysis has been performed to understand the spatial and temporal variation of the monsoon. EOF mode 3 shows such a distinct east-west dipole pattern, highlighting the existence of a modal feature representing the recent trend in the rainfall distribution. The physical nature of this mode is also established. The regression pattern of the rainfall anomalies to the Webster-Yang Index (Webster and Yang, 1992) exhibits a similar east-west pattern that further confirms the physical existence of this east-west rainfall modal dipole pattern. Since rainfall across the northwest is directly linked to the Arabian Sea and rainfall over the eastern region to the Bay of Bengal, the characteristics of these two regions are studied separately. Over the Arabian Sea, there is a significant negative trend in the Sea Level Pressure (SLP) anomalies and an increase in the specific humidity, causing greater moisture convergence. In contrast, over the Bay of Bengal, the SLP shows an increasing trend. The SST warming over the Arabian Sea is higher than that of the Bay of Bengal. Further, while investigating the zonal wind(u) at 850hPa, it shows an increasing trend along the northern branch that is more directed towards the northwestern part of the country. These factors together create dynamically favorable conditions for enhanced convection and thus receive more rainfall across the northwest compared to the northeast India.

Organoids are three-dimensional (3D) in vitro models for studying tissue development, disease progression, and physiological responses. Holotomography (HT) enables long-term, label-free imaging of live organoids by reconstructing volumetric refractive-index (RI) maps, but quantitative analysis is limited by the missing-cone artifact, which introduces anisotropic resolution and axial distortion. Here, we present a quantitative analysis framework that addresses the missing-cone problem at the level of image representation rather than reconstruction. We introduce morphology-preserving holotomography (MP-HT), a torus-shaped spatial filtering strategy that emphasizes high-spatial-frequency RI texture while suppressing low-frequency components most susceptible to missing-cone-induced distortion. Based on MP-HT, we develop a 3D segmentation pipeline for robust separation of epithelial and luminal structures, together with a model-based RI quantification approach that incorporates the system point spread function to enable morphology-independent estimation of dry-mass density and total dry mass. We apply the framework to long-term imaging of live hepatic organoids undergoing expansion, collapse, and fusion. The results demonstrate consistent segmentation across diverse geometries and reveal coordinated epithelial-lumen remodeling, breakdown of morphometric homeostasis during collapse, and transient biophysical fluctuations during fusion. Overall, this work establishes a physically transparent and reproducible approach for quantitative, label-free analysis of organoid dynamics in three dimensions.

How quantum electron and nuclei motions affect biomolecular chemical reactions remains a central challengeable question at the interface of quantum chemistry and biology. Ultrafast charge migration in deoxyribonucleic acid (DNA) has long been hypothesized to play a critical role in photochemistry, genome stability, and long-range biomolecular signaling, however, direct real-time observation of these electronic processes has remained elusive. Here, we present a theoretical investigation and propose the concept of future experimental measurements of laser-driven charge dynamics in the canonical DNA nucleobase pairs thymine_adenine and cytosine_guanine. Attosecond-resolved simulations employing high-level ab initio methods reveal base-dependent ionization mechanisms, directional charge migration pathways, and electronic coherences that govern sub-femtosecond redistribution of electron density across hydrogen-bonded nucleobase interfaces. Accordingly, we propose the concept of a quantum attosecond scanning electron microscope, termed the quantum attomicroscope (Q-attomicroscope), a capable of imaging photoinduced quantum chemistry reactions in attosecond temporal resolution and sub-nanometer spatial precision. As a proof of principle, we propose to image the charge migrations dynamics in DNA which we studied theoretically. Together, our preceptive bridges theory, instrumentation, and control, outlining a pathway toward laser mediated manipulation of DNA structure with implications for repair processes, chemical reactivity, and future personalized medicine.

Magnetohydrodynamics (MHD) generators present a promising pathway for clean energy conversion by directly transforming conductive fluids' kinetic energy into electricity. This study investigates the impact of electrode geometry modifications on the performance of a vortex-type seawater MHD generator. Three electrode designs, partial, whole-area, and spiral, are analyzed through combined analytical and numerical simulations using COMSOL Multiphysics. The study focuses on internal resistance reduction, current density distribution, and overall power output. The results indicate that electrode area and spacing are critical determinants of performance. The whole-area electrode achieves the highest output, with a 155 percent increase in power compared to the baseline partial electrode. The spiral electrode demonstrates reduced internal resistance and improved current flow but exhibits lower open-circuit voltage due to reduced electrode spacing. The simulations show strong agreement with theoretical models, with deviations of less than 4 percent in open-circuit voltage predictions. These findings highlight the importance of geometric optimization for advancing seawater-based MHD generators as sustainable and efficient energy conversion systems.

Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate fast ionic conduction. Herein, we use laser-driven ultrafast impedance spectroscopy (LUIS) to resonantly excite phonons using a THz field and probe ion hopping with picosecond time resolution. We apply LUIS to understand the dynamical role of phonons in $Li_7La_3Zr_2O_{12}$ (LLZO). When in its cubic phase (c-LLZO), this garnet-type solid electrolyte has an ionic conductivity two orders of magnitude greater than its tetragonal phase (t-LLZO). Upon excitation of phonons in the 0.5-7.5 THz range, the ionic conductivity of both polymorphs is enhanced. Furthermore, we observe a similar time decay constant of the perturbation between t-LLZO and c-LLZO despite their distinct Li sublattices and migration mechanisms. Along with Raman spectra and MD-computed vibrational density of states, these findings suggest that low-energy optical phonons perturb the lattice to enhance ionic conduction independently of the underlying static properties. We propose that it does so by increasing the population of phonons which directly modulate the ion hopping pathway and by increasing the entropy of migration thereby partially counteracting the migration barrier set by the Li sublattice. Overall, this work highlights the interplay of static and dynamic factors that enables improved ionic conductivity in both highly and poorly conducting inorganic solids alike.