Browse Articles

SMART model-based social marketing intervention to improve vitamin d supplementation adherence in female university students: a quasi-experimental mixed-methods study

Molecular dynamics simulations of structure and dynamics of ionic solutions under Couette shear flow

Comprehending the behaviors of ionic solutions under shear flow is essential for the development of lubrication, electroplating, electrochemical sensing, energy conversions, and many other research fields. In this study, we propose a novel coarse-grained simulation method to investigate the physicochemical properties of ionic solutions under Couette shear flow, in which the ions are modeled as charged beads, the solvent molecules are coarse-grained as dipolar beads using the Stockmayer fluid model, and the SLLOD algorithm is employed to account for the movements of ions and solvent under shear. This method can effectively capture the interplay between ion–ion, ion–dipole, and dipole–dipole electrostatic interactions, as well as their coupling with the imposed flow field. We systematically investigate the impacts of ionic concentrations, dipole moments, and shear rates on the microscopic structure, steady-state viscosity, and ionic diffusivity of ionic solutions. Our results reveal that the formation of ionic clusters via cation–anion electrostatic interactions leads to increased viscosity and suppressed ionic diffusion. Increasing dipolar interactions or applying stronger shear fields can both lead to the disassembly of ionic clusters; however, these two effects show distinct impacts on solution viscosity and ionic diffusion. In particular, enhancing the solvent dipole moment strengthens ion solvation, resulting in increased viscosity and diffusion, whereas elevating the shear rate weakens solvation by displacing bound solvent molecules from the ions, thereby reducing viscosity and enhancing ionic diffusivity. Our study confers insights into the fundamental understanding of the physicochemical properties of ionic solutions and provides guidance for the design and optimization of the functional ion-containing liquid materials.

Analysis of immune-related alterations in blood and spinal cord of canine degenerative myelopathy, a spontaneous model of amyotrophic lateral sclerosis

Committor-regularized learning of differentiable collective variables from non-differentiable structural descriptors

Collective variables (CVs) are essential for interpreting and accelerating rare events in molecular simulations, yet their construction is constrained by the requirement of differentiability with respect to atomic coordinates. This excludes many powerful structural descriptors, such as discrete local-structure classifiers, which are routinely used for analysis but cannot be applied directly to define bias potentials in enhanced-sampling molecular dynamics simulations. Here, we introduce a physics-informed machine-learning framework that transforms non-differentiable structural descriptors into fully differentiable, bias-ready collective variables. The approach uses graph neural networks to learn smooth surrogate representations of inherently discontinuous descriptors, trained directly from atomic coordinates while preserving their physical interpretability. To guide interpolation between metastable states, we introduce committor regularization, a physically motivated constraint derived from transition path theory that biases learning toward committor-like behavior along reactive pathways. We apply this framework to crystal nucleation in a supercooled copper melt, using polyhedral template matching (PTM), a discrete, non-differentiable classifier of local crystal structure, as the target descriptor. Neural network CVs are trained to reproduce the fractions of FCC and BCC environments inferred from PTM labels while remaining fully differentiable. We show that committor-regularized models yield reproducible free-energy surfaces across ensembles of independently trained CVs, overcoming the stochastic variability that commonly limits machine-learned collective variables. By bridging discrete physics-based descriptors and continuous differentiable representations, this work establishes a general route to constructing interpretable, reproducible, and physically grounded collective variables for enhanced sampling.

Availability of drone mission with binary decision diagram based on uncertain data

Abstract Unmanned aerial vehicles (UAVs), or drones, are increasingly deployed for critical missions such as environmental monitoring, infrastructure inspection, and disaster response. Assessing the reliability of these missions is essential for operational planning, yet conventional approaches often fail when input data are incomplete or epistemically uncertain. We present a novel framework for mission availability analysis that integrates fuzzy decision tree (FDT) induction with binary decision diagram (BDD) construction. The method interprets a drone mission as a reliability system, where checkpoints act as components and mission success is modeled by a structure function. Expert evaluations expressed as confidence degrees are used to induce an FDT, which is subsequently defuzzified and transformed into a canonical BDD. This representation enables efficient computation of mission availability and sensitivity measures using established BDD algorithms. We validate the approach on a real-world case study of a forest fire monitoring mission comprising eight checkpoints and demonstrate high predictive accuracy (94%) despite incomplete training data. The proposed method provides a transparent, reproducible pipeline for translating uncertain, expert-driven data into quantitative reliability metrics, offering practical insights for mission planning under uncertainty.

1H spin-lattice relaxation in solutions of coated superparamagnetic nanoparticles—Challenging the validity range of the low anisotropy energy model

The theoretical model for 1H spin–lattice superparamagnetic relaxation enhancement, under the assumption of low anisotropy energy, was evaluated using Fe3O4 nanoparticles (15 and 20 nm) coated with a protein G–conjugated IPG polymer and dispersed in water and water/glycerol solutions. The experimental 1H relaxation data were collected over a frequency range from 5 kHz to 40 MHz (referring to 1H resonance frequency) in the temperature range from 278 to 308 K. Distinct 1H spin–lattice relaxation maxima, as predicted by the low anisotropy energy model, were observed; however, the overall frequency dependence of the relaxation rates increasingly resembles that expected for systems with higher anisotropy energy (larger nanoparticles). A detailed comparison between the experimental data and the theoretical model predictions revealed discrepancies. The ratio between the theoretical and experimental values varies between 1.1 and 0.6, except in the case of a water solution of the 20 nm nanoparticles, for which the discrepancies are more pronounced. This effect was explained by fast electronic spin–spin relaxation. The results provide a quantitative explanation of the factors that define the applicability limits of the model of superparamagnetic relaxation enhancement derived under the assumption of low anisotropy energy and identify conditions under which its predictions remain reliable.

How object naming dissociates from repetition and comprehension impairments when post stroke aphasia is less severe

Abstract Object naming is widely used for assessing aphasia. We provide the first quantitative analysis of how well: (A) impaired spoken object naming (anomia) detects auditory repetition and/or speech comprehension impairments and (B) intact naming rules these impairments out. Participants were 382 stroke survivors (1 month to 34 years post-stroke) with impaired naming, repetition and/or comprehension, but intact object recognition. We assessed: (1) Incidence of anomia within the full sample; (2) its positive predictive value (PPV), i.e., the proportion of patients with anomia who had impaired repetition and/or comprehension; (3) its sensitivity to other impairments, i.e., the proportion of patients with impaired repetition or comprehension who had anomia; and (4) how object naming, word repetition, sentence repetition, word comprehension and sentence comprehension compared in their incidence, PPV and sensitivity, when each was treated as the reference task. Incidence, PPV and Sensitivity of anomia were 66%, 90% and 63% across sample; 93%, 100% and 93% for the most severely aphasic patients and 50%, 86% and 46% for the remaining patients. These metrics were not higher for object naming than sentence comprehension, sentence repetition and word repetition; but word comprehension showed markedly lower incidence and sensitivity. Although anomia may be the most salient symptom of aphasia in everyday conversation, our findings (i) challenge assumptions that object naming is a superior test of aphasia, (ii) show that the presence of anomia was insensitive to 37% of patients with repetition and comprehension impairments and (iii) highlight how PPV and sensitivity within an aphasic sample are influenced by impairment severity, task dependency, measurement variability and inter-patient differences.

Competing effects of activity and diffusive noise in collective ordering of rod-like particles

Self-organization and emergent order are hallmarks of active matter. Using large-scale Brownian dynamics simulations, we study a binary mixture of self-propelled and passive rod-like particles, representing bacterial cells or synthetic anisotropic colloids. The interplay between motility, diffusive noise, and shape anisotropy produces a rich spectrum of collective states, including clustering, demixing, and orientational ordering. We find that the degree of spatial and orientational order exhibits a non-monotonic dependence on both the Péclet number and the noise strength ratio. At intermediate activity and optimal noise contrast, passive particles form tetratically ordered domains accompanied by a pronounced decrease in configurational entropy of the entire system, indicating an entropy-driven ordering transition. At high activity or large noise disparity, orientational coherence and clustering are lost, restoring a homogeneous disordered phase. These results reveal the minimal physical ingredients, such as motility, noise asymmetry, and shape anisotropy, sufficient to drive large-scale organization in active–passive mixtures, offering new insights into the collective dynamics of dense active soft matter.

Astrocyte diversity and aging in the mouse lemur primate brain

Abstract Astrocytes play key roles in maintaining brain homeostasis, metabolism, and neurovascular integrity, yet their diversity and age-related modulation remain insufficiently understood, particularly across primate lineages. While rodent studies have generated extensive knowledge, notable species differences highlight the need for comparative analyses in non-human primates. The gray mouse lemur ( Microcebus murinus ), a small primate widely used in aging research, offers a valuable but underexplored model for studying astroglial aging. In this study, we characterized astrocyte distribution, morphology, and reactivity in 17 mouse lemurs aged 1.0–11.5 years using GFAP and vimentin immunohistochemistry. We identified marked regional and morphological heterogeneity, with dense astrocytic labeling in white matter, hippocampus, and sparse but diverse cortical populations. Distinct astrocyte subtypes—including fibrous, protoplasmic, projection, pial and subpial interlaminar, radial glia-like cells, tanycytes—were documented. Varicosity-bearing processes were common across multiple astroglial subtypes and may indicate altered physiological states. Quantitative analyses revealed pronounced age-related increases in astrocytic reactivity, particularly in white matter and interlaminar astrocytes. Cortical and hippocampal changes were comparatively modest. These findings indicate region-specific astrocytic vulnerability during aging and support the translational value of the mouse lemur for investigating glial aging in primates.

Basic requirements for potential differences across solid–fluid interfaces

At model water–vapor and water–solid interfaces, molecular ordering leads to charge oscillations and, thereby, to a spatially varying electrostatic potential. Atomistic simulations indicate that such ordering leads to an electric potential difference χ, the surface potential, of about −0.5 V across the first few molecular layers. Here, we calculate surface potentials at interfaces between simple model fluids and a solid with molecular dynamics simulations. The fluids are made up of either diatomic, dipolar molecules or a single Lennard-Jones particle with a dipole moment. All fluids show some structuring near the interface, but charge oscillations and a non-zero surface potential are present only for asymmetric molecules (unequal diameters of the atoms) or molecules with an off-center dipole. We condense this finding into the criterion that the geometric and dipolar centers of a molecule must differ for the fluid to exhibit a surface potential. Remarkably, while the solid–fluid interaction strength strongly affects the magnitude of charge oscillations, it hardly affects the potential drop χ. Furthermore, our results demonstrate that changing the diameter of the smaller atom can flip the sign of the surface potential, thus highlighting the importance of steric effects.

Properties and inference of the Pareto Lomax distribution with applications to real data

Assessment of trajectory surface hopping methods in long-time nonadiabatic dynamics

We present an assessment of an extensive set of trajectory surface hopping methodologies for modeling long-time population dynamics in a two-level spin-boson model. The considered methodological recipes involve combinations of three surface hopping approaches with five decoherence correction methods, several decoherence-time computation schemes, and two initial-condition sampling methods. In addition to these combinations, the phase correction approach of Shenvi, Subotnik, and Yang (SSY) is considered. By exploring a wide range of meta-parameters controlling decoherence and dephasing times, we determine the optimal performance of such methods and provide a ranking of the best-performing approaches. We find that inclusion of the SSY correction generally over-accelerates the population relaxation dynamics. We also find a strong dependence of the trajectory surface hopping (TSH) calculations on the type of initial-condition sampling: Wigner sampling leads to overestimated population transfer rates and requires decoherence corrections. The simplified decay of mixing approach is found to perform well in this situation. In contrast, Boltzmann sampling leads to a surprisingly remarkable performance of bare TSH schemes without decoherence, which may be a consequence of error cancellation. We provide a critical discussion of the observed trends in different methods’ performance and suggest possible avenues for their further improvement.

A mathematical approach to Chikungunya transmission dynamics incorporating media awareness and optimal control

Fluorescence-detected two-dimensional electronic spectroscopy: A coarse-grained simulation approach

Fluorescence-detected two-dimensional electronic spectroscopy (F-2DES) offers superior sensitivity compared to the traditional coherent two-dimensional electronic spectroscopy (2DES) technique. However, theoretical modeling remains essential to interpret F-2DES spectra, especially for multi-chromophoric systems. While widely used to study excitation energy transfer in molecular assemblies, even conventional 2DES faces computational challenges for large systems. To address these challenges, we extend a recently developed coarse-grained method for 2DES to simulate F-2DES and account for signatures of exciton–exciton annihilation events that affect cross-peak intensities in F-2DES. We then apply this approach to the light-harvesting II complex of purple bacteria, a well-studied benchmark system, and we find that F-2DES simulations reproduce experimental cross-peaks at zero and early waiting times. Moreover, disabling exciton–exciton annihilation recovers results identical to standard 2DES simulations, confirming that the observed cross-peaks arise from annihilation events, as hypothesized earlier. The implemented method opens the door for future exploration of waiting-time dynamics and extends the possibility of predicting F-2DES spectra to extensive photosynthetic systems.

Integrated cortical-cognitive signatures identified by machine learning enable early detection of MCI in type 2 diabetes

Surface chemistry governs ultrafast charge polarization in CdSe quantum dots: A real-time TDDFT study

Ligand chemistry plays an important role in tuning the optoelectronic response of cadmium selenide (CdSe) quantum dots, yet the microscopic mechanisms linking ligand–core interactions to charge separation and exciton dynamics remain elusive. In this work, we employ real-time time-dependent density functional theory (rt-TDDFT) simulations to investigate the ultrafast electronic response of (CdSe)33 nanocrystals functionalized with methylamine, acetate, and 1-propanethiol ligands under resonant optical excitation. The time evolution of the dipole moment, Mulliken charge distribution, and orbital populations indicates that ligand identity modulates the amplitude and rate of charge separation as well as the degree of exciton coherence and stabilization. Methylamine ligands promote enhanced charge polarization and exciton-like delocalization through weak Cd–N coupling and field-induced Stark effects, while thiol passivation introduces deep trap states that favor back-transfer and suppress sustained charge separation on the simulated ultrafast time scale. Acetate ligands exhibit intermediate behavior, with shallow O 2p-derived traps and moderate stabilization of the excitonic charge distribution. Within the coherent electronic regime accessed by rt-TDDFT, these results provide atomistic insight into how ligand-induced fields and covalency influence the electronic dynamics in colloidal CdSe, helping to bridge the experimental observations of ligand-dependent photophysics with microscopic charge-transfer mechanisms.

Dynamic evaluation of waterfowl habitat quality based on an integrated multi-indicator framework and habitat function enhancement strategies in Xianghai Nature Reserve

Nanoscale SERS probing of thermal and non-thermal molecular vibrational excitations

Understanding how vibrational energy is generated, redistributed, and dissipated at the nanoscale is central to contemporary molecular and chemical physics. Plasmonic nanostructures offer highly efficient channels for both driving and probing molecular vibrations, enabling access to regimes where steady-state populations markedly depart from thermal equilibrium. This perspective examines how anti-Stokes surface-enhanced Raman scattering (SERS) has become a quantitative tool for resolving such thermal and non-thermal vibrational populations within nanoscale hotspots. We first outline the general framework linking Stokes and anti-Stokes Raman/SERS intensities to vibrational occupation, followed by experimental approaches that realize and probe thermal excitation (nanoscale thermometry) and non-thermal excitation pathways. We conclude by highlighting key methodological challenges—especially plasmonic bias correction and quantitative population analysis—and discuss future opportunities for employing anti-Stokes SERS as a molecular-level probe of energy flow in next-generation nanophotonic and catalytic systems.

A hybrid deep learning approach with temporal awareness for intelligent intrusion detection in 6G-enabled IIoT networks

Abstract The integration of the sixth-generation (6G) communication technology and the Industrial Internet of Things (IIoT) has realized the intelligence and automation of industrial applications. However, due to the complexity, dynamics, and heterogeneity of data, traditional threat detection methods make it difficult to deal with cyber threats in the 6G-IIoT environment. In view of these limitations, this study proposes a hybrid Deep Learning (DL) model combining a Deep Neural Network (DNN), a Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism for threat detection in a 6G-IIoT environment. DNN extracts global features, BiGRU captures bidirectional temporal dependencies, and the attention mechanism highlights key anomalies. Experimental results on the Edge-IIoTset dataset show that the accuracy rate of the model is $$96.88\%$$ . It outperforms baseline models (such as ANN, CNN, DNN-LSTM). The model achieves high accuracy and low False Positive Rate (FPR), and meets the dynamic security requirements of the 6G-IIoT environment. This research provides a promising solution for real-time threat detection in next-generation industrial networks.

Many-electron systems with fractional electron number and spin: Exact properties above and below the equilibrium total spin value

The description of many-electron systems with a fractional electron number, Ntot, and fractional z-projection of the spin, Mtot, is of great importance in physical chemistry, solid-state physics, and materials science. In this study, we analyze the fundamental question of what the ensemble ground state of a general, finite, many-electron system at zero temperature is, with a given Ntot and Mtot, distinguishing between low- and high-spin cases (separated by the boundary spin MB). For the low-spin case, the general form of the ensemble ground state has been rigorously derived in Goshen and Kraisler [J. Phys. Chem. Lett. 15, 2337 (2024)], generalizing the piecewise linearity and the flat-plane conditions for many-electron systems. Here, we provide an alternative proof for this case, discuss the ambiguity in the description of the ground state, and show that this ambiguity can be removed via maximization of the system’s entropy. For the high-spin case, we find that the form of the ensemble ground state strongly depends on the system in question. We prove three general properties that characterize the ground state at high spins and narrow down the list of pure states it may consist of. We illustrate the aforementioned properties of high-spin cases by examining the ensemble ground state when Mtot approaches MB from above during the addition of (a fraction of) an up- and down-electron to a given system. Furthermore, we relate the frontier orbital energies of Kohn–Sham (KS) density functional theory (DFT) to total energy differences at high spin values, particularly the ionization potential (IP), the fundamental gap, and the spin flip energies. Analyzing the frontier energies on both sides of each boundary in the total energy profile, where the energy slope changes abruptly, we derive expressions for new derivative discontinuities, which are predicted to appear as jumps in the corresponding KS potentials. In this way, we generalize the well-known IP theorem of DFT to cases with fractional electron number and to cases with high spin. Our analytical results are supported by an extensive numerical analysis of the Atomic Spectra Database of the National Institute of Standards. The new exact conditions for many-electron systems derived in this study are instrumental for the development of advanced approximations in DFT and other many-electron methods.