Browse Articles

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.

Potential gonadal-beneficial effect of sitagliptin against paclitaxel-induced testicular dysfunction via mediating PERK/CHOP/NLRP3/Sestrin2 signaling pathway

Abstract Paclitaxel (PTX) is broadly prescribed to treat various malignancies. However, it induces negative impacts on many organs, including testes. This study explored the beneficial role of sitagliptin (SIT) in PTX-provoked testicular damage and the underlying mechanisms. Rats were allocated into four groups: (I) control, (II) PTX, (III) PTX + SIT5, and (IV) PTX + SIT10. Histopathological and ultrastructural analyses were conducted along with sperm analysis. Immunohistochemical examinations of NOD-like receptor protein 3 (NLRP3), cleaved caspase-3, caspase-3, cytochrome c (Cyt.c), and interleukin-1 beta (IL-1β) were assessed. Serum testosterone and testicular 17β-hydroxy steroid dehydrogenase (17β-HSD), sestrin2, phosphorylated protein kinase R-like ER kinase (pPERK), and C/EBP homologous protein (CHOP) were determined. SIT induced a remarkable increase in sperm count, motility, and viability, with a pronounced decline in sperm abnormality compared to PTX group. SIT increased testosterone and 17β HSD levels. SIT elevates sestrin2, reduced glutathione (GSH), and catalase, and reduces malondialdehyde (MDA), reflecting its antioxidant action. SIT mitigates ER stress via diminishing pPERK and CHOP. SIT reduces NLRP3 and IL-1β levels, clarifying its anti-inflammatory action. SIT decreases cleaved caspase-3, caspase-3, and Cyt.c levels, verifying its anti-apoptotic features. Overall, SIT ameliorated PTX-provoked testicular dysfunction via mediating PERK/CHOP/NLRP3/Sestrin2 signaling pathway.

N K-edge XAS measurements for ionic liquids and N-heterocycle molecules in liquid phase analyzed by simulations with explicit inclusion of solvent molecules

We present a framework for interpreting nitrogen K-edge x-ray absorption of ionic liquids and imidazole derivatives in water on a single absolute energy scale. Liquid transmission measurements provide reproducible spectra that can be compared across solutes without ad hoc shifting. Experimental line shapes fall into three patterns defined by the availability of low-lying π acceptors. Pyrrolidinium and ammonium-like centers show no discrete pre-edge. Imidazolium cations show a single pre-edge. Imidazole derivatives show a robust doublet. Real-space calculations with explicit inclusion of water molecules test how hydration controls these patterns. A radius scan in simulation identifies a practical cluster size that captures the first hydration shell while avoiding spurious long-range structure. Direct summation of simulated spectra based on 100 molecular dynamics snapshots on a common energy scale converts discrete transitions into a continuous liquid-phase profile. It merges the two ring nitrogen atoms of imidazolium into one pre-edge and preserves the imidazole doublet with improved intensity ratios. A minimal protocol that combines absorber-centered p DOS with targeted COOP maps separates σ and π channels and locates hydration-sensitive π windows without broader projections. Hydration increases low-energy π overlap while leaving the σ onset comparatively stable. The result is a transferable set of rules for assignment and for choosing simulation settings that match spectra for liquid phases.

Redefining water scarcity through the integrated water strategic resilience index amid climate and conflict pressures

Abstract Water scarcity is a dynamic condition influenced by a variety of factors, including environmental variables but also political, economic, technological, and social variables. This research reflects the intersection of natural resources, governance, and human systems. Redefining water scarcity is a crucial factor for greater sustainable management in the face of increasing climate variability and geopolitical stress. The traditional water scarcity indices overlook the cumulative impact of climate change, socio-economic patterns, governance, and policies. To bridge this gap, we propose the Integrated Water Strategic Resilience Index (IWSRI), a novel, multidisciplinary index that quantifies water scarcity on the basis of water availability, quality, climate resilience, and socio-political considerations. By integrating hydrological, environmental, and socio-political factors, IWSRI can potentially serve policymakers, researchers, and stakeholders with an interdisciplinary tool for strategic water resource planning. This study outlines the theoretical and mathematical foundations of IWSRI, highlighting its ability to enhance decision-making in transboundary water management, disaster preparedness, and sustainable development. The application of IWSRI is particularly relevant for regions facing severe water stress and political instability, where water availability is both an environmental and security challenge. MENA countries, Israel, Turkey, Qatar, and the UAE possess high water resilience due to solid infrastructure and good governance, while Yemen, Syria, and Libya possess low resilience, driven by conflict and poor management. Egypt, Iran, and Algeria demonstrate moderate resilience due to potential in water management policy. In this respect, while emphasizing its broader applicability as a global tool for assessing water scarcity resilience, this research applies the IWSRI to the MENA region, as its climate, socio-political instability, and regional water stress make it a relevant case study to test its overall efficacy.

Beyond the Kuhn segment: Conformational substructures and relaxation dynamics in flexible chains

The statistical, “monomer-based” segment length b and the Kuhn length lk are central to polymer physics, yet the minimal size required for a segment to be truly statistical—Gaussian, uncorrelated, and valid as an entropic spring—has not been rigorously established. Using atomistic simulations of entangled polyethylene, we reexamine these foundational quantities. By fitting end-to-end distance distributions of C–C bond blocks to Gaussian forms and validating them with higher-moment analyses, we identify the minimal sizes corresponding to a statistical segment and an entropic spring. A single Kuhn segment (≈11 bonds) is the smallest statistically uncorrelated unit, but its distribution is strongly non-Gaussian, while the widely used monomer-based length b is not statistical. Gaussian statistics emerge only for blocks containing multiple Kuhn segments. At the Kuhn scale, we identify a heterogeneous organization into aligned chain segments (ACS), random conformational sequences (RCS), and chain ends (CE), each with distinct dynamical signatures. ACS exhibit strongly stretched relaxation with β ≈ 0.5, whereas RCS and CE relax faster with β ≈ 0.7. All segments display subdiffusive translational motion on the Kuhn scale. These results provide a molecular interpretation of stretched-exponential relaxation in polymer melts, in which the exponent β reflects the dimensionality and cooperativity of conformational rearrangements at the Kuhn-segment scale.