Browse Articles

Numerical analysis of dispersion and elastic wave propagation in spatiotemporally modulated spring–mass metamaterials

Social determinants of postpartum depression among refugees and internally displaced women in Lebanon: a cross-sectional study

Comparative plastid genomics of Hippophae reveals phylogenetic relationships and provides candidate DNA markers for taxonomic identification

Abstract Sea buckthorn ( Hippophae L., Elaeagnaceae) is of considerable ecological and economic importance, and primarily distributed across the Qinghai–Tibet Plateau and adjacent regions. Morphological similarity among taxa has long hindered accurate species and subspecies identification, underscoring the need for robust molecular diagnostics. This study analyzed the complete plastid genome sequences of five Hippophae species (17 accessions), including a newly assembled genome of Hippophae rhamnoides subsp. mongolica . The genomes (~ 155–156 kb) exhibited a conserved quadripartite structure comprising 85 protein-coding, eight rRNA, and 38 tRNA genes. Phylogenomic reconstruction based on 78 protein-coding genes well-resolved the interspecific relationships, confirmed the monophyly of Hippophae and H. rhamnoides , and consistently placed H. tibetana within the H. rhamnoides clade. Comparative analyses identified 46 highly variable regions and abundant A/T-rich simple sequence repeats, predominantly in intergenic spacers. Inverted repeat boundaries were largely conserved across taxa, with H. salicifolia exhibiting a distinctive ndhF –IRb/SSC configuration. These plastid genomic resources provide a robust foundation for the development of diagnostic molecular markers with direct applications in Hippophae taxonomy, phylogenetics, germplasm conservation, and targeted breeding programs.

Cascading geomorphic hazards triggered by fluvial floods in an anthropogenically-impacted beach

Earthquakes induced by overpressure methane-bearing fluid in northwest Sichuan Basin, China

Perceived busyness shapes assortment size preferences via distinct thinking styles

Impact of different stocking densities on growth performance, welfare and physiology of Litopenaeus vannamei in RAS

Abstract Optimal stocking density allows shrimp farmers to maximise profit while maintaining animal welfare. To date, holistic assessments of chronic crowding stress effect on Litopenaeus vannamei are lacking regarding biological impairments, stress-related behaviors, and recovery capacity. Twelve tanks of a recirculation aquaculture systems were maintained at three stocking densities: 1 kg/m 2 (low density—Low), 2 kg/m 2 , (standard density—Standard) and 4 kg/m 2 (high density—High). The experiment was divided into phase 1 (21 days stress) and phase 2 (21 days recovery). After stress, shrimp at Low treatment exhibited the highest survival rate, best growth performance, antennae and uropods quality. Stress behavior included more frequent abnormal swimming patterns and loss of balance. Hemolymph analysis suggested homeostatic mechanisms for the mobilization of energy from storage organs. Mild upregulation of specific markers indicated cellular/oxidative stress. Limited immune system effects indicate low correlation with crowding or distress. During the recovery phase, mortalities ceased and partial compensatory growth was recorded. Shrimp recovered from injuries, while abnormal behaviors frequency also decreased. Oxidative stress marker expression returned to baseline levels for unstressed shrimp. Morphological and behavioral observations can be used industrially to assess crowding stress or as tools for developing welfare indices or monitoring systems.

Information and communications technology-based versus handout-based home exercise programs for heel pain syndrome: a prospective randomized study

DriveEmo-FL: in-cabin radar-based emotion sensing for autonomous vehicles smart response

Error minimized LO modeling of electric vehicle integrated off-grid microgrids using Taylor-Laurent series expansion and BBO based optimization under stability and steady state constraints

Abstract This paper presents an effective approach for lower-order (LO) modeling of an electric vehicle–integrated off-grid microgrid (OMG) system. The seventh-order system (SOS) of the OMG is reduced to a second-order model (SOM) while preserving the original system’s dynamic characteristics and ensuring computational efficiency. The Taylor series (TS) and Laurent series (LS) expansions are employed to simplify the complex system that plays a significant role in the reduction process. The expansion parameters of the higher-order system (HOS) of OMG and its lower-order model (LOM) are exploited to construct the fitness function. The proposed approach constructs three sub-objective functions based on TS and LS. These sub-objective functions are then combined into a single fitness function to obtain an improved LOM by enhancing the transient and steady-state responses with respect to the HOS of OMG. To minimize the error, the resultant fitness function is optimized using the brown bear optimization (BBO) algorithm. The optimization is performed under two key constraints: (i) ensuring zero steady-state error, and (ii) satisfying the Hurwitz stability criterion. To demonstrate the efficacy of the proposed LOM, it is compared with other LOMs obtained from different approximation techniques. The proposed LOM and other LOMs are graphically validated through step, impulse, Bode, Nichols, and Nyquist response comparisons with the HOS. Additionally, the performance error criteria (PEC), time-domain specifications (TDSs) and frequency domain specifications (FDSs) of the proposed LOM are compared with other LOMs using the HOS to establish the validation and applicability of the proposed method.

Psychoacoustically guided midfrequency band-limiting improves the diagnostic utility of classical acoustic measures in dysphonia

Abstract Human hearing is most sensitive around 3 kHz, where small changes in harmonic structure and noise are particularly noticeable. However, traditional or standard acoustic measures of dysphonia—jitter, shimmer, harmonics-to-noise ratio, and cepstral peak prominence smoothed—are usually calculated from full-band signals and do not account for this psychoacoustic weighting. Consequently, their diagnostic accuracy is often reduced in connected speech and cases of mild dysphonia, which are common clinical presentations. We tested a simple, low-cost preprocessing strategy that limited analysis to the 2–4 kHz frequency band before computing these measures. Using sustained vowels and connected speech from Japanese speakers across a wide range of dysphonia severities, we compared band-limited and full-band results against the auditory-perceptual judgments of breathiness, roughness, and the grade of hoarseness. Across utterance and perceptual dimensions, band-limiting consistently improved diagnostic performance, with particularly notable gains in mild dysphonia. These findings suggest that restricting analysis to the high-sensitivity auditory range reveals microperturbations that are masked by dominant low-frequency energy in full-band signals. This psychoacoustically guided approach enhances the clinical usefulness of traditional acoustic measures without requiring complex modeling. It is device-independent and suitable for telemedicine and real-world voice-assessment workflows.

Explainable deep learning for early diagnosis of chronic kidney disease from CT images in Bangladeshi patients

Combining multiple interface set path ensembles with MBAR reweighting

We introduce a method to compute the reweighted path ensemble by combining transition interface sampling simulations conditioned on different collective variables in a globally consistent reweighting framework. The approach is based on the multistate Bennett acceptance ratio methodology applied to entire trajectories. By illustrating the technique with simple 2D potential models and a more complex host–guest system, we show that the statistics can significantly improve compared to independently reweighted combinations.

Physics-aware deep learning models for predicting the heterogeneous mechanical properties of polymeric nanostructured materials

Developing innovative materials with superior properties has been a major engineering challenge. Rationally designed polymer nanocomposites represent an emerging field that offers materials with enhanced mechanical properties and added functionalities. Here, we propose a computational methodology for predicting the heterogeneous mechanical behavior of model polymer nanocomposites that combines deep learning and detailed atomistic simulations. Atomistic simulations, while crucial for unraveling the mechanical behavior of composite materials, often focus on global mechanical properties by applying macroscopic strain and calculating the overall stress response. However, exploring the distributions of mechanical properties in heterogeneous polymer systems requires the computation of stress and strain fields for each atom within the simulation box. Our approach is based on a hierarchical data-driven computational framework that involves a nano/micro/macro coupling approach to predict the mechanical properties of polymer nanocomposites. We introduce a physics-aware deep learning method to predict the distribution of mechanical properties of model nanocomposites by directly computing stress and strain at the atomic level. Incorporating a set of physics-based constraints in the loss function encourages the deep learning model to follow the physical symmetries of the underlying system and to accurately predict the values of local stress and strain data in any arbitrarily chosen domain of the model systems. The proposed framework is transferable over systems with different volume fractions of nanofiller while being computationally efficient and effectively agnostic to the underlying microscopic model.

Attacking the integral transformation bottleneck: A fast orbital-optimization algorithm with sub-cubic computational cost for arbitrary seniority-zero wavefunctions

An orbital-optimization algorithm is devised for finding stationary points of seniority-zero wavefunctions applied to quantum-chemical Hamiltonians of full seniority. The algorithm is agnostic to peculiarities of the seniority-zero method, requiring only the availability of its one- and two-electron reduced density matrices. Their simpler structure is exploited to avoid the computationally demanding four-index two-electron integral transformation; instead, intermediary rank-three tensors are constructed, which greatly reduce the consumption of computational resources. In combination with the spatial locality of atomic and molecular orbitals, as well as the sparsity of the seniority-zero density cumulant, the algorithm achieves sub-cubic scaling with system size. A direct inversion in the iterative subspace scheme is applied to accelerate orbital-optimization convergence. Using pECCD as the seniority-zero wavefunction, it is demonstrated that the algorithm succeeds in optimizing large linear oligomer chains and hydrogen 2D/3D clusters with up to 1391 orbitals on modest computer hardware. The method is subsequently applied to predict molecular properties of ozone, the rotational barrier in ethylene, and isomerization energies of organic reactions, where it is benchmarked against conventional quantum-chemical methods.

Peering into the crystal ball: Excess entropy scaling predicts equilibrium transport coefficients before equilibration

In a molecular-dynamics simulation, an equilibrium transport coefficient is to be computed (as the name suggests) in a system that has reached thermodynamic equilibrium. Indeed, the two most common methods for computing equilibrium transport coefficients—using the Einstein–Helfand (EH) relation and using the Green–Kubo (GK) relation—make the formal assumption that a system has reached thermodynamic equilibrium before sampling begins. There is, however, “no free lunch”: Equilibration always demands an upfront computational investment. In this work, we study the question of just how much equilibration is needed for each computational method to yield a serviceable estimate for a transport coefficient, using as our case study a simple fluid that is initialized far out of configurational equilibrium. We show that a third method for computing transport coefficients—excess entropy scaling, which makes use of system structure in the form of the radial distribution function—has several statistically beneficial properties as compared to EH and GK, including faster convergence to the long-time-average value of the transport coefficient and lower sample-to-sample variance en route to convergence, which we rationalize from an information-theoretic perspective. Overall, this work points to the significant value that structure-based estimators may bring to any workflow demanding high-throughput calculation of transport coefficients.

Elucidating many-body effects in molecular core spectra through real-time approaches: Efficient classical approximations and a quantum perspective

Accurately resolving many-body satellite features in molecular core-level spectra requires theoretical approaches that capture electron correlation both efficiently and systematically. The recently developed time-dependent double coupled-cluster (TD-dCC) Ansatz achieves this by combining correlation effects from the N- and (N − 1)-electron sectors, but its exact formulation remains computationally demanding. Here, we introduce a hierarchy of cost-effective approximate TD-dCC-truncated Baker–Campbell–Hausdorff (BCH) expansions, which preserve a single-similarity-transformation structure while retaining the essential correlation diagrams responsible for satellite formation. We further develop a detailed component analysis that isolates hole-mediated excitation pathways—correlated processes arising from the coupling between ground-state and ionized-state amplitudes—and use it to interpret quasiparticle and satellite features across the hierarchy. Applications to the single-impurity Anderson model and molecular systems (H2O and CH4) demonstrate that the approximate TD-dCC methods closely and efficiently reproduce exact many-body spectral features and quasiparticle weights. In parallel, we construct a fault-tolerant quantum signal processing algorithm for the core-hole Green’s function, providing a scalable quantum route for simulating correlated core-level dynamics. Together, these developments establish complementary classical and quantum methodologies for quantitative, many-body-accurate core spectroscopy.

Observation and modeling of bound–bound and bound-free transitions in Cu2

Laser-induced transitions from the perturbed J0u+, v′=0−2∼G0u+,v′=62−68 system to the X1Σg+(0g+) ground state of dicopper have been investigated experimentally and theoretically. Upon excitation of specific rotational levels, long progressions to vibrational levels are observed. Strong emissions are observed at low vibrational levels. Notably, spectra show significant line strength near and above the asymptotic atom dissociation limit. The latter feature is due to the Condon internal diffraction of bound-free transitions to the continuum region of the ground state. Based on the Rydberg–Klein–Rees potentials for the J0u+, G0u+, and X1Σg+(0g+) states, eigenvalues and wave functions are obtained by solving the radial Schrödinger equation. Hence, frequencies of rovibrational transitions, Franck–Condon factors, and Einstein coefficients for spontaneous emissions are determined. The required transition dipole moment functions are obtained from high-level electronic structure calculations at the multi-reference configuration-interaction level of theory. Simulated line positions and relative intensities for transitions near and above the dissociation limit are in good agreement with the experiment. Furthermore, the feasibility of photoassociation experiments involving ultracold copper atoms is estimated by calculating the coupling rate between the colliding cold atoms and the excited G0u+ state.

Repetitive after-discharges are more common in acquired demyelinating polyneuropathies

Stoichiometric-like ion–dipole coordination saturation in an isolated polymer–ion system: Single-molecule force spectroscopy and theoretical insights

Ion–dipole interactions are well defined in small-molecule systems, but in macromolecular systems, their behaviors remain insufficiently understood due to structural complexity and binding heterogeneity. Herein, the ion–dipole interactions between a single poly[trifluoropropyl(methyl)siloxane] (PMTFPS) chain and 1-allyl-3-methylimidazolium chloride (AMIMCl) have been systematically investigated using single-molecule force spectroscopy combined with complementary techniques. For the first time, the isolated polymer–ion system is shown to reach a state with stoichiometric-like saturated ion–dipole bridges at an AMIMCl-to-PMTFPS repeating unit molar ratio of 0.33:1, in which one AMIMCl molecule bridges the two terminal trifluoropropyl groups within each three-unit segment. Further increasing the AMIMCl content allows for additional association with the intermediate trifluoropropyl group, but steric and electrostatic constraints prevent the formation of new bridges. The ion–dipole binding energy is calculated to be 9.32 ± 0.11 kJ/mol. These results reveal the distinctive ion–dipole combining pattern of a single polymer chain under molecular confinement. This work is expected to bridge the gap between the well-defined binding behaviors of small-molecule systems and the complex landscape of bulk macromolecular systems, offering mechanistic insights into how ion–dipole interactions can be tuned within macromolecular materials.