Search Results (30,435)

This brief proposes a novel Virtual-Geography Hawkes Process (VG-Hawkes) to model citation dynamics considering academic networks. The VG-Hawkes model incorporates academic relationships between authors as virtual distances by extending the conventional temporal Hawkes process, enabling a more detailed and realistic representation of citation behavior. Validation results on real-world datasets show that the VG-Hawkes model consistently achieves higher log-likelihood scores than temporal Hawkes models and effectively captures citation peaks and distributional patterns. While this study focuses on selected datasets and pairwise interactions, the model is general and readily extensible. Future work includes scaling to broader datasets and incorporating more complex author relationships. The VG-Hawkes model provides a novel and flexible framework for academic network analysis and scientific impact prediction. Full article

A novel low-cost poly(propylene carbonate-co-epichlorohydrin) (PPC-ECH) with mechanical properties similar to those of poly (butylene adipate-co-terephthalate) (PBAT) was developed and incorporated into a poly(propylene carbonate-co-phthalate) (PPC-P) matrix. Meanwhile, 4, 4’-diphenylmethane diisocyanate (MDI) was employed as a reactive compatibilizer and mixed with PPC-P and PPC-ECH to create a variety of PPC-P/PPC-ECH/MDI blends. The effects of PPC-ECH and MDI content on the mechanical, optical, thermal, morphological, and gas barrier properties of the blends were systematically investigated. Results demonstrated that MDI reacts with both PPC-P and PPC-ECH, forming a chemically bonded interface that significantly improves their compatibility. Notably, when 2 phr of MDI was incorporated, the elongation at break of the PPC-P/PPC-ECH/2MDI blend increased dramatically from 71% to 502%, while maintaining good tensile strength (~23 MPa) and light transmittance (~80%). To further enhance the gas barrier performance, a high-oxygen-barrier poly(vinyl alcohol) (PVA)/tannic acid (TA) complex coating was applied to the surface of the PPC-P/PPC-ECH/2MDI film. This coating synergistically leveraged the abundant hydroxyl groups in PVA and TA to form a dense hydrogen-bonded network, reducing oxygen permeability to an ultra-low value of 0.1 cm³·mm/(m²·day). This outstanding performance highlights the strong potential of PPC-P/PPC-ECH-based films for advanced packaging applications. Full article

With the expansion of urban rail transit, increased train operation density, and the large-scale grid integration of renewable energy such as offshore photovoltaic power, traction power supply systems face stricter requirements for operational safety, power supply reliability and energy utilization efficiency. Offshore photovoltaic power, integrated into the traction power supply network via flexible DC transmission technology, promotes renewable energy consumption, but its random and volatile output overlaps with time-varying traction loads, increasing the complexity of DC-side fault characteristics and protection control. Flexible DC technology is a core direction for next-generation traction substations, and three-level converters (key energy conversion units) have advantages over traditional two-level topologies. However, their P-O-N three-terminal DC-side topology introduces new faults (e.g., PO/ON bipolar short circuits, O-point-to-ground faults), making traditional protection strategies ineffective. In addition, wide system current fluctuation (0.5–3 kA) and offshore photovoltaic power fluctuation easily cause fixed-threshold protection maloperation, and the coupling mechanism among modulation strategies, DC bus capacitor voltage dynamics and fault current paths is unclear. To solve these bottlenecks, this paper establishes a simulation model of the system based on the PSCAD/EMTDC(A professional simulation software for electromagnetic transient analysis in power systems V4.5.3) platform, analyzes the transient electrical characteristics of three-level converters under traction and braking conditions for typical faults, clarifies the coupling mechanism, proposes a condition-adaptive fault identification strategy, and designs a reconfigurable fault energy handling system with bypass thyristors and adaptive crowbar circuits. Simulation and hardware-in-the-loop (HIL) experiments show that the proposed scheme completes fault identification and protection within 2–3 ms, suppresses fault peak current by more than 70%, limits DC bus overvoltage within ±10% of the rated voltage, and has good post-fault recovery performance. It provides a reliable and engineering-feasible protection solution for related systems and technical references for similar flexible DC system protection design. Full article

Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation. Full article

Vaccination behavior and epidemic spreading are strongly intertwined processes, and their coevolution is often shaped by both individual decision-making and social interactions. However, most existing studies model such interactions at the pairwise level, overlooking the potential impact of higher-order social influence arising from group interactions. In this work, we develop a coupled vaccination–epidemic spreading model on multilayer higher-order networks, where vaccination behavior evolves on a simplicial complex and epidemic propagation occurs on a physical contact network. The model incorporates imperfect vaccine efficacy, allowing vaccinated individuals to become infected, and introduces a hybrid vaccination strategy that combines rational cost–benefit evaluation with social influence from both pairwise and higher-order interactions, as well as negative effects induced by vaccine failure. By constructing the coupled dynamical equations, we analytically derive the epidemic outbreak threshold and elucidate how higher-order interactions, behavioral responses, and vaccine-related parameters jointly affect epidemic dynamics. Numerical simulations on networks with different structural properties validate the theoretical results and reveal pronounced structure-dependent effects. The results show that higher-order social interactions can significantly reshape vaccination behavior and epidemic prevalence, while network heterogeneity and vaccine imperfection play crucial roles in determining the outbreak threshold and steady-state infection level. These results emphasize the necessity of incorporating higher-order interactions together with realistic vaccination behavior into epidemic modeling and offer new insights for the design of effective vaccination strategies. Full article

Polyurethanes are widely used polymeric materials; their crosslinked structure and compositional diversity significantly hinder effective end-of-life management. The review emphasizes polyurethane recycling technologies, with chemical aspects discussed only insofar as they directly affect recyclability. The influence of polyol and isocyanate structure on phase separation, network architecture and thermal stability is discussed in the context of degradation and depolymerization mechanisms. Mechanical, chemical, thermochemical and emerging biological recycling routes are compared, with emphasis on their respective advantages, limitations and technological maturity. Mechanical recycling remains the most accessible option on an industrial scale but typically leads to reduced mechanical and thermal-insulation performance. Chemical recycling—particularly glycolysis, hydrolysis and aminolysis—enables partial recovery of polyols suitable for reuse in new polyurethane formulations, albeit at the cost of higher energy demand and increased process complexity. The environmental impact of polyurethane recycling is considered in terms of energy consumption, greenhouse-gas emissions, waste-reduction potential and alignment with circular-economy principles. Emerging biological and hybrid recycling strategies are highlighted as promising low-temperature alternatives with potential environmental benefits, despite their current low technological readiness. Key structural and technological barriers to efficient polyurethane recycling are identified, and future research directions toward improved sustainability and resource efficiency are outlined. Full article

Background/Objectives: This study introduces a reproducible and interpretable graph-based framework for resting-state functional near-infrared spectroscopy (fNIRS) that enables network-level biomarker discovery for Alzheimer’s disease (AD). Although resting-state fNIRS is well suited for task-free assessment, most existing approaches rely on static channel-wise features or conventional functional connectivity, limiting insight into coordinated network dynamics and reproducibility. Methods: Resting-state prefrontal fNIRS signals were represented as subject-level graphs in which edges captured coordinated fluctuations of nonlinear signal complexity across channels, computed using sliding-window analysis. Graph neural networks (GNNs) were employed as analytical tools to identify disease-stage-related network patterns. Interpretability was assessed using edge-level importance measures, and reproducibility was evaluated through fold-wise stability analysis and consensus network construction. Results: The proposed complexity–fluctuation-based graph representation consistently outperformed conventional amplitude-based functional connectivity. Statistically supported prefrontal network biomarkers distinguishing mild cognitive impairment (MCI) from healthy aging were identified, with statistically significant group differences (p = 0.001). In contrast, network patterns associated with Alzheimer’s disease were more heterogeneous and less consistently expressed. Consensus analysis revealed a subset of prefrontal connections repeatedly selected across cross-validation folds, and attention-based network patterns showed strong spatial correspondence with statistically derived biomarkers. Conclusions: This study establishes a reproducible and interpretable framework for resting-state fNIRS analysis that emphasizes coordinated complexity dynamics rather than classification accuracy. The results indicate that network-level alterations are most consistently expressed at the MCI stage, highlighting its role as a critical transitional state and supporting the potential of the proposed approach for longitudinal monitoring and clinically applicable fNIRS-based assessment of neurodegenerative disease. Full article

Traditional multiple-input multiple-output (MIMO) systems are confronted with significant challenges in realizing ubiquitous connectivity for sixth-generation (6G) networks, particularly in environments characterized by severe signal blockage and dynamic co-mobility. While aerial intelligent reflecting surfaces (AIRS) offer a promising paradigm to address these difficulties, the existing channel models often fail to capture fast channel changes, thereby leading to inefficient phase optimization in time-varying scenarios. To address these limitations, a geometric MIMO channel model is proposed for AIRS-assisted communications. This model comprises an indirect link from the base station (BS) via the AIRS to the receiver (Rx) and a direct BS-Rx link, whose direct propagation environment is rigorously characterized by a one-cylinder model specifically designed to tackle the complexities of dynamic co-mobility and intricate propagation. A joint optimization problem is formulated to maximize the achievable rate by optimizing the transmitted signal’s covariance matrix and the AIRS phase shift. Subsequently, an iterative algorithm employing the projected gradient method (PGM) is proposed for its solution, which is tailored for efficient operation in time-varying environments. Furthermore, expressions for the space–time correlation function and Doppler power spectrum are derived to evaluate the overall channel properties. Significant enhancements in achievable rates are demonstrated by AIRS, with substantial gains being observed even for a small number of reflecting elements. Consequently, crucial guidance for the design of robust AIRS-assisted MIMO systems is provided by these findings, and the broad applicability of the proposed algorithm is thereby confirmed. Full article

The development of effective antioxidants has evolved from descriptive analysis toward a precise, mechanism-driven discipline targeting the molecular “redox switch”. This review synthesizes the critical advances reported since 2021, focusing on how the interplay between polyphenolic architecture and electronic descriptors, such as bond dissociation enthalpy and ionization potential, governs radical scavenging through the HAT, SET, and SPLET pathways. We evaluate the dual influence of metal coordination, where interactions can either enhance antioxidant stability through σ bond polarization or trigger pro-oxidant transitions via ligand-to-metal charge transfer. Central to this progress is the integration of computational models (DFT, QSAR) with advanced synchrotron methodologies (XAS, STXM, SR-FTIR, and SAXS), which provide element-specific validation of antioxidant behavior and subcellular oxidative mapping within complex matrices. Furthermore, we highlight how these molecular insights inform formulation engineering, specifically the development of organic nanocarriers and hybrid delivery systems, such as metal–phenolic networks, that shield therapeutic cargo from degradation and govern release in challenging physiological environments. These fundamental studies provide an essential physicochemical basis for medicine by enabling a better understanding and the rational design of antioxidant drugs, dietary supplements, and antioxidant strategies. Full article

Background: The growing recognition of shared molecular pathways and molecular signatures between cardiovascular diseases and cancer has motivated interest in exploring antihypertensive-associated chemical space for oncological applications. Concurrently, artificial intelligence (AI)-driven molecular generation has enabled the rapid creation of virtual lead candidates for specific therapeutic indications, although their broader biological interaction profiles often remain unexplored. Methods: In this paper, we explore the computational screening of a library of AI-generated antihypertensive virtual lead compounds to evaluate their polypharmacological anticancer potential. The compounds were originally designed and prioritized for modulating $\mathsf{\beta }$-adrenergic receptors but are here re-evaluated in a cancer-focused context using a multi-stage in silico approach. We chose five (5) known cancer target proteins and performed compound profiling for drug-likeness, pharmacokinetic suitability, and safety. Docking simulations, binding free energy estimates, molecular interaction mapping, and pharmacophore modeling were used to evaluate the molecules’ interactions with the cancer-linked protein targets. We employed the binding free energy estimates of the ligand–protein complexes to determine compounds with polypharmacological anticancer potential. In addition, molecular dynamics simulations of some of the compounds with polypharmacological anticancer potential were employed to evaluate binding stability and dynamic behavior of selected ligand–target complexes. Results: Several compounds showed good docking scores, physicochemical characteristics, and pharmacokinetic profiles. Also, the results reveal that several AI-generated antihypertensive virtual leads exhibit favorable multi-target binding profiles, with consistent docking affinities and stable interaction networks across multiple cancer-related targets. Conclusions: Our findings suggest that several of the hypothetically evaluated compounds exhibit favorable physicochemical properties, acceptable predicted pharmacokinetic and safety profiles, and consistent predicted binding affinities across multiple cancer-relevant targets. Full article

Cadmium (Cd) contamination of agricultural soils threatens crop productivity and food safety by disrupting physiological and molecular processes in plants. Increasing evidence indicates that epigenetic regulation, including DNA methylation, histone modifications, and emerging epitranscriptomic marks such as RNA methylation, plays a crucial role in coordinating plant responses to Cd stress. In parallel, plant-associated microbiomes have emerged as influential modulators of metal uptake, antioxidant capacity, hormone signaling, and stress resilience. Yet the mechanisms by which microbiome-derived signals intersect with host chromatin and transcriptome regulation under Cd exposure remain poorly understood. This review synthesizes current knowledge on plant epigenetic responses to Cd stress and critically examines how microbial metabolites, phytohormones, and redox-active compounds shape plant regulatory networks. Network-based ecological studies reveal that increased microbial community complexity and cooperative interactions are consistently associated with reduced Cd accumulation and enhanced plant performance, suggesting that microbial organization itself may represent an additional regulatory layer influencing plant responses. Despite compelling conceptual links, direct experimental evidence connecting microbiome signals to stable epigenetic or epitranscriptomic reprogramming under Cd stress remains limited. To date, only limited experimental studies have demonstrated causal relationships between microbial cues and host DNA or RNA methylation dynamics in Cd-exposed plants, highlighting clear mechanistic potential while also underscoring remaining knowledge gaps. By integrating physiological, ecological, and chromatin-level perspectives, this review identifies key unanswered questions and outlines future research directions to establish causal links between microbial community dynamics, epigenetic regulation, and long-term Cd stress adaptation in plants. Full article

Accurate inversion of the deep initial in situ stress field is a fundamental prerequisite for stability analysis of surrounding rock in underground engineering, roadway support design, and prevention and control of dynamic disasters. To address the problems of scarce in situ stress measurements in deep mining areas, the inability of conventional regression methods to capture the nonlinear characteristics of complex tectonic stress fields, and the tendency of traditional inversion algorithms to fall into local optima and overfitting, this paper proposes an intelligent inversion method based on support vector regression optimized by the artificial bee colony algorithm (ABC-SVR). The artificial bee colony algorithm is employed to adaptively optimize the core parameters of the SVR model, thereby enabling high-precision inversion of complex deep stress fields. Comparing the results with acoustic emission tests demonstrated that the ABC-SVR model significantly outperforms conventional SVR and backpropagation neural networks across various performance metrics. The inversion results show high consistency with the measured data, achieving a root mean square error (RMSE) of 1.25, a mean absolute percentage error (MAPE) of 4.16%, and a coefficient of determination (R²) of 0.908. This method can rapidly reconstruct high-precision initial in situ stress fields in deep unmined regions, providing highly reliable boundary conditions for numerical simulations and demonstrating significant engineering application potential. Full article

The assessment of reliability in non-repairable subsystems of mining electronic equipment represents a computationally challenging problem, particularly for complex and highly connected structures. This study presents a systematic comparative analysis of several deterministic approaches for reliability estimation, focusing on their computational efficiency, accuracy, and applicability. The investigated methods include classical boundary techniques (minimal paths and cuts), analytical decomposition based on the Bayes theorem, the logic–probabilistic method (LPM) employing triangle–star transformations, and the algorithmic Structure Convolution Method (SCM), which is based on matrix reduction of the system’s connectivity graph. The reliability problem is formally represented using graph theory, where each element is modeled as a binary variable with independent failures, which is a standard and practically justified assumption for power electronic subsystems operating without common-cause coupling. Numerical experiments were carried out on canonical benchmark topologies—bridge, tree, grid, and random connected graphs—representing different levels of structural complexity. The results demonstrate that the SCM achieves exact reliability values with up to six orders of magnitude acceleration compared to the LPM for systems containing more than 20 elements, while maintaining polynomial computational complexity. Qualitatively, the compared approaches differ in the nature of the output and practical applicability: boundary methods provide fast interval estimates suitable for preliminary screening, whereas decomposition may exhibit a systematic bias for highly connected (non-series–parallel) topologies. In contrast, the SCM consistently preserves exactness while remaining computationally tractable for medium and large sparse-to-moderately dense graphs, making it preferable for repeated recalculations in design and optimization workflows. The methods were implemented in Python 3.7 using NumPy and NetworkX, ensuring transparency and reproducibility. The findings confirm that the SCM is an efficient, scalable, and mathematically rigorous tool for reliability assessment and structural optimization of large-scale non-repairable systems. The presented methodology provides practical guidelines for selecting appropriate reliability evaluation techniques based on system complexity and computational resource constraints. Full article

Unlike conventional positioning systems that rely on multiple sensors, the positioning system proposed in this study uses a single anisotropic magnetoresistive (AMR) sensor to measure the magnetic field of a target permanent magnet. This approach significantly reduces the system hardware cost and complexity, facilitating the miniaturization of positioning systems. Leveraging a BP neural network model, which is shown to be fast and accurate, the positioning system obtains the real-time magnetic field of the target magnet using a single sensor, subsequently converting three-axis magnetic field data into coordinate information to achieve real-time tracking and localization. The results show that the root mean square errors (RMSEs) for the X and Z axes in the simulation are 0.27 mm and 0.26 mm, respectively, while the RMSEs for the X, Y, and Z axes in the actual test are 0.83 mm, 1.15 mm, and 0.85 mm, respectively. It is also observed that the positioning error correlates with variations in the magnetic field with respect to position, which originate from the strong distance-dependent nonlinearity of the magnetic field. This method not only reduces hardware costs but also maintains accuracy. It is particularly well-suited to applications requiring high-precision positioning and tracking, achieving millimeter-level accuracy within a volume of 50 × 40 × 40 mm³. It has potential applications in aerospace intelligent connectors, medical devices and automation systems, where space and signal lines are limited. Full article

Partial discharge (PD) signals in gas-insulated switchgear (GIS) exhibit complex characteristics, and single-modal feature recognition methods face limitations in achieving satisfactory diagnostic accuracy due to incomplete fault information representation. This paper proposes a multi-modal fault diagnosis framework that effectively integrates complementary information from different sensing modalities to improve defect identification performance. First, PRPD time-domain statistical features from HFCT measurements and frequency-domain features from UHF signals are extracted to construct a comprehensive hybrid feature set. Z-score normalization is applied to eliminate scale differences between heterogeneous features. Principal component analysis (PCA) is then employed for dimensionality reduction, preserving essential discriminative information while removing redundancy. Finally, a ResNet-MLP classifier with skip connections is designed to enhance nonlinear feature extraction and alleviate gradient vanishing problems in deep network training. Experimental validation on four typical defect types—protrusion defect, floating discharge, metal particle discharge, and surface discharge on insulator—demonstrates that the proposed method achieves 99.38% classification accuracy on the test set, with consistently high precision, recall, and F1-score across all categories. The proposed approach significantly outperforms standard MLP without residual connections, achieving 98.94% ± 0.49% accuracy compared to 95.47% ± 3.72% over 20 independent runs, demonstrating superior diagnostic accuracy and generalization capability for GIS insulation fault diagnosis. Full article