Search Results (2,277)

A distribution feeder is built meshed and operated radially, so at any instant it occupies one of a combinatorial family of topologically radial configurations. We show that this family, weighted in the natural maximum-entropy way, is a determinantal point process whose kernel is the transfer-current matrix of the network, and we read that kernel in the operator’s language: the probability that a line section is energised equals its own self transfer-current factor, Foster’s sum rule is the trace identity, and the covariance of two switching states is minus the square of their normalised transfer current. Independent faults leave the feeder exactly within this family for any number of faults, whereas no restoration mechanism ignorant of the section resistances can return it there; among those that can, one is canonical, being the unique mechanism that reverses the fault, and its weight is the section’s transfer-current factor in the post-fault network with everything still energised shorted. We show, and report, that these weights are a structural diagnostic and not a repair priority. The maintained feeder is solved in closed form, and an exact transport equation prices what a reinforcement programme costs the feeder’s ability to reconfigure. The central spanning-tree and sector identities are verified against exhaustive enumeration; the dynamical and sensitivity statements are checked by exact master-equation computations and finite differences. Full article

This study evaluates a hierarchical workflow for fault diagnosis in transmission systems by integrating established machine-learning techniques for fault-type classification and zonal fault location with a complementary double-ended traveling-wave procedure for point-location estimation. The contribution lies at the level of process integration and comparative evaluation rather than in the proposal of a new machine-learning or traveling-wave algorithm. The methodology was evaluated using the IEEE 9-bus test system. Symmetrical and asymmetrical short-circuit scenarios were automatically simulated at multiple positions along six transmission lines using DIgSILENT PowerFactory, and the resulting oscillographic records were exported in COMTRADE format, producing a database of 2952 fault events. Phase voltages and currents, together with positive-, negative-, and zero-sequence components, were used to evaluate Decision Trees, Self-Organizing Maps (SOM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) for fault-type classification and zonal fault location. Under the simulated noise-free conditions and the adopted fixed hold-out partition, all four algorithms correctly classified the 591 fault-type testing observations, yielding 100% test-set accuracy. This result characterizes the specific evaluation subset considered in the study; repeated, cross-validated, or grouped partitions were not performed, and neighboring simulated fault positions may therefore be represented across the training and testing subsets. For zonal fault location, the ANN exhibited the strongest and most consistent observed performance in the retained 590-event evaluation set, with class-specific recall values between approximately 0.96 and 0.99 across the six fault zones, whereas the Decision Tree provided a favorable compromise between zonal discrimination and computational efficiency. Some model-specific hyperparameter values from the original executions are unavailable in the retained experimental record, which limits exact replication of those original configurations; the reported results correspond to the evaluated executions documented in this study. As a complementary third component of the workflow, the double-ended traveling-wave procedure based on discrete wavelet analysis was illustrated for one AG event simulated at 25% of the transmission-line length, producing a normalized point-location estimate of approximately 25.07% from the local terminal. This single-event analysis demonstrates the operation of the traveling-wave processing sequence, while broader multi-event validation is outside the present experimental scope. Overall, the results demonstrate the coordinated application of fault-type classification, zonal fault location, and traveling-wave-based point-location refinement within a common diagnostic workflow. The findings should be interpreted within the deterministic simulation conditions, fixed evaluation subsets, and experimental records considered in this study. Full article

Long-term monitoring provides a unique opportunity to distinguish persistent ecological processes from short-term fluctuations in forest insect dynamics. However, the extent to which local insect populations persist through time and the mechanisms regulating their occupancy remain poorly understood. Using a 13-year dataset from the Polish ICP Forests network, we investigated temporal and spatial dynamics of insect occurrence in alder stands. We quantified persistence at tree and plot levels, evaluated signatures of density-dependent regulation, and assessed spatial structure using generalized additive models, logistic regression, transition analyses, and spatial autocorrelation metrics. Insect occurrence exhibited exceptionally strong temporal persistence. Occurrence in the previous year was the dominant predictor of current occurrence across all modeling approaches. Persistence patterns differed between plot and tree levels, with transition analyses revealing greater long-term stability at the level of monitoring plots, consistent with the long-term stability of local populations despite turnover among host trees. Occupancy dynamics were consistent with negative density-dependent regulation, with local populations fluctuating around a stable equilibrium occupancy of approximately 75%. Although year-to-year changes occurred regularly, most fluctuations were relatively small and rarely resulted in complete disappearance of populations from occupied locations. Insect occurrence also exhibited pronounced spatial structure. Occupancy remained significantly clustered throughout the study period, indicating persistent regional differences in local population abundance. In contrast, annual changes in occupancy showed only weak spatial autocorrelation, suggesting that short-term dynamics were driven primarily by local ecological processes rather than by highly synchronized regional fluctuations. Our results indicate that infestation patterns in alder stands are consistent with persistent and self-regulating local herbivore populations that remain associated with the same forest stands over extended periods. Long-term dynamics are governed primarily by temporal persistence, density-dependent feedback, and stable spatial structure rather than by repeated cycles of colonization and extinction. These findings highlight the importance of long-term monitoring for understanding population persistence and provide new insight into the processes maintaining herbivore populations in forest ecosystems. Full article

Drug–target interaction (DTI) prediction is a critical step in drug discovery, and accurate prediction of potential interactions can significantly accelerate the drug-development process. Although deep-learning approaches have achieved promising performance in DTI prediction, two challenges remain: single models often fail to comprehensively capture heterogeneous sequence information, resulting in limited stability and generalization, while insufficient integration of local and global features restricts interaction representation. To address these limitations, we propose HFEDTI, a DTI prediction model that integrates hierarchical feature fusion and weighted ensemble learning. Specifically, a residual convolutional neural network (ResCNN) is employed to extract local structural features of drugs and targets, while a self-attention-based hierarchical bidirectional long short-term memory network (SAHBiLSTM) captures global contextual dependencies. Furthermore, a hierarchical heterogeneous attention mechanism is introduced to align and fuse multi-level cross-modal representations, and a weighted ensemble strategy based on validation performance ranking is developed to enhance model robustness and generalization. Experimental results on three benchmark datasets demonstrate the effectiveness of HFEDTI. On the DrugBank dataset, HFEDTI achieves an AUC of 0.9238 and an AUPR of 0.9327, improving the best-performing baseline by 0.90 and 1.40 percentage points, respectively. Moreover, HFEDTI consistently achieves strong performance on the C. elegans and Human datasets, further validating its effectiveness and generalization capability for DTI prediction. Full article

This paper proposes an on-chip time-to-digital converter (TDC) named pulse delay and capture circuit (PDCC), dedicated to single-event transient (SET) pulse measurement for radiation characterization of flash-based FPGAs. To address the inherent trade-offs of mainstream vernier delay line (VDL) and snapshot TDC schemes, the proposed PDCC employs a 16-stage single-chain self-triggering capture circuit with a SET indicator flip-flop, incorporates heterogeneous-cell ring-oscillator-based process-voltage-temperature (PVT) calibration, and avoids the need for resource-intensive first-in-first-out (FIFO) buffers. Implemented on a 130 nm A3PE600 flash-based FPGA, the PDCC achieves a nominal theoretical resolution of 640 ps and a nominal theoretical dynamic range of 0.64–10.24 ns, with 53.5% lower logic resource overhead than the VDL benchmark. Heavy-ion irradiation experiments validate the measurement capability and reveal asymmetric SET pulse propagation evolution characterized by positive-pulse broadening and negative-pulse attenuation, as well as sensitivity disparities among logic gates, which are attributed to the physical implementation of internal transistor networks within the VersaTile. A multi-criteria assessment based on the Simple Additive Weighting (SAW) method, evaluating resolution, dynamic range, logic resource overhead, and measured SET pulse width, yields an overall performance score (OPS) of 1.43, corresponding to a 43% higher score relative to the VDL baseline. Full article

Millimeter-wave (mmWave) radar-based gesture recognition has attracted increasing attention for real-time human–computer interaction owing to its robustness to illumination changes, privacy-preserving sensing capability, and suitability for embedded deployment. However, existing single-stream models often couple heterogeneous point-cloud and temporal statistical features, which may limit their ability to capture fine-grained motion patterns and key action frames. To address this problem, this paper proposes a dual-stream long short-term memory network (LSTM) and a bidirectional gated recurrent unit (BiGRU) with an attention mechanism (Attention-BiGRU) network for mmWave radar-based hand gesture recognition, termed as DSTG-Net. In the proposed DSTG-Net framework, an LSTM branch is used to process radar point-cloud sequences and extract fine-grained spatio-temporal features, while an Attention-BiGRU branch models global motion trends from statistical and temporal-difference features. The attention mechanism in the Attention-BiGRU branch is introduced to emphasize discriminative frames during gesture transitions, and the complementary features from the two branches are fused through feature concatenation for final classification. The proposed method is evaluated on a public mmWave radar gesture dataset to verify its recognition performance, and an additional self-built near-field dataset is used to test its effectiveness under a constrained acquisition scene. The proposed method achieves recognition accuracies of 97.40% and 98.75% on the two datasets, respectively, outperforming several baseline models. The Raspberry Pi-based implementation with a TI IWR1642 radar confirmed the functional feasibility of the proposed pipeline. Full article

Accurate prediction of bus arrival time is critical to improving transit service reliability and scheduling efficiency. The estimated time of arrival (ETA) is jointly determined by two processes, namely inter-station travel and station dwell, which are dominated by different traffic mechanisms and thus follow essentially different operating patterns. Most existing methods model the entire route in a unified way, making it difficult to account for both processes simultaneously. This paper therefore proposes a feature-separation prediction method. For inter-station travel time, which is driven by the continuous evolution of traffic flow, we design a Dual-Branch Spatio-Temporal Graph Convolutional Network (DSTGCN); its spectral graph convolution and spatial self-attention branches work together to capture road-network topology and the spatial propagation of traffic flow. For station dwell time, which follows a long-tailed distribution, we design a Spatio-Temporal Graph Convolutional Network with Transformer (STGCN-Trans) and introduce a robust loss function to suppress the effect of extreme outliers. Comparative and ablation experiments show that both models achieve the lowest RMSE, reducing it by 21.7% and 6.7% over the second-best baselines, respectively. They also remain stable under disturbances such as peak hours and extreme weather. Full article

We analyse whether Darwinian natural selection acts as a metabolic-entropy-production-increasing process in a model of an open chemostat ecosystem, where Michaelis–Menten uptake kinetics are grounded in a thermodynamically consistent mesoscopic chemical-reaction-network model, and the continuum limit of the discrete replicator equation is derived as a Crow–Kimura reaction–diffusion process. On the quasi-static ecological manifold, an exact Fisher-type identity yields a monotonic increase in the metabolic entropy-production rate, proportional to the uptake-rate variance. Solving the reduced moment equations in closed form, under an explicit quasi-static and zero-skewness closure, identifies a thermodynamic ceiling imposed by mass conservation, while phenotypic trade-offs produce a finite sub-ceiling. When ecological and evolutionary timescales become comparable, a frozen-parameter Routh–Hurwitz analysis identifies an instantaneous spectral instability boundary whose scope and self-consistency are assessed for both unbounded and bounded trait models. The results of this analysis delineate the precise dynamical and biochemical conditions under which selection increases metabolic entropy production and the regimes in which that tendency is reshaped. Full article

Metro station areas are transitioning from single-purpose transportation transfer nodes into multifunctional public spaces that integrate public services, cultural displays, and place-based experiences. However, there remains a lack of systematic evaluation tools for assessing public cultural service quality in metro station areas that integrate cultural meanings, public identity, and experiential expressions. An Analytic Network Process (ANP) network structure was established based on expert judgments to determine the weights, and Technigue for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied for comprehensive evaluation and ranking using the scores provided by professional evaluators for 13 sampled stations in Foshan, Xi’an, and Shenzhen. Analytic Hierarchy Process (AHP)-TOPSIS was used for robustness testing, and dimensional weight perturbation analysis was conducted to assess model stability. Six types of Point of interests (POIs) were further incorporated to characterize the external functional contexts of the sampled stations. The results showed that the weights of Authenticity, Legitimacy, and Theatricality were 0.498, 0.296, and 0.206, respectively. Traditional, Self-expressive, Charismatic, Exhibitionistic, and Utilitarian were identified as the key indicators with relatively high weights. The 13 sampled stations showed significant differences in overall performance. Higher-performing cases demonstrated more balanced performance across the three scene dimensions of Authenticity, Legitimacy, and Theatricality. In contrast, lower-performing cases showed relatively weaker performance on some key indicators. The robustness test based on AHP-substituted weights and Spearman correlation analysis verified the stability of the evaluation framework, while the sensitivity analysis showed that the evaluation results remained relatively stable under weight perturbations. This study extends the application of scene theory in metro station area research and provides an evaluation framework and practical reference for optimizing public cultural service spaces in transit-oriented urban environments. Full article

To address the voltage violation problem in distribution networks with large-scale distributed generators (DGs), this paper proposes an optimized control strategy for power self-balancing in distribution networks based on virtual power plant (VPP) aggregation. An optimized control architecture for power self-balancing is constructed, comprising a VPP aggregation layer, an independent optimization control layer for individual VPPs, a coordinated optimization control layer for multiple VPPs, and an emerging benefits allocation layer. Then, at the VPP aggregation layer, a VPP aggregation method is proposed considering VPP benefit coupling degree, resource adequacy, and coordination interaction degree. At the independent optimization control layer, an independent optimization control model incorporating active and reactive power regulation of DGs is established for each VPP to achieve power self-balancing for voltage control within the VPP. At the coordinated optimization control layer, a multi-VPP coordination optimization model is constructed based on a linking matrix to achieve coordination among multiple VPPs during the power self-balancing control process and obtain emerging benefits. At the emerging benefits allocation layer, an allocation model based on the contribution degree of each VPP is established to ensure fair distribution of the emerging benefits. Finally, the effectiveness of the proposed method is validated using an actual 10 kV feeder system in Zhejiang Province, China. Full article

Earthquakes remain among the most destructive natural hazards, necessitating reliable monitoring and early warning systems for effective risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved seismic signal analysis, enabling more accurate event detection, phase picking, classification, and magnitude estimation. This study presents a systematic review of ML- and DL-based approaches for earthquake monitoring, with particular emphasis on Distributed Acoustic Sensing (DAS) as an emerging technology for high-resolution, real-time seismic observation. Following the PRISMA 2020 guidelines, a systematic literature search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar, yielding 252,223 initial records. After applying the predefined publication period, removing duplicate records, conducting relevance screening, and performing eligibility assessment, 138 peer-reviewed studies published between 2021 and 2025 were retained for detailed analysis and synthesis. The review reveals a significant transition from conventional signal-processing techniques to advanced artificial intelligence-based approaches, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Bidirectional Long Short-Term Memory (BiLSTM) networks, Transformer-based architectures, hybrid models, and Bayesian learning methods for uncertainty quantification. The findings further demonstrate that Distributed Acoustic Sensing (DAS) has emerged as a transformative sensing technology because of its dense spatial coverage, high spatial resolution, and continuous monitoring capability. However, several challenges remain, including the lack of standardized datasets, limited model generalization across diverse geological settings, insufficient model interpretability, high computational complexity, and the limited integration of uncertainty-aware approaches for real-time seismic monitoring. This review identifies these critical research gaps and highlights promising future research directions, including multimodal data fusion, interpretable artificial intelligence, physics-informed learning, self-supervised learning, and robust uncertainty quantification for next-generation intelligent seismic monitoring systems. Unlike previous review studies that primarily focus on individual machine learning techniques or conventional seismic monitoring, this review provides a comprehensive and systematic synthesis of recent advances in machine learning, deep learning, and Distributed Acoustic Sensing (DAS), identifies current research gaps, and offers practical recommendations to guide future research on intelligent earthquake monitoring systems. Full article

Background: Population aging has led to an increasing proportion of older adults (aged 65 and older) acting as informal caregivers. These caregivers face risks of burden, stress, and depression. While research frequently documents negative psychological outcomes, resilience represents a crucial protective process. This systematic review synthesizes and maps the empirical evidence regarding the association between the socio-ecological resilience process and the well-being of older informal caregivers. Methods: Following Joanna Briggs Institute (JBI) mixed-methods guidelines, a convergent segregated mixed studies systematic review was conducted. A systematic search of Medline (PubMed), CINAHL, PsycINFO, and Scopus identified primary articles (2014–2025) in English and Spanish. Methodological quality was appraised using JBI critical appraisal checklists and the Mixed Methods Appraisal Tool. PROSPERO registration number: CRD420251142190. Results: Thirty-one studies were included. Elevated caregiver resilience was associated with lower reported symptoms of depression and anxiety, higher self-rated health, and positive psychological adaptation. Framed within a social ecological model, personal resources such as spirituality, hope, and self care, alongside relational assets like dyadic relationship quality and mutual coping, demonstrated a positive association with resilience and a reduction in subjective burden. Within the community context, informal peer networks and Online Health Communities functioned as essential supportive resources. On a structural level, both household wealth and the regional availability of long-term care beds acted as moderators for spousal well-being, whereas exceeding 30 weekly caregiving hours acted as a temporal threshold for positive adaptation. Conclusions: These findings suggest that resilience in older caregivers may be best conceptualized not as a static individual trait, but as a multi-level, dynamic socio-ecological process. Rather than relying on individual coping alone, public policies and clinical practice should prioritize systemic, relational, and structural environmental support, including formal respite services and long-term care infrastructure, to preserve the well-being of older spousal caregivers. Full article

This study introduces a training-free, self-contained adaptive low-light image enhancement (LLIE) framework driven by a metaheuristic optimization algorithm (MOA) and a context-aware dual image quality assessment (IQA) engine. Although deep-learning-based methods exhibit rapid inference, their static parameters often suffer from severe performance degradation in out-of-distribution (OOD) scenarios—such as those involving unseen sensor noise or environmental shifts. To bridge this generalization gap, the proposed framework operates within a decoupled HSV color space, specifically targeting the luminance ($V$) channel to formulate image enhancement as an instance-specific optimization task. We introduce a novel hybrid Log-Gamma mapping function that mathematically unifies the localized dark-stretching capabilities of logarithmic compression with the global dynamic range regulation of power-law gamma curves, thereby substantially expanding the expressiveness of the transformation space. To govern parameter convergence without reference images, a multi-stage Low-Light Image Discrimination (LLID) engine classifies the input frame by computing context-specific trimmed skewness residuals and global intensity means, effectively mitigating highlight biases. Under normal-light conditions, the swarm intelligence engine optimizes the Log-Gamma coefficients via the Patch-based Contrast Quality Index (PCQI) to maximize structural fidelity; conversely, under severe low-light degradations, the framework leverages the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) to minimize spatial artifacts. Using the Marine Predators Algorithm (MPA), the framework iteratively searches the continuous bounding space to fine-tune a parameter matrix tailored exclusively to each image. Empirical evaluations across four benchmark datasets (Bicycle, DF1000, DICM, and VV) validate the effectiveness of the proposed paradigm. The proposed variant, $OLG\left(\mathrm{MPA}\right)$, secured the top average rank in internal algorithm ablation ($\overline{R}=3.10$) and achieved a competitive global average rank ($\overline{R}=2.95$) against four state-of-the-art deep networks, matching the performance of leading data-driven models. Although the evolutionary optimization loop incurs an average per-frame latency of $17.300 \mathrm{s}$, this instance-specific paradigm successfully trades instantaneous processing speed for absolute domain adaptability and predictable, artifact-free image restoration. Full article

Building community discovery aims to identify spatially continuous and cognitively coherent groups of buildings that represent meaningful urban spatial structures. Existing methods mainly integrate geometric similarity, spatial proximity, and topological adjacency with handcrafted grouping rules or supervised learning but rarely use spatial-cognitive knowledge to guide the learning process, limiting the ability to distinguish ordinary geometric adjacency from cognitively meaningful building associations. To address this limitation, this study proposes a knowledge-guided graph representation learning framework for building community discovery. First, a spatial-cognitive building knowledge graph is constructed by integrating building attributes, spatial relationships, and cognitive organization principles. Then, spatial-cognitive knowledge is quantified as cognitive affinities between neighboring buildings and incorporated into a graph neural network model to guide self-supervised graph representation learning. Finally, the learned node embeddings are used to identify building communities through graph-based community discovery. Experimental results across 50 cities showed that the proposed method produces building communities with good spatial continuity and cognitive consistency. Comparative analysis and ablation studies further confirm the effectiveness of integrating spatial-cognitive knowledge with graph representation learning. Full article

The Olfactory System is receiving increasing attention in recent years as a potential biomarker for Alzheimer’s disease (AD). Early-stage AD is often associated with a decline in olfactory function, with studies suggesting that olfactory memory deficit may precede cognitive symptoms. We explore the intricate relationship between the Olfactory System and Alzheimer’s disease, examining both the neuroanatomical and physiological changes that occur in the olfactory pathways during the progression of AD. Memory, as a functional feature, was modeled using artificial neural networks, and it was related to the macro-parameters of the network. While these networks cannot completely capture the intricacies and functions of the human brain, they provide a clear understanding of how processes occur within it. Neural networks exhibited memory domains, defined by stable and unstable steady states; the former can be considered a prerequisite for memory storage and recall; the latter can be considered threshold values between stable steady states. Additionally, by introducing division of the neural population into subpopulations, the networks manifested multiple stable states, corresponding to multiple memory domains, which in a qualitative manner suggest hierarchically organized nonlinear complexity and multi-scale self-similarity of memory processes. Full article