Search Results (471)

With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. Full article

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential V(x,t) and weighted by a time-dependent strength γ_t. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on γ_t. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes. Full article

Single-cell genomics offers novel insights into genomic heterogeneity within cell populations, reframing our understanding of human development, tumorigenesis, and aging. However, constrained by the picogram-scale DNA templates of individual cells, Whole-Genome Amplification (WGA) remains a necessary precondition. Current quality control frameworks primarily focus on amplification uniformity but fail to capture the molecular independence of DNA amplicons, leading to an overestimation of information content in redundant WGA libraries. Here, we propose the Depth of Independent Amplicons Gauge (DIAG) to accurately quantify the effective number of amplicons derived from the primary template. The robustness of the DIAG was first validated using in silico datasets, revealing that the Depth of Independent Amplicons (DIA) is directly coupled with the precision and specificity of mutation calling. Furthermore, we established an organoid-derived ground-truth to evaluate mutation fidelity in real biological contexts, confirming the practical utility of the DIAG. Our results demonstrate that the DIAG provides a high-fidelity assessment of an individual WGA library without the need for costly external experiments, especially in Single-Nucleotide Variant (SNV) calling. Furthermore, we revealed that traditional uniformity indices, like the Gini index or Kullback–Leibler (KL) divergence, exhibit incongruous fluctuations under down-sampling perturbations. In contrast, the DIA remains a robust and high-fidelity predictor of mutational accuracy, maintaining stability across varying sequencing strategies. Finally, we conducted a systematic comparison of current single-cell Whole-Genome Amplification (scWGA) strategies, providing a standardized benchmarking of diverse technologies for high-resolution single-cell mutation analysis. Full article

The attribution of systemic financial stress to specific market sectors requires metrics that are faithful to the model’s computations, statistically consistent, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through information geometry, contributing to SDGs 7, 8, 9, and 17 through an entropic causal chain linking energy infrastructure failure to financial market stress. We conjecture and empirically verify the Entropy–Saliency Equivalence: Metabolic Saliency is an asymptotically unbiased estimator of the local Kullback–Leibler divergence between stressed and resting sector return distributions, with bias decaying at a parametric rate under Gaussian regularity conditions. The finite-sample bias–variance decomposition of the Kraskov–Stögbauer–Grassberger transfer entropy estimator is derived, establishing a minimax-optimal convergence rate. A novel metric, the Spatio-Temporal Information Flux (STIF), quantifies directed inter-sector stress transmission in bits per trading day, providing a bootstrap-calibrated audit trail aligned with the South African Financial Sector Regulation Act and MiFID II. Empirical validation on the JSE canonical panel (87 securities, 2857 trading days, 2015–2026) with Eskom load-shedding stages as exogenous stress injectors confirms the equivalence ($R^2=0.810$, $\widehat{\rho }=0.90$), with walk-forward $R^2=0.789$ and placebo $R^2=0.081$ ruling out estimation artefacts. The energy sector is identified as the primary stress transmitter during Stage 4+ Eskom events (STIF rising from 0.14 to 0.43 bits/day, directional asymmetry ratio 4.7). Robustness checks confirm stability across non-Gaussian securities and rolling transfer entropy windows. Full article

In fixed-view interaction scenarios of unmanned restaurants, face detection models face two core bottlenecks: the mismatch between training data distribution and real deployment scenarios, and the misalignment between model feature capacity allocation and business priority. To address these problems, this paper takes YOLOv8n (You Only Look Once version 8n) as the baseline, proposes a unified Scale-Aligned Capacity Allocation (SACA) theoretical framework, and constructs an end-to-end Scale Distribution Reconstruction Network (SDRNet) for lightweight face detection. First, we define the SACA loss with KL (Kullback-Leibler) divergence as the core optimization objective, which mathematically characterizes the matching degree between model capacity allocation and real scene face scale distribution. Second, a two-stage scene-aware scale distribution reconstruction strategy is designed based on the SACA framework, which derives the core face scale interval of the unmanned restaurant scene through a monocular imaging model, and constructs a scene-adaptive training dataset based on the public WIDER FACE benchmark, which is highly consistent with the real scale distribution of unmanned restaurant scenarios. Third, three scale-aligned lightweight modules, including LFEM (Lightweight Feature Extraction Module), LDown (Feature Segmentation and Sparse Optimization Module), and MSCH (Multi-Feature Shared Convolution Module), are proposed to realize the priority allocation of model capacity to core interaction scales, achieving collaborative optimization of data distribution and model structure. Fourth, a 2 × 2 controlled experiment is designed to separate the independent contributions of the data strategy and architectural improvements, and the robustness of the proposed model is verified on the standard WIDER FACE benchmark. Finally, a scale-specific validation mechanism is established to conduct fine-grained evaluation of the model’s detection performance on faces of different scales, avoiding the overall indicator masking the accuracy fluctuation of core scenarios. Experimental results show that the parameters of the proposed model are reduced to 1.76 M (a decrease of 41%), and the computational complexity is reduced to 5.5 GFLOPs (Giga Floating-point Operations Per Second) (a decrease of 32%). The mAP@0.5 (mean Average Precision) of the core medium-scale face reaches 0.684, with the performance loss controlled within 2% compared with the baseline. On the standard WIDER FACE benchmark, the model maintains competitive detection accuracy under extreme lightweight compression, which fully verifies its robustness. On the NVIDIA Jetson Orin NX embedded platform, the inference frame rate of TensorRT-FP16 reaches 79.9 FPS (Frames Per Second), which fully meets the real-time deployment requirements of resource-constrained unmanned restaurant scenarios. Full article

Hand gesture recognition (HGR) is a critical area in computer vision that supports intuitive human–computer interaction and sign language communication, yet existing systems remain sensitive to lighting variations, background clutter, and diverse hand postures. This study introduces two contributions to address these limitations: a Gradient-Based Augmentation Validation (GBAV) framework that establishes structurally safe augmentation ranges before training, and a multi-backbone Convolutional Neural Network (CNN) architecture combining ResNet50 and InceptionV3 with optional attention-based pooling. GBAV uses magnitude-weighted gradient orientation histograms with Pearson correlation and Kullback–Leibler divergence thresholds to verify label invariance under spatial transformations, providing a classifier-agnostic pre-training calibration mechanism. The proposed framework is evaluated on three static gesture datasets, Indonesian Sign Language (BISINDO), American Sign Language (ASL), and Hand Gesture 14 (HG14), yielding validation accuracies of 96.87%, 99.92%, and 95.25%, respectively, with 5-fold cross-validation on HG14 confirming result stability (93.51% ± 2.31%). Quantitative attention localization, cross-dataset transfer evaluation, and computational efficiency analysis (26.8 ms per image, ~37 FPS) further support the framework’s robustness and practical deployability. These findings establish GBAV-calibrated augmentation as the principal performance driver, which complements the multi-backbone architecture for robust hand gesture recognition across diverse visual contexts. Full article

Capsizing, sinking, and flooding accidents occurring in the coastal waters of the Republic of Korea constitute a persistent marine safety concern, accounting for approximately 17% of total fatalities associated with marine accidents. Previous statistical analyses of accident causation have identified key contributing factors such as adverse weather conditions, improper cargo loading, and deficiencies in vessel maintenance; however, the complex interdependencies among these factors have not been sufficiently quantified. To address this limitation, this study proposes a fuzzy Bayesian network (FBN) model to systematically evaluate and quantify the risk factors associated with capsizing, sinking, and flooding accidents. A total of 164 adjudicated marine accident cases that occurred in Korean coastal waters over a 10-year period (2015–2024) were analyzed (data collection cutoff: 31 December 2024) to estimate prior probabilities for six major causal categories. Conditional probability tables (CPTs) were derived through a structured Delphi survey conducted with marine safety experts possessing more than 10 years of professional experience. To mitigate the subjectivity inherent in expert judgment, triangular fuzzy numbers (TFNs) and centroid-based defuzzification were applied. Sensitivity analysis identified sea state (SI = 0.0155) and cargo loading condition (SI = 0.0125) as the two most influential factors affecting the probability of capsizing. Scenario analysis further revealed that when adverse weather conditions and improper cargo loading occur simultaneously, the probability of capsizing increases to 39.3%, representing a 5.3 percentage point increase compared to the baseline. In addition, the model demonstrated a close agreement with observed accident outcome distributions, with a Kullback–Leibler (KL) divergence of 0.038, indicating differences within 1.3 percentage points across all outcome categories. The findings of this study provide practical implications for targeted marine safety interventions and the prioritization of regulatory measures in the coastal waters of the Republic of Korea. Full article

Multi-agent reinforcement learning (MARL) relies on trial-and-error interactions to update policies. However, trial-and-error learning typically requires extensive interactions to achieve satisfactory performance, resulting in low sample efficiency, which limits its application in the real world. To reduce the trial-and-error costs of MARL and accelerate the convergence of multi-agent collaborative policies, we propose a MARL policy transfer method named LoLM-MARL, based on fine-tuning large language models (LLMs). First, leveraging the general world knowledge and reasoning capabilities of LLMs, low-rank adaptation (LoRA) is employed to fine-tune the pre-trained model on source tasks, thereby providing general decision-making knowledge for cross-scenario policy transfer. Second, a dynamic prompt construction method for LLMs is designed. By dynamically eliminating the state information of ineffective agents from the prompts, the method provides denser observation data for the large language model, thereby enhancing its policy representation capability in specific complex collaborative scenarios. Meanwhile, the dynamic prompt design concept enriches the training sub-scenarios for the algorithm, thereby laying the foundation for the model to learn more general decision-making knowledge. Finally, a Kullback–Leibler (KL) divergence regularization method based on an annealing strategy is constructed to ensure consistency between the policy distributions of the fine-tuned model and the pre-trained model, effectively mitigating the catastrophic forgetting problem during the fine-tuning process of the pre-trained model. Experimental results show that in zero-shot transfer tasks, LoLM-MARL achieves a maximum improvement of 101.4% in average win rate compared to existing state-of-the-art (SOTA) methods. In six few-shot transfer tasks, our method consistently achieves better generalization performance than traditional SOTA methods, and improves the convergence speed by 4 to 30 times compared to the training-from-scratch approach, providing a new solution paradigm for efficient policy transfer in complex dynamic environments. Full article

Smart metering of high-time-resolution energy data enables efficient power grid management. However, it also raises significant privacy concerns by revealing users’ consumption patterns. In this paper, a novel privacy-preserving idea is introduced by utilizing a rechargeable battery (RB) to reshape the smart meter readings to statistically align with random target readings, which are preset independently of the private user energy consumption data. For the long-term privacy-preserving and cost-efficient objectives, we formulate a sequential energy management unit (EMU) policy design as a constrained Markov decision process (CMDP), where the cost-efficient objective is optimized subject to the constraint on privacy preservation. We then develop a novel variational-constraint adversarial proximal policy optimization (VCA-PPO) algorithm to solve the CMDP without requiring prior knowledge of probabilistic models. Experimental results on a standard real-world dataset demonstrate the effectiveness of the proposed method and its superiority to the load-flatness benchmark method. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

The utilization of photovoltaic (PV) energy has witnessed significant recent growth, positioning it as the fastest-growing renewable energy technology. This rise in deployment underscores the importance of monitoring PV systems, which has become an increasingly critical area of research. However, the heightened focus on monitoring has also revealed a key challenge: various types of faults often remain undetected, potentially causing severe performance degradation. This study proposes a machine learning-based fault classification method that integrates Principal Component Analysis (PCA) for feature extraction with Kullback–Leibler (KL) divergence for distribution-based classification. The approach is applied to a publicly available dataset on a single benchmark of a 5 kW PV plant containing voltage, current, temperature, and irradiance measurements for five operating conditions: normal operation, short circuit, open circuit, partial shading, and degradation. A dedicated normalization strategy and optimal bin-width selection are employed to enhance the stability and accuracy of probability density function estimation. Experimental results demonstrate that the proposed PCA–KLD framework achieves superior accuracy compared to existing methods applied to the same dataset. These results confirm the effectiveness and robustness of the approach for fault classification in PV systems and highlight its potential for handling challenging fault scenarios. Full article

Gene regulatory networks differ substantially across individual cell lines, and population-level network inferences often fail to capture the underlying biological heterogeneity. To better capture this heterogeneity, cell line-specific gene network analysis is required. However, interpreting such high-dimensional cell line-specific networks remains a major challenge in the field of network biology. One interpretative approach is to identify differentially regulated gene networks (DGNs) between phenotypes because these networks can highlight phenotype-specific regulatory mechanisms. Although several methods have been proposed for DGN analysis, they are not suitable for cell line-specific gene network analysis, which is characterized by pronounced heterogeneity across individual networks. To address this problem, we proposed a novel statistical method for identifying DGNs in a cell line-specific scenario. The proposed framework integrates cell line-specific network estimation, a Kullback–Leibler divergence-based comparison of multivariate distributions, and a DKL-ratio statistic to quantify between-phenotype heterogeneity relative to within-phenotype homogeneity. Our method evaluates both between-phenotype heterogeneity and within-phenotype homogeneity, ensuring the robust detection of phenotype-specific network structures. Through Monte Carlo simulation studies, we systematically evaluated the performance of the proposed method and demonstrated that our strategy consistently outperformed existing methods in terms of accuracy, precision, true positive rate (TPR), true negative rate (TNR), and F-measure across diverse network structures and mean shift scenarios. Statistical significance was assessed using a permutation-based framework, confirming that the identified networks are unlikely to arise from random variation. We further applied our strategy to Quizartinib sensitivity-specific gene network analysis and identified immune-related subnetworks enriched in antigen processing and presentation pathways. These subnetworks included hub genes such as IFIT1, PSMB9, and HLA-B, which are known to be associated with immune evasion and drug resistance in acute myeloid leukemia. Our findings demonstrate that the proposed method enables statistically reliable and biologically interpretable identification of phenotype-specific gene regulatory mechanisms, providing insights into potential therapeutic targets. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article