Search Results (8,532)

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity. Full article

Traffic classification remains a critical challenge in the Internet of Things (IoT), particularly for enhancing security and ensuring Quality of Service (QoS). Although deep learning methods have shown strong performance in traffic classification, learning diverse and complementary representations across heterogeneous network traffic patterns remains difficult. To address this issue, this study proposes a novel Mixture of Experts (MoE) architecture for multiclass traffic classification in IoT environments. The proposed model integrates five specialized expert networks, each targeting a distinct feature category in network traffic. Specifically, it employs a Dense Neural Network for general features, a Convolutional Neural Network (CNN) for spatial patterns, a Gated Recurrent Unit (GRU)-based model for statistical variations, a Convolutional Autoencoder (CAE) for frequency-domain representations, and a Long Short-Term Memory (LSTM) for temporal dependencies. A dynamic gating mechanism, coupled with an Attention-based Learnable Fusion (ALF) module, adaptively aggregates the experts’ outputs to produce the final classification decision. The proposed ALF-MoE model was evaluated on three public benchmark datasets, such as ISCX VPN-nonVPN, Unicauca, and UNSW-IoTraffic, achieving accuracies of 98.43%, 98.96%, and 97.93%, respectively. These results confirm its effectiveness and reliability across diverse scenarios. It also outperforms baseline methods in terms of its accuracy and the F1-score. Full article

This paper investigates bipartite synchronization in signed Lur’e networks influenced by semi-Markovian jump dynamics. A control strategy is proposed that adapts to mode-dependent switching by combining quantized feedback with selective pinning. The approach accommodates both leaderless and leader–following synchronization scenarios. For each switching mode, Lyapunov–Krasovskii-based analysis is employed to establish sufficient conditions using linear matrix inequalities (LMIs). The robustness and convergence of the method are confirmed through simulation studies, even in the presence of stochastic switching and limited communication precision. Full article

Elderly patients often experience multimorbidity and long-term polypharmacy, making medication safety a critical challenge in disease management. In China, the concurrent use of Western medicines and proprietary Chinese medicines (PCMs) further complicates this issue, as potential drug interactions are often implicit, increasing risks for physiologically vulnerable older adults. Although large language model-based medical question-answering systems have been widely adopted, they remain prone to unsafe outputs in medication-related contexts. Existing retrieval-augmented generation (RAG) frameworks typically rely on static retrieval strategies, limiting their ability to appropriately allocate retrieval and verification efforts across different question types. This paper proposes FusionGraphRAG, an adaptive RAG framework for geriatric disease management. The framework employs query classification-based routing to distinguish questions by complexity and medication relevance; integrates dual-granularity knowledge alignment to connect fine-grained medical entities with higher-level contextual knowledge across diseases, medications, and lifestyle guidance; and incorporates explicit contradiction detection for high-risk medication scenarios. Experiments on the GeriatricHealthQA dataset (derived from the Huatuo corpus) indicate that FusionGraphRAG achieves a Safety Recall of 71.7%. Comparative analysis demonstrates that the framework improves retrieval accuracy and risk interception capabilities compared to existing graph-enhanced baselines, particularly in identifying implicit pharmacological conflicts. The results indicate that the framework supports more reliable geriatric medical question answering while providing enhanced safety verification for medication-related reasoning. Full article

Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatial–temporal dependencies, while paying limited attention to the propagation of irrelevant or unstable information through dynamic graph structures. In this work, we propose a Dynamic Graph Information Bottleneck (DGIB) framework that enhances prediction stability by introducing task-aware representation compression into dynamic graph learning. Instead of relying solely on architectural complexity, DGIB explicitly regulates the information flow within spatio-temporal embeddings through a variational bottleneck objective. The model adaptively constructs time-evolving adjacency matrices, extracts spatial features via graph convolutions, captures temporal dependencies using recurrent modeling, and constrains the latent representation to retain only predictive content relevant to future traffic states. By jointly optimizing topology adaptation and information-theoretic regularization in an end-to-end manner, the proposed framework mitigates the amplification of noisy or redundant signals in dynamic graphs. Experiments on multiple benchmark traffic datasets demonstrate that DGIB achieves competitive forecasting accuracy while maintaining strong robustness under noisy and incomplete data scenarios. Full article

Due to the influence of factors such as satellite jitters, orbital errors, star sensor errors, and satellite clock errors, significant geometric systematic errors often exist among multi-view satellite images. This is common for multi-view, cross-orbit satellite data, where complex nonlinear systematic errors are present, making it difficult to correct them using traditional error compensation models. To achieve high-precision block adjustment, this paper proposes a direct adjustment and optimization method for Rational Function Model (RFM) parameters based on prior soft constraints. In this method, the original RFM parameters are used as prior information, which is formulated as prior information soft constraint equations in the adjustment model, aiming at effectively addressing the ill-posed problems. By directly optimizing part or all of the RFM parameters, this method can obtain stable adjustment results in scenarios of complex systematic errors. Experiments among WorldView-3, GaoFen Multi-mode, ZY-3 (Ziyuan-3), and GaoFen-7 satellite data show that, when using multi-view, cross-orbit satellite data and with sufficient and evenly distributed tie points, the proposed full-parameter RFM optimization method and the adaptive RFM optimization method can achieve the highest adjustment accuracy. On the other hand, when using in-track satellite data, the affine systematic error compensation model achieves the highest accuracy, while the adaptive RFM optimization method can achieve comparable accuracy. Therefore, the research results can be applied to intelligent processing scenarios for multi-view, cross-orbit satellite data, such as multi-temporal change detection and multi-view, cross-orbit satellite 3D modeling. Full article

Multi-object tracking (MOT) in vineyard environments remains challenging due to frequent and long-term occlusions caused by dense foliage, overlapping grape clusters, and complex plant structures. These characteristics often result in identity switches and fragmented trajectories when using conventional tracking methods. This paper proposes OATSAM-Track, an occlusion-aware multi-object tracking framework designed for vineyard fruit monitoring. The framework integrates lightweight MobileSAM-assisted instance segmentation to estimate target visibility and occlusion severity. Occlusion-state reasoning is further incorporated into temporal association, appearance memory updating, and identity recovery. An adaptive temporal memory mechanism selectively updates appearance features according to predicted occlusion states, reducing identity drift under partial and severe occlusions. To facilitate occlusion-aware evaluation, an extended vineyard multi-object tracking dataset (GrapeOcclusionMOTS) with SAM-refined instance masks and fine-grained occlusion annotations is constructed. The experimental results demonstrate that OATSAM-Track improves identity consistency and tracking robustness compared to representative baseline trackers, particularly under medium and severe occlusion scenarios. These results indicate that explicit occlusion modeling is beneficial for reliable fruit monitoring in precision agriculture. Full article

High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture. Full article

Open banking reshapes the financial sector by enabling regulated third-party providers to access bank data through APIs, fostering innovation but amplifying operational and financial-crime risks due to increased ecosystem interdependence. To address these challenges, this study proposes an integrated risk-management framework combining System Dynamics, Agent-Based Modelling, and Monte Carlo simulation. This hybrid approach captures feedback effects, heterogeneous agent behaviour, and loss uncertainty within a simulated PSD2-style environment. Simulation experiments, particularly those modelling credential-stuffing waves, demonstrate that stricter onboarding thresholds, tighter API rate limits, and enhanced anomaly detection reduce operational tail losses by approximately 20–30% relative to baseline scenarios. Beyond these specific findings, the proposed framework exhibits significant universality; its modular design facilitates adaptation to broader contexts, including cross-border regulatory variations or emerging BigTech interactions. Ultimately, this multi-method approach translates complex open-banking dynamics into actionable risk metrics, providing a robust basis for targeted resource allocation and supervisory stress testing in evolving financial ecosystems. Full article

The fast-paced utilization of innovative Internet of Things (IoT) applications emphasizes the critical role that routing protocols play in designing an efficient communication system between network nodes. In this context, the lack of adaptive routing mechanisms in the standard Routing Protocol for Low-power and Lossy Networks (RPL), such as load balancing and congestion mechanisms, especially under heavy load scenarios, causes significant degradation of network performance. In this regard, integrating innovative and effective learning abilities, such as Reinforcement Learning, into an efficient routing policy has demonstrated promising solutions for future networks. Hence, this paper introduces Aris-RPL, an adaptive routing policy for the RPL protocol. Aris-RPL utilizes a multi-objective Q-learning algorithm to learn optimal paths. Each node translates neighboring node information into a Q-value representing a composite multi-objective metric, including Buffer Utilization, Energy Level, Received Signal Strength Indicator (RSSI), Overflow Ratio, and Child Count. Furthermore, Aris-RPL operates effectively during the exploitation and exploration phases and continuously monitors the network overflow ratio during exploitation to respond to sudden changes and maintain performance. The extensive Contiki OS 3.0/COOJA simulator experiments have verified Aris-RPL efficiency. It enhanced Control Overhead, Packet Delivery Ratio (PDR), End-to-End Delay (E2E Delay), and Energy Consumption results compared to other counterparts for all scenarios on average by 39%, 25%, 7%, and 38%, respectively. Full article

The study analyzes the long-term development prospects of the Latvian dairy sector until 2032, using an integrated modeling approach that combines the AGMEMOD partial equilibrium model with the TOPSIS multi-criteria evaluation method. The study addresses the main challenge facing the sector—how to maintain productivity in the context of structural consolidation and increasing environmental requirements. The AGMEMOD model was recalibrated using updated data for Latvia for 2015–2023. Two scenarios were developed: A1 “Targeted and intensive farm modernization” and A2 “Limited farm modernization”. Scenario A1 is characterized by gradual technological adoption, leading to higher productivity while keeping total milk production almost unchanged relative to the Baseline scenario, whereas scenario A2 reflects slower modernization and reduced productivity growth. The TOPSIS evaluation identified scenario A1 as the most attractive alternative, as it combines productivity gains and greater adaptability to policy and environmental requirements. The results confirm that technological modernization and flexible policy mechanisms are essential to maintain the competitiveness and productivity performance of Latvia’s dairy sector. The integrated AGMEMOD–TOPSIS approach provides a methodological tool for evidence-based policy analysis and strategic planning in agricultural market management. Full article

To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model’s adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash–Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model’s robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from −0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river–lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins. Full article

The importance of statistical distributions in representing real-world scenarios and aiding in decision-making is widely acknowledged. However, traditional models often face limitations in achieving optimal fits for certain datasets. Motivated by this challenge, this paper introduces a new probability distribution termed the weighted sine generalized Kumaraswamy (WSG-Kumaraswamy) distribution. This model is constructed by integrating the Kumaraswamy baseline distribution with the weighted sine-G family, which incorporates a trigonometric transformation to enhance flexibility without adding extra parameters. Various statistical properties of the WSG-Kumaraswamy distribution, including the quantile function, moments, moment-generating function, and probability-weighted moments, are derived. Maximum likelihood estimation is employed to obtain parameter estimates, and a comprehensive simulation study is performed to assess the finite-sample performance of the estimators, confirming their consistency and reliability. To illustrate the practical advantages of the proposed model, two real-world datasets from epidemiology and reliability engineering are analyzed. Comparative evaluations using goodness-of-fit criteria demonstrate that the WSG-Kumaraswamy distribution provides superior fits compared to established competitors. The results highlight the enhanced adaptability of the model for unit-interval data, positioning it as a valuable tool for statistical modeling in diverse applied fields. Full article

Groundwater in Mediterranean regions is facing increasing threats from climate change and intensive agriculture, necessitating robust vulnerability assessment tools. This study evaluates nitrate pollution vulnerability of the Almyros aquifer (Thessaly, Greece) using the DRASTIC index under the high-emission scenario RCP8.5. Bias-corrected Med-CORDEX climate projections were integrated into a coupled hydrological–hydrogeological modeling framework to simulate recharge, groundwater levels, and nitrate transport. DRASTIC results for the baseline (1991–2018) showed strong agreement with observed nitrate concentrations, while future projections (2031–2060, 2071–2100) revealed shifting vulnerability patterns, particularly in low-lying agricultural areas. The findings highlight climate-driven changes in groundwater vulnerability and support targeted adaptive management strategies. Full article

Forest ecosystems provide essential ecological functions in the context of accelerating climate change. However, evaluating their conservation values and conditions remains challenging due to conceptual and methodological ambiguities. In particular, ecosystem integrity and ecosystem authenticity are often conflated in vegetation-based assessments, despite representing distinct dimensions of ecosystem condition. This study advances vegetation-based assessments by explicitly decoupling ecosystem integrity from ecosystem authenticity, while integrating spatial completeness, vegetation patterns and quality, and successional–disturbance attributes into a unified operational framework for reserve-level diagnosis and comparison. The resulting indices enable managers to distinguish boundary-driven limitations of landscape integrity from internal vegetation conditions that persist in near-natural states, thus enhancing interpretability for conservation planning in the context of climate change. Using standardized forest resource survey data and spatial analysis, we constructed two composite indices: Forest Ecosystem Integrity (FEI) and Forest Ecosystem Authenticity (FEA). These indices were applied to two adjacent cold-temperate forest nature reserves, Hanma and Huzhong, in the Greater Khingan Mountains of northeastern China, as well as to a merged spatial scenario. The results demonstrate consistently high ecosystem authenticity (>90%) across all study areas, indicating strong naturalness and successional maturity. In contrast, ecosystem integrity remains moderate (63–69%), primarily constrained by the low spatial completeness of conservation units. The spatial integration of the two reserves significantly improved ecosystem integrity without compromising authenticity, highlighting the role of boundary configuration in conservation effectiveness. By operationalizing integrity and authenticity as complementary yet distinct dimensions, this study provides a reproducible framework for evaluating forest ecosystem conditions and offers practical insights for the design of protected area networks and adaptive management in cold-temperate forest regions. Full article