Search Results (5,393)

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

Accurate electric vehicle (EV) charging load forecasting is essential for grid planning and resource allocation, yet existing approaches struggle with the inherent sparsity of charging data—a phenomenon characterized by excessive zeros representing periods of no charging activity. This paper addresses this challenge through a novel framework combining a Zero-Inflated Neural Network (ZINN) architecture with an Evolutionary Neural Architecture Search (ENAS) algorithm. ZINN explicitly decomposes the forecasting problem into binary classification (predicting charging occurrence) and regression (estimating energy magnitude conditioned on occurrence), enabling the model to learn distinct patterns for the absence and presence of charging events. Rather than relying on manually designed architectures, ENAS automatically discovers optimal encoder and decoder configurations from a comprehensive search space encompassing modern architectures (LSTM, GRU, Transformer, and iTransformer), layer configurations, activation functions, and hyperparameters. The evolutionary algorithm balances prediction accuracy with computational efficiency through multi-objective optimization. Extensive experiments on real-world EV charging data from 30 stations in Wuhan demonstrate that the ZINN+ENAS framework achieves the lowest prediction error compared to conventional baselines, with the discovered optimal configuration substantially outperforming hand-crafted designs. Comprehensive ablation studies reveal that the asymmetric dual-head architecture and adaptive regularization strategies are critical for handling data sparsity. These findings highlight the importance of explicit zero-inflation modeling and automated architecture discovery for specialized forecasting tasks, providing practitioners with an open-source framework for practical EV charging load prediction. Full article

Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle–UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient α within 0.2–0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

Conductive hydrogels are attractive for flexible electronics, but their practical use is often limited by resistance drift during repeated deformation and performance degradation at low temperatures. Here, core–shell polyaniline-coated polyacrylonitrile (PANi@PAN) electrospun nanofibers were incorporated into a polyacrylamide/hydroxypropyl cellulose (PAM/HPC) hydrogel matrix to construct a hybrid conductive network. The PANi shell serves as an electronic pathway alongside ionic conduction in the hydrated polymer network, leading to markedly improved electromechanical stability. The resistance drift is about 11% after 2000 stretching–relaxation cycles at 0–100% strain, about 12 times lower than that of the nanofiber-free hydrogel. Stable electrical responses are maintained under large deformation, with a resistance drift as low as 3.3% over a strain range of 0–400%. The hydrogels show a conductivity of 0.32 S m⁻¹ while retaining high stretchability (>600%). An ethylene glycol/water binary solvent is used to suppress ice formation and improve moisture retention, allowing stable electromechanical performance at −15 °C over 500 cycles. The hydrogel also adheres reliably to human skin (about 10.25 kPa) and functions as a conformal strain sensor without extra fixation. Full article

Railways constitute a core component of China’s national comprehensive transportation network, and their spatial organization and temporal accessibility jointly shape transport integration and system efficiency. Identifying their evolution from the dual perspectives of spatial expansion and time compression is therefore of both theoretical and practical significance. Drawing on fractal theory, this study examines the structural characteristics, evolutionary trends, and driving factors of railway networks in China’s five major urban systems from 2014 to 2024 from a “space–time” dual perspective. The results show that railway networks exhibit a staged pattern of “spatial filling preceding temporal correlation”, with a lag of approximately 1–8 years—about 1 year in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), 5 years in the Middle Yangtze River (MYR) region and Beijing–Tianjin–Hebei (BTH), and up to 8 years in the Chengdu–Chongqing (CC) region. In addition, clear regional differences are observed: the Yangtze River Delta (YRD) is polycentric, with the greatest potential, projected to continue rapid spatial growth until 2027 and to remain in a fast-growth phase of temporal correlation; GBA is highly coordinated; BTH is developed but characterized by dual-core agglomeration; CC grows rapidly with lagging functionality; and MYR is corridor-dependent with limited potential. These findings indicate that network functionality does not emerge synchronously with infrastructure expansion, but depends on subsequent improvements in operational organization and service capacity. Compared with single-scale-based indicators, the “spatial distribution–temporal correlation” framework more effectively captures network performance and provides quantitative support for transport optimization and coordinated regional development. Full article

The sodium-taurocholate cotransporting polypeptide (NTCP) plays a critical dual role in liver function: maintaining bile acid (BA) enterohepatic circulation and acting as a receptor for the entry of hepatitis B and D viruses into hepatocytes. This review outlines the impact of various post-translational modifications (PTMs) of NTCP—including phosphorylation, oligomerization, ubiquitination, and glycosylation—on its dynamic regulatory network. These modifications coordinate the modulation of NTCP’s membrane localization, stability, conformational state, and protein interactions, precisely controlling its functions in BA uptake and viral invasion. Targeting this PTM network presents a promising strategy for next-generation therapies that selectively inhibit viral infection while preserving BA transport, overcoming the limitations of conventional inhibitors that indiscriminately disrupt virus–NTCP interactions. By synthesizing recent insights into NTCP PTM research, this article highlights its role as a central regulator of its bifunctional properties and reveals potential avenues for precision therapies in viral hepatitis, cholestasis, and related liver diseases. However, most existing evidence is derived from in vitro or cell-based models, whereas in vivo studies and clinical validation remain limited; thus, the translational feasibility of strategies targeting post-translational modifications of NTCP still requires further investigation. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

Brain tumor detection from magnetic resonance imaging (MRI) is fundamental to computer-aided diagnosis, yet automated models must remain robust to heterogeneous imaging conditions. Despite strong recent progress, many deep learning and transformer-based approaches primarily optimize performance accuracy without explicitly improving feature selectivity and spatial localization, and they typically produce deterministic output without uncertainty estimates, which limits reliability. To overcome these limitations, we introduce UA-EffNet-DA, an uncertainty-aware EfficientNet framework that addresses these limitations through three complementary components. First, EfficientNet-B4 serves as an efficient backbone for discriminative feature extraction. Second, lightweight dual attention modules, comprising channel and spatial attention in parallel, are applied to the model to emphasize what and where discriminative features to focus within MRI slices. Third, Monte Carlo dropout is employed during inference to quantify predictive uncertainty and enable confidence-aware decision. Experiments on two public benchmarks demonstrate strong performance, yielding accuracies of 98.73% on the Figshare dataset and 99.23% on the Kaggle dataset. In addition, explainable AI analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) further indicates that the proposed model concentrates on diagnostically relevant tumor regions rather than background structures, supporting transparent decision-making. Ablation studies confirm the complementary contribution of dual attention refinement and uncertainty-aware inference. Overall, the proposed UA-EffNet-DA framework offers an efficient and interpretable approach for brain tumor detection that supports more reliable clinical decision support through uncertainty-aware predictions. Full article

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this paper proposes an optimal scheduling strategy for bundled operation based on capacity interval matching of PSH with WP and PV under extreme high-temperature weather. First, typical scenarios are generated based on a Time-series Generative Adversarial Network (TimeGAN), and an interval matching transaction model is established based on the forecast intervals of WP and PV capacity and the corrected intervals of PSH capacity. Second, considering PSH as an independent market entity, a bi-level optimization model is constructed, in which the upper-level objective is to maximize the revenue of PSH, while the lower-level objective is to minimize the total cost of the joint clearing of the energy and ancillary service markets. Finally, simulation case studies verify that under extreme high-temperature weather, the proposed optimal scheduling method increases the bundled operation capacity by 17.9% and improves the revenue of PSH in the reserve ancillary service market by 14.8%, thereby effectively enhancing the economic performance of PSH while ensuring the safe and stable operation of the system. Full article

To improve the feature representation and cross-condition generalization of bearing fault diagnosis, this paper proposes an adaptive multi-sensor fusion network with a skip fusion module and a parameter-efficient dual-convolution diagnosis block. The vibration and current signals are first augmented by overlapping segmentation and transformed into the frequency domain using FFT. Multi-scale depthwise convolutions are then employed in parallel branches to capture fault patterns at different receptive fields, and an attention-based skip fusion mechanism selectively aggregates cross-sensor features for complementary enhancement. After fusion, self-calibrated convolution and dilated convolution are alternately applied to strengthen discriminative representation without increasing model complexity. Experiments on multiple bearing datasets under both constant and variable operating conditions demonstrate that the proposed method achieves consistently higher accuracy and robustness than representative CNN-based baselines, verifying its effectiveness for practical bearing fault diagnosis. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article

Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) represent fundamental cellular adaptive mechanisms that maintain protein homeostasis and metabolic balance. Many RNA viruses, particularly flaviviruses such as dengue virus (DENV), Zika virus (ZIKV), West Nile virus (WNV), yellow fever virus (YFV), and Japanese encephalitis virus (JEV), extensively remodel the ER to establish replication compartments and assemble progeny virions. This massive reorganization disrupts ER homeostasis, leading to UPR activation. Emerging evidence reveals that flaviviruses not only trigger but also manipulate the three UPR branches—PERK, IRE1, and ATF6—to optimize viral translation, replication, and egress. In parallel, flavivirus infection profoundly alters host lipid metabolism and promotes dynamic changes in lipid droplets (LDs), key organelles that mediate lipid storage and serve as scaffolds for viral replication and assembly. The UPR intimately connects to LD biogenesis through transcriptional and translational programs mediated by XBP1, ATF4, and ATF6, thereby coupling ER stress responses to lipid remodeling and energy homeostasis. This intricate crosstalk between UPR and LDs creates a metabolic and structural niche favorable for viral replication but detrimental to host cell integrity. This review provides a comprehensive analysis of the molecular mechanisms by which flaviviruses exploit ER stress and the UPR to reprogram lipid metabolism and LD dynamics. We highlight the dual role of UPR signaling in promoting adaptive lipid synthesis and initiating cell death under prolonged stress, discuss recent insights into ER–LD interactions during flavivirus infection, and explore therapeutic opportunities targeting UPR–lipid metabolic pathways as broad-spectrum antiviral strategies. Understanding this interconnected network will advance our knowledge of viral pathogenesis and identify new avenues for host-directed antiviral intervention. Full article

While convolutional neural networks (CNNs) excel at image classification, their generalization across domains and robustness to nonlinear degradation remain challenges in art media classification (AMC). To address these challenges, this article presents a dual-stage analytical framework: first, an evaluation of seven discrete CNN architectures—ranging from VGG16 to ConvNeXt—subjected to domain shift using the New Spain (Mexico) Art Media Dataset; and second, a formal robustness analysis using an artistic corruption benchmark (Art-C). This benchmark simulates nonlinear degradations, including cracking, oxidized varnish, and pictorial abstraction. Our results demonstrate that while deep convolutional representations maintain acceptable transferability (accuracy $>70\%$), significant variability exists in architectural stability (mean 0.0607) under progressive stochastic degradation. Notably, Xception exhibited the highest robustness (Art-mCE = 0.8039), whereas VGG16 showed the greatest relative performance decay. Severity analysis further indicates that structural perturbations induce higher error rates than chromatic shifts, suggesting that CNNs are more sensitive to topological features (depth and residual connections) than color-space distributions. We provide quantitative evidence characterizing the relationship between architectural topology and empirical stability in non-natural image domains. Full article

Multi-link terrestrial backhauls create a shared transport environment in which heterogeneous multipath protocols compete for the same forwarding resources while reacting to congestion with different control logics. In this paper, we investigate this problem in a 5G Integrated Access and Backhaul (IAB) scenario where an IAB node aggregates traffic from multiple User Equipments (UEs) and forwards it toward the core network over two terrestrial backhaul paths. We focus on the coexistence of Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), evaluating how cross-protocol Congestion Control (CC) pairings affect performance. Specifically, all feasible BBR, CUBIC, and Reno cross-pairings are assessed under symmetric and asymmetric dual-backhaul conditions, considering Enhanced Mobile Broadband (eMBB) and dense low-rate traffic regimes representative of mMTC-like operation. The analysis considers throughput, Jain’s fairness index, jitter , and packet loss to identify the trade-offs of each CC pairing. Results show that CC selection is a first-order design factor in MPTCP/MPQUIC coexistence over shared backhauls. No single pairing is uniformly optimal across all metrics: some configurations provide more balanced throughput sharing, others improve fairness, while the most favorable solutions for jitter do not necessarily maximize transport efficiency. These findings identify CC pairing as a tuning dimension for multi-link backhaul systems based on heterogeneous multipath transports. Full article