Search Results (14,461)

Typhoon track prediction is an important research direction in weather forecasting. Although deep learning methods have achieved some progress in this field, challenges remain, including insufficient fusion of meteorological features, limited capability in modeling temporal and spatial evolution, and high computational cost of some models. To address these issues, this paper proposes a dual-path, multi-modal typhoon track prediction model that incorporates a gated axial Transformer to enhance the modeling of deep structural features in the meteorological environment. Numerical experimental results show that the proposed model achieves higher prediction accuracy than comparative methods in typhoon track prediction tasks across multiple time scales, demonstrating the effectiveness of the approach. Full article

In the digital economy era, how to effectively leverage data elements to promote green productivity has become a critical issue. The Big Data Comprehensive Pilot Zone (BDCPZ) serves as an institutional arrangement to promote data circulation, governance, and efficient allocation. Utilizing panel data from Chinese A-share listed firms spanning 2012–2023, this study treats the 2016 establishment of BDCPZ as a quasi-natural experiment and employs a difference-in-differences (DID) model to investigate how improvements in the data institutional environment induced by BDCPZ affect enterprise green total factor productivity (GTFP). Empirical results indicate that the establishment of BDCPZ significantly enhances GTFP, with results remaining robust across specification tests. Heterogeneity analyses demonstrate that these positive effects are more pronounced among non-heavily polluting enterprises, high-technology enterprises, and enterprises in less competitive markets. Mechanism analyses suggest that data-oriented institutional reforms primarily enhance GTFP through innovation incentives, human capital accumulation, and industrial structure upgrading. Furthermore, superior managerial efficiency and stronger managerial equity ownership amplify these positive effects. This study provides firm-level empirical evidence on the relationship between data-oriented institutional reforms and GTFP enhancement, contributing to the literature on data-driven institutional reforms and green productivity, and policy implications for optimizing data element utilization and promoting sustainable development. Full article

Accurate crop classification provides fundamental data for agricultural resource management and ecological research. Hyperspectral image (HSI) classification is the core technique for achieving precise crop mapping. However, existing models often suffer from excessive parameters, limited robustness under few-shot conditions, and a trade-off between efficiency and robustness. To address these issues, this paper proposes a Multi-Scale Wavelet Kolmogorov–Arnold Network (MSWKN). The model employs a Two-Branch Feature Extractor (TBFE) to capture both spectral correlations and spatial textures. a Channel Cross-Spatial (CCS) module to suppress background clutter and highlight discriminative regions. A group convolution-based Fixed Wavelet Multi-Scale Convolutional Layer (FW-MSCL) that leverages the time–frequency localization of wavelets and learnable linear combinations to enhance robustness against spectral distortion while reducing parameters. And a Fourier-based Transformer encoder to enable global frequency–space modeling. Experiments on the WHU-Hi-HanChuan and WHU-Hi-HongHu hyperspectral crop datasets show that MSWKN achieves high overall accuracy and performs favorably on few-shot categories. Under lower parameter counts and fast inference conditions, the model demonstrates a reasonable trade-off between accuracy and computational efficiency. Ablation studies and wavelet kernel comparisons further confirm the contribution of each module and the advantage of the wavelet. The proposed framework provides an efficient and robust solution for fine-grained hyperspectral crop classification. Full article

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change. Full article

In this paper, we study two aspects of quantum adiabatic evolution for a prototypical search problem: the optimality of the corresponding algorithm and its relation to the quantum circuit model. Firstly, we propose a general framework for proving the square-root speedup of the quantum adiabatic algorithm to be optimal over classical computation, which is readily applicable to the case of multiple targets. Through this framework, we also find that it is possible to further reduce the time complexity by increasing the physical energy of the system, encompassing results from previous works. Secondly, we find that, on the one hand, when the quantum adiabatic algorithm that achieves quadratic speedup is implemented on a quantum circuit, the time slice needed is always consistent with its time complexity, which also encompasses previous results; on the other hand, however, if a further algorithmic improvement is considered, the time slice always remains invariant. This phenomenon represents a significant observation with potential applications. We anticipate that the main results of this paper will interest the quantum adiabatic computation community and may help us to design efficient quantum algorithms for practical problems in the future. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

Images captured in low-light conditions often have poor visibility, low contrast, and color distortion due to uneven lighting. Most existing enhancement methods often suffer from unstable brightness recovery and color cast, which affect both visual quality and performance of advanced vision tasks. To address those issues, we propose DADNet, a dual-branch network with an attention mechanism and dark channel prior containing an Illumination Enhancement Module (IEM) and Color Transformation Module (CTM). The IEM extracts multi-scale features and improves lighting based on the dark channel prior, while the CTM employs the attention mechanism to handle color features and adjust saturation adaptively. Experimental results on three datasets show that DADNet performs well in both qualitative and quantitative evaluations. It effectively preserves image structure and texture details while achieving a good balance between overall brightness and color quality. Full article

Construction sites implement various safety management activities, including toolbox meetings, risk assessments, and safety knowledge assessments, to reduce accidents. Multiple-choice question (MCQ)-based assessments are widely used to evaluate worker safety competencies. However, the effectiveness of MCQ assessments depends critically on distractor quality; incorrect options must be plausible enough to challenge uninformed respondents while remaining clearly distinguishable from knowledgeable ones. Manual distractor creation requires substantial expertise and is prone to inconsistency, whereas large language models (LLMs) often generate options that lack domain relevance. This paper proposes context-aware multipath adaptive safety scoring (CoMPASS), an algorithm that integrates construction safety domain knowledge with LLM capabilities for MCQ distractor generation. CoMPASS operates through two pathways: CoMPASS-H leverages a hierarchical hazard factor ontology for hazard identification questions, whereas CoMPASS-R uses hybrid retrieval-augmented generation (RAG) for risk control questions. An evaluation using 50 real construction accident cases with a robotic assessment test (RAT) using frontier LLMs as virtual examinees demonstrated that CoMPASS-R achieved a 90% quality pass rate, whereas all baseline methods failed to meet the composite quality criteria. The proposed framework provides a scalable approach to generating assessment content that supports effective safety management at construction sites. Full article

Background/Objectives: Type 2 diabetes (T2D) develops gradually over many years through a prolonged preclinical phase, yet traditional static risk scores often fail to capture these dynamic metabolic trajectories. We propose a longitudinal deep learning framework to predict the 10-year risk of Type 2 diabetes onset defined by comprehensive ADA criteria by modeling the physiological acceleration of routine clinical biomarkers. Methods: Utilizing an 18-year longitudinal dataset from the community-based Korean Genome and Epidemiology Study (KoGES) cohort, we selected $N=4354$ participants with complete follow-up records, ensuring high data integrity without requiring synthetic data augmentation. We constructed a 3-dimensional tensor of 21 non-invasive clinical variables spanning a 6-year observation window. To resolve the inherent precision-recall trade-offs of individual models, we developed a stacking ensemble that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures via a logistic regression meta-learner. To evaluate the added value of longitudinal modeling, we compared this dynamic framework against a static XGBoost baseline that only saw the most recent data. Results: Evaluated on an independent test set ($n=874$), the ensemble significantly outperformed baseline models, achieving an overall accuracy of 0.90 (95% CI: 0.88–0.92) and an AUROC of 0.94 (95% CI: 0.93–0.95). By harmonizing LSTM’s sensitivity and GRU’s precision, the model yielded an exceptional Positive Predictive Value (PPV) of 0.97, a sensitivity of 0.80, and a specificity of 0.98. Conclusions: This framework provides a highly accurate, resource-efficient triage instrument for T2D screening, thereby reducing unnecessary clinical alerts and improving screening efficiency. Full article

Multi-source radar data fusion has become increasingly vital for advancing weather monitoring and forecasting. However, effectively integrating Doppler radar with an X-band phased-array radar remains challenging. Doppler radar offers only low and inconsistent spatial resolution, whereas an X-band phased-array radar provides high resolution but is limited by short detection range, severe signal attenuation, and high deployment costs, constraining its use to localized monitoring. To address the aforementioned challenges, this paper proposes the Multi-source Radar Reflectivity Complementary Enhancement method (MSR-CE). By constructing a paired training dataset, real X-band phased-array radar reflectivity data serve as the starting samples for the forward diffusion process, while paired S-band Doppler radar reflectivity data act as conditional guidance. Leveraging a conditional diffusion model, the method generates high-resolution pseudo X-band phased-array reflectivity fields. Additionally, a Radar-Physics-Aware Loss (RPA Loss) is introduced to enhance spatial detail fidelity and physical consistency. Experiments on multi-source radar observations from Northeast China in 2025 demonstrate that MSR-CE achieves an SSIM of 0.892 and a PSNR of 41.6 dB, outperforming traditional interpolation methods and state-of-the-art generative approaches in radar reflectivity enhancement. Full article

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

This article details a matrix-based mathematical method to calculate power flows and transmission losses in an electric grid specifically attributable to low-carbon and renewable energy sources (LCRES) (wind, solar, nuclear). The goal is to improve the transparency and reliability of Guarantees of Origin (GO) certificates. Current GO schemes rely on contractual accounting and neglect physical power losses, undermining consumers’ confidence that they receive “clean” energy. The method uses steady-state power flow analysis to derive a power-loss distribution coefficient matrix. This matrix accurately allocates grid losses back to the LCRES generating nodes, complying strictly with electrical engineering principles. It accommodates both time-varying renewable output and stable nuclear generation. The results offer highly accurate loss-attribution data, supporting more verifiable GOs, ensuring fair compensation for losses, and enhancing energy balance accuracy in hybrid power systems. Full article

Pinching-antenna systems (PASSs) offer a low-complexity and reconfigurable solution for near-field downlink communications by deploying multiple radiating elements along a single waveguide. Existing studies mainly assume simplified propagation conditions or focus on spectral efficiency, while the impact of environment-dependent interference patterns arising from user-specific blockage conditions on energy-efficient design remains unclear. An energy-efficient downlink design for single-waveguide PASS based on environment-division multiple access (EDMA) is investigated. Under a given propagation environment, EDMA exploits user-dependent blockage and visibility differences through proper pinching-antenna placement, thereby inducing different multi-user interference patterns without increasing radio-frequency hardware complexity. We examine how such blockage-dependent interference influences the relationship between spectral efficiency and energy efficiency, and develop an energy-aware EDMA framework that jointly considers pinching-antenna locations and transmit power allocation under quality-of-service constraints. The resulting coupled design problem is solved through an alternating optimization procedure. EDMA is compared with conventional time-division multiple access (TDMA) using a unified hardware and power-consumption model. Numerical results reveal clear energy-efficiency threshold behaviors with respect to blockage intensity, user population, and service requirements. The results further show that EDMA can significantly outperform TDMA in specific operating regimes. Full article

Background/Objectives: Abnormal muscle tone and impaired motor control commonly limit gait recovery after stroke. Robot-assisted gait training has been introduced to augment conventional rehabilitation; however, its effects on stage-based motor recovery, functional ambulation, and muscle tone during the subacute phase remain unclear. Methods: This prospective, single-center, randomized controlled trial enrolled 30 patients with subacute stroke who received robot-assisted gait training plus conventional rehabilitation (R-BoT Plus group, n = 15) or conventional rehabilitation alone (control group, n = 15) over 4 weeks. The primary outcome was the change in Brunnstrom recovery stage of the lower extremities (BRS-LE). Secondary outcomes included Functional Ambulation Category (FAC), Fugl–Meyer Assessment for the Lower Extremity (FMA-LE), clinical spasticity measures (Modified Ashworth Scale and Modified Tardieu Scale), and muscle mechanical properties (MyotonPRO). Exploratory analyses were conducted to examine the associations between changes in stage-based motor recovery (ΔBRS-LE), functional ambulation (ΔFAC), and MyotonPRO parameters. Within-group changes were assessed using the Wilcoxon signed-rank test. Between-group effects were primarily evaluated using baseline-adjusted ANCOVA with HC3 robust standard errors, with Wilcoxon rank-sum tests on change scores as sensitivity analyses. Associations between changes in clinical outcomes and MyotonPRO parameters were evaluated using Spearman’s rank correlation coefficient (ρ). Results: BRS-LE (p = 0.014) and functional ambulation (p = 0.041) were significantly improved in the R-BoT Plus group. Changes in FMA-LE and clinical spasticity measures did not differ significantly between groups. Quantitative myotonometry revealed selective muscle- and parameter-specific changes. No robust correlations were observed between MyotonPRO parameters and changes in BRS-LE. Conclusions: The addition of robot-assisted gait training to conventional rehabilitation was associated with greater improvements in stage-based lower-limb motor recovery and functional ambulation in patients with subacute stroke. In contrast, cumulative impairment scores and conventional clinical spasticity measures demonstrated limited changes between groups. Quantitative muscle mechanical assessment revealed selective muscle-specific adaptations, supporting its role as a complementary tool for mechanistic characterization rather than as a surrogate marker of motor recovery. Future studies incorporating dose-matched designs and longer follow-up periods are warranted to clarify the independent and long-term effects of robot-assisted gait training. Full article

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article