Search Results (22,973)

Generalized aquifers are widely used in various fields, such as groundwater use, mine water prevention and control, and geothermal energy. This paper presents a transformer-model-based automatic aquifer generalization method using borehole logs in scenarios with scarce experimental parameters. Relying only on basic borehole data, the method used an agent-assisted approach to extract and clean key lithological and coordinate information, which was then fused using a dual embedding mechanism. The model leveraged multi-head self-attention to calculate attention weights between the target stratum and its adjacent strata, capturing the potential contextual correlations in aquifer potential across strata. The resulting deep feature vectors from the transformer’s encoder were fed into a classification head to predict aquifer potential labels. Evaluation results demonstrated a model accuracy of 0.86, significantly outperforming the random classification baseline in precision, recall, the F1-score, and the kappa coefficient. Full article

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

Green apples exhibit a coloration that closely matches their surrounding environment, leading to low recognition accuracy for existing artificial intelligence models. This paper presents a green apple recognition algorithm that integrates an improved U-shaped network (U-Net) and you only look once network (YOLO) v8 to address this challenge. First, the U-Net is enhanced via Dilated Convolution, Attention Gates, and Residual Connections to blur the background, thereby emphasizing the green apple target. Second, convolutional transformations and an attention mechanism are incorporated into YOLO v8, enabling it to focus more effectively on green apple targets within similarly colored backgrounds. Finally, the improved YOLO v8 is employed to recognize green apple targets segmented by the U-Net, with its performance compared against existing models. Research results show that the proposed algorithm achieves a precision of 92.5% and a Recall of 96.8% in green apple recognition, representing a significant improvement over classical models. To mitigate omission issues and further enhance overall performance, an improved YOLO v8 module is connected in parallel with the primary model. Based on its underlying principles, this approach is also applicable to other green fruits with colors and textures highly similar to their backgrounds, demonstrating strong robustness and generalization capabilities. Full article

Dairy products contain complex types and contents of proteins, lipids, and lactose. The Maillard reaction (MR) occurs between proteins and reducing sugars during the processing and storage of dairy products. Maillard reaction products (MRPs) have garnered attention for their potential antioxidant activity. MRPs include melanoidins, reductones, and volatile heterocyclic compounds, which affect flavor and color. Relevant literature was identified through a structured search of PubMed and Web of Science; studies were included if they investigated MRPs in dairy products and reported antioxidant-related outcomes. This review offers a comprehensive overview of the MR in dairy products, systematically investigating the influence of protein, reducing sugars, and their ratios, as well as reaction conditions (process technology, temperature, time, pH, and water activity) on the formation and antioxidant activity of MRPs. The review also covers current applications and the future potential of MRPs as natural antioxidants in dairy products. Although MRPs effectively delay lipid oxidation and enhance stability in dairy products, research on their molecular structure and antioxidant mechanisms remains insufficient. Future research should focus on understanding the multifactorial synergistic effects within the complex dairy matrix, elucidating the molecular structure and extraction of antioxidant substances, and developing regulatory techniques to balance the antioxidant properties of MRPs with the safety concerns of potential harmful byproducts. Full article

Background/Objectives: Intracranial arachnoid cysts (Acs) are congenital, usually benign lesions that are frequently regarded as clinically silent in adulthood. Nonetheless, growing evidence indicates that Acs may be associated with subtle but measurable cognitive dysfunction. This systematic review synthesizes neuropsychological and functional neuroimaging findings in adults with intracranial Acs, with a focus on cognitive profiles, functional interactions with the adjacent cortex, and postoperative reversibility. Methods: In accordance with PRISMA 2020 guidelines, MEDLINE/PubMed and Scopus were searched for English-language studies published up to 2023 that reported neuropsychological assessments and/or functional neuroimaging in adult patients with Acs, including single-case reports, case series, and group studies with pre- and post-operative data. Results: Sixty studies met the inclusion criteria. Across anatomical locations, Acs were most consistently associated with impairments in verbal and visual memory and learning, attention, and executive functions, as well as reduced processing or psychomotor speed, whereas language deficits were less consistently observed. Several studies reported postoperative improvement in one or more cognitive domains, suggesting partial reversibility in selected patients. Functional neuroimaging findings revealed altered cortical function in regions adjacent to the cyst, including reduced regional metabolism or cerebral blood flow and task-related activation changes, supporting a functional interaction between Acs and the neighboring cortex. Conclusions: Overall, adults with Acs may exhibit subtle cognitive alterations that vary according to cyst location and appear to be moderated by compensatory mechanisms. These findings underscore the clinical relevance of systematic neuropsychological evaluation and highlight the need for prospective, standardized studies integrating cognitive and neuroimaging outcomes. Full article

Ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality despite advances in prevention, diagnosis, and therapy. Traditional clinical risk scores and biomarkers often fail to fully capture the complex molecular processes underlying atherosclerosis, myocardial infarction, and ischemic cardiomyopathy, leaving substantial residual risk. MicroRNAs have emerged as promising regulators and biomarkers of cardiovascular disease, among which microRNA-21 (miR-21) has attracted particular attention. MiR-21 is deeply involved in key pathophysiological mechanisms of IHD, including endothelial dysfunction, vascular inflammation, vascular smooth muscle cell proliferation, plaque development and vulnerability, cardiomyocyte survival, and myocardial fibrosis. Accumulating clinical evidence suggests that circulating miR-21 holds diagnostic value across the ischemic continuum, from stable coronary artery disease to acute coronary syndromes, myocardial infarction, and ischemic heart failure. Moreover, miR-21 demonstrates prognostic relevance, correlating with plaque instability, adverse remodeling, heart failure progression, and long-term cardiovascular outcomes. Preclinical studies further indicate that miR-21 represents a double-edged therapeutic target, offering cardio protection in acute ischemic injury while contributing to fibrosis and maladaptive remodeling if dysregulated. This narrative review summarizes current evidence on the diagnostic, prognostic, and therapeutic utility of miR-21 in IHD, highlighting its clinical promise as well as key limitations and future translational challenges. Full article

Drug–target affinity (DTA) prediction is one of the core components of drug discovery. Despite considerable advances in previous research, DTA tasks still face several limitations with insufficient multi-modal information of drugs, the inherent sequence length limitation of protein language models, and single attention mechanisms that fail to capture critical multi-scale features. To alleviate the above limitations, we developed a multi-granularity fusion model for drug–target binding affinity prediction, termed MGF-DTA. This model is composed of three fusion modules, specifically as follows. First, the model extracts deep semantic features of SMILES strings through ChemBERTa-2 and integrates them with molecular fingerprints by using gated fusion to enhance the multi-modal information of drugs. In addition, it employs a residual fusion mechanism to integrate the global embeddings from ESM-2 with the local features obtained by the k-mer and principal component analysis (PCA) method. Finally, a hierarchical attention mechanism is employed to extract multi-granularity features from both drug SMILES strings and protein sequences. Comparative analysis with other mainstream methods on the Davis, KIBA, and BindingDB datasets reveals that the MGF-DTA model exhibits outstanding performance advantages. Further, ablation studies confirm the effectiveness of the model components and case study illustrates its robust generalization capability. Full article

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

To address challenges in weld recognition during vacuum electron beam welding caused by dark environments and metal reflections, this study proposes an improved hybrid algorithm combining YOLOv11-seg with adaptive Canny edge detection. By incorporating the UFO-ViT attention mechanism and optimizing the network architecture with the EIoU loss function, along with adaptive threshold setting for the Canny operator using the Otsu method, the recognition performance under complex conditions is significantly enhanced. Experimental results demonstrate that the optimized model achieves an average precision (mAP) of 77.4%, representing a 9-percentage-point improvement over the baseline YOLOv11-seg. The system operates at 20 frames per second (FPS), meeting real-time requirements, with the generated welding trajectories showing an average length deviation of less than 3 mm from actual welds. This approach provides an effective pre-weld visual guidance solution, which is a critical step towards the automation of electron beam welding. Full article

In recent years, extracting coastlines from high-resolution remote sensing imagery has proven difficult due to complex details and variable targets. Current methods struggle with the fact that CNNs cannot model long-range dependencies, while Transformers incur high computational costs. To address these issues, we propose CVMFusion: a land–sea segmentation network based on a U-shaped encoder–decoder structure, whereby both the encoder and decoder are hierarchically organized. This architecture integrates the local feature extraction capabilities of CNNs with the global interaction efficiency of Mamba. The encoder uses parallel ConvNeXtV2 and VMamba branches to capture fine-grained details and long-range context, respectively. This network incorporates Dynamic Multi-Scale Attention (DyMSA) and Dynamic Weighted Cross-Attention (DyWCA) modules, which replace the traditional concatenation with an adaptive fusion mechanism to effectively fuse the features from the dual-branch encoder and utilize skip connections to complete the fusion between the encoder and decoder. Experiments on two public datasets demonstrate that CVMFusion attained MIoU accuracies of 98.05% and 96.28%, outperforming existing methods. It performs particularly well in segmenting small objects and intricate boundary regions. Full article

Defect categorization is essential to product quality assurance during the production of magnetic random access memory (MRAM). Nevertheless, traditional defect detection techniques continue to face difficulties in large-scale deployments, such as a lack of labeled examples with complicated defect shapes, which results in inadequate identification accuracy. In order to overcome these problems, we create the MARMset dataset, which consists of 39,822 photos and covers 14 common defect types for MRAM defect detection and classification. Furthermore, we present a baseline framework (GAGBnet) for MRAM defect classification, including a global attention module (GAM) and an attention-guided block (AGB). Firstly, the GAM is introduced to enhance the model’s feature extraction capability. Secondly, inspired by the feature enhancement strategy, the AGB is designed to incorporate an attention-guided mechanism during feature fusion to remove redundant information and focus on critical features. Finally, the experimental results show that the average accuracy rate of this method on the MARMset reaches 92.90%. In addition, we test on the NEU-CLS dataset to evaluate cross-dataset generalization, achieving an average accuracy of 98.60%. Full article

Rapid and reliable building damage assessment (BDA) is crucial for post-disaster emergency response. However, existing methods face challenges such as complex background interference, the difficulty in jointly modeling local geometric details and global spatial dependencies, and adverse weather conditions. To address these issues, this paper proposes the Adaptive Difference State-Space Fusion Network (ADSFNet), capable of processing both optical and Synthetic Aperture Radar (SAR) data to alleviate weather-induced limitations. To achieve this, ADSFNet innovatively introduces the Adaptive Difference Attention Fusion (ADAF) module and the Hybrid Selective State-Space Convolution (HSSC) module. Specifically, ADAF integrates pre- and post-disaster features to guide the network to focus on building regions while suppressing background interference. Meanwhile, HSSC synergizes the local texture extraction of CNNs with the global modeling strength of Mamba, enabling the simultaneous capture of cross-building spatial relationships and fine-grained damage details. Experimental results on sub-meter high-resolution MultiModal (BRIGHT) and optical (xBD) datasets demonstrate that ADSFNet attains F1 scores of 71.36% and 73.98%, which are 1.29% and 0.6% higher than the state-of-the-art mainstream methods, respectively. Finally, we leverage the model outputs to construct a disaster-centric knowledge graph and integrate it with Large Language Models to develop an intelligent management system, providing a novel technical pathway for emergency decision-making. Full article

Video super-resolution (VSR) has advanced rapidly in enhancing video quality and restoring compressed content, yet leading methods often remain too costly for real-world use. We present LIF-VSR, a lightweight, near-real-time framework built with an efficiency-first philosophy, comprising economical temporal propagation, a new neighboring-frame fusion strategy, and three streamlined core modules. For temporal propagation, a uni-directional recurrent architecture transfers context through a compact inter-frame memory unit, avoiding the heavy compute and memory of multi-frame parallel inputs. For fusion and alignment, we discard 3D convolutions and optical flow, instead using (i) a deformable convolution module for implicit feature-space alignment, and (ii) a sparse attention fusion module that aggregates adjacent-frame information via learned sparse key sampling points, sidestepping dense global computation. For feature enhancement, a cross-attention mechanism selectively calibrates temporal features at far lower cost than global self-attention. Across public benchmarks, LIF-VSR achieves competitive results with only 3.06 M parameters and a very low computational footprint, reaching 27.65 dB on Vid4 and 31.61 dB on SPMCs. Full article

To address the limitations of traditional lithium battery defect detection—low efficiency, high missed detection rates for minute/composite defects, and inadequate multimodal fusion—this study develops an improved YOLOv8 model based on multimodal fusion and attention enhancement for unified full-lifecycle multi-type defect detection. Integrating visible-light and X-ray modalities, the model incorporates a Squeeze-and-Excitation (SE) module to dynamically weight channel features, suppressing redundancy and highlighting cross-modal complementarity. A Multi-Scale Fusion Module (MFM) is constructed to amplify subtle defect expression by fusing multi-scale features, building on established feature fusion principles. Experimental results show that the model achieves an mAP@0.5 of 87.5%, a minute defect recall rate (MRR) of 84.1%, and overall industrial recognition accuracy of 97.49%. It operates at 35.9 FPS (server) and 25.7 FPS (edge) with end-to-end latency of 30.9–38.9 ms, meeting high-speed production line requirements. Exhibiting strong robustness, the lightweight model outperforms YOLOv5/7/8/9-S in core metrics. Large-scale verification confirms stable performance across the battery lifecycle, providing a reliable solution for industrial defect detection and reducing production costs. Full article