Search Results (25,913)

Lactylation, a recently identified post-translational modification (PTM) triggered by excessive lactate accumulation, has emerged as a crucial regulator linking metabolic reprogramming to pathological processes in liver diseases. In hepatic contexts, aberrant lactylation contributes to a range of pathological processes, including inflammation, dysregulation of lipid metabolism, angiogenesis, and fibrosis. Importantly, lactylation has been shown to impact tumor growth, metastasis, and therapy resistance by modulating oncogene expression, metabolic adaptation, stemness, angiogenesis, and altering the tumor microenvironment (TME). This review synthesizes current knowledge on the biochemical mechanisms of lactylation, encompassing both enzymatic and non-enzymatic pathways, and its roles in specific liver diseases. From a therapeutic perspective, targeting lactate availability and transport, as well as the enzymes regulating lactylation, has demonstrated promise in preclinical models. Additionally, combinatorial approaches and natural compounds have shown efficacy in disrupting lactylation-driven pathways, providing insights into future research directions for hepatic diseases. Although the emerging role of lactylation is gaining attention, its spatiotemporal dynamics and potential for clinical translation are not yet well comprehended. This review aims to synthesize the multifaceted roles of lactylation, thereby bridging mechanistic insights with actionable therapeutic strategies for liver diseases. Full article

The rapid detection and confirmation of Suspicious Regions of Forest Fire (SRoFF) are critical for timely alerts and firefighting operations. In the early stages of forest fires, small flames and heavy occlusion lead to low accuracy, false detections, omissions, and slow inference in existing target-detection algorithms. We constructed the Suspicious Regions of Forest Fire Dataset (SRFFD), comprising publicly available datasets, relevant images collected from online searches, and images generated through various image enhancement techniques. The SRFFD contains a total of 64,584 images. In terms of effectiveness, the individual augmentation techniques rank as follows (in descending order): HSV (Hue Saturation and Value) random enhancement, copy-paste augmentation, and affine transformation. A detection model named SRoFF-Yolover is proposed for identifying suspicious regions of forest fire, based on the YOLOv8. An embedding layer that effectively integrates seasonal and temporal information into the image enhances the prediction accuracy of the SRoFF-Yolover. The SRoFF-Yolover enhances YOLOv8 by (1) adopting dilated convolutions in the Backbone to enlarge feature map receptive fields; (2) incorporating the Convolutional Block Attention Module (CBAM) prior to the Neck’s C2fLayer for small-target attention; and (3) reconfiguring the Backbone-Neck linkage via P2, P4, and SPPF. Compared with the baseline model (YOLOv8s), the SRoFF-Yolover achieves an 18.1% improvement in mAP@0.5, a 4.6% increase in Frames Per Second (FPS), a 2.6% reduction in Giga Floating-Point Operations (GFLOPs), and a 3.2% decrease in the total number of model parameters (#Params). The SRoFF-Yolover can effectively detect suspicious regions of forest fire, particularly during winter nights. Experiments demonstrated that the detection accuracy of the SRoFF-Yolover for suspicious regions of forest fire is higher at night than during daytime in the same season. Full article

In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data. A cross-modal attention mechanism, environment-guided modality weighting strategy, and decoupled recognition heads are incorporated to enhance the model’s robustness against small targets, intermodal variations, and environmental disturbances. Evaluated on a high-complexity multimodal field dataset, the proposed model significantly outperforms mainstream methods across four key metrics, precision, recall, F1-score, and mAP@50, achieving 91.5% precision, 89.2% recall, 90.3% F1-score, and 88.0% mAP@50. These results represent an improvement of over 6% compared to representative models such as YOLOv8 and DETR. Additional ablation studies confirm the critical contributions of key modules, particularly under challenging scenarios such as low light, strong reflections, and sensor data noise. Moreover, deployment tests conducted on the Jetson Xavier edge device demonstrate the feasibility of real-world application, with the model achieving a 25.7 FPS inference speed and a compact size of 48.3 MB, thus balancing accuracy and lightweight design. This study provides an efficient, intelligent, and scalable AI solution for pest surveillance and biological control, contributing to precision pest management in agricultural ecosystems. Full article

Semi-enclosed bays are greatly affected by human activities and have undergone drastic changes in their ecological environment, which requires our continuous attention. Laizhou Bay (LZB) is a typical semi-closed bay that is greatly influenced by human activities, and it is highly representative on a global scale. Investigating the multi-scale variation in nutrient concentrations in semi-enclosed bays can provide scientific support for environmental management and policy adjustments. To address the limitations of in situ data and the high cost of field surveys, this study utilizes machine learning methods to construct MODIS remote sensing models for quantitatively analyzing the concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the surface water of LZB, as well as the spatiotemporal factors influencing them. Among various methods tested, the Support Vector Machine Regression (SVR) algorithm demonstrated the best performance in retrieving nutrient concentrations in LZB. The R² values of the DIN and DIP retrieval results based on the SVR algorithm are 0.91 and 0.92, respectively, while the RMSE values are 5.43 and 0.08 μmol/L, respectively. The retrieval results indicate that nearshore nutrient concentrations are significantly higher than those in offshore areas. Temporally, from 2003 to 2024, the DIN concentration in l has decreased at a rate of 0.4 μmol/L/yr, while the DIP concentration has remained relatively stable. Given sufficient observation data, the proposed machine learning and remote sensing approach can be effectively applied to other bays, offering the advantages of long time series, high spatial resolution, and a low cost. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

The construction of cascade dam systems profoundly reshapes river hydrological processes, yet the analysis of their spatial heterogeneity effects has long been constrained by the mechanistic deficiencies and interpretability limitations of traditional mechanistic models. Focusing on the middle-lower Han River (a 652 km reach regulated by seven dams) as a representative case, this study develops an LSTM-Attention-SHAP interpretable framework to achieve, for the first time, intelligent decoupling of dam-induced hydrological effects and mechanistic analysis of spatial differentiation. Key findings include the following: (1) The LSTM model demonstrates exceptional predictive performance of water level and flow rate in intensively regulated reaches (average Nash–Sutcliffe Efficiency, NSE = 0.935 at Xiangyang, Huangzhuang, and Xiantao stations; R² = 0.988 for discharge at Xiantao Station), while the attention mechanism effectively captures sensitive factors such as the abrupt threshold (>560 m³/s) in the Tangbai River tributary; (2) Shapley Additive exPlanations (SHAP) values reveal spatial heterogeneous dam contributions: the Cuijiaying Dam increases discharge at Xiangyang station (mean SHAP +0.22) but suppresses water level at Xiantao station (mean SHAP −0.15), whereas the Wangfuzhou Dam shows a stable negative correlation with Xiangyang water levels (mean SHAP −0.18); (3) dam operations induce cascade effects through altered channel storage capacity. These findings provide spatially adaptive strategies for flood risk zoning and ecological operations in globally intensively regulated rivers such as the Yangtze and Mekong. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Facial emotion recognition has become increasingly important in healthcare, where understanding delicate cues like pain, discomfort, or unconsciousness can support more timely and responsive care. Yet, recognizing facial expressions in real-world settings remains challenging due to varying lighting, facial occlusions, and hardware limitations in clinical environments. To address this, we propose TriViT-Lite, a lightweight yet powerful model that blends three complementary components: MobileNet, for capturing fine-grained local features efficiently; Vision Transformers (ViT), for modeling global facial patterns; and handcrafted texture descriptors, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG), for added robustness. These multi-scale features are brought together through a texture-aware cross-attention fusion mechanism that helps the model focus on the most relevant facial regions dynamically. TriViT-Lite is evaluated on both benchmark datasets (FER2013, AffectNet) and a custom healthcare-oriented dataset covering seven critical emotional states, including pain and unconsciousness. It achieves a competitive accuracy of 91.8% on FER2013 and of 87.5% on the custom dataset while maintaining real-time performance (~15 FPS) on resource-constrained edge devices. Our results show that TriViT-Lite offers a practical and accurate solution for real-time emotion recognition, particularly in healthcare settings. It strikes a balance between performance, interpretability, and efficiency, making it a strong candidate for machine-learning-driven pattern recognition in patient-monitoring applications. Full article

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making. Full article

Understanding the mechanisms governing forest community assembly across different growth stages is essential for revealing succession dynamics and guiding forest restoration. While much attention has been given to overstory trees, the understory regeneration layer, critical for forest succession, remains less explored, particularly regarding its stage-specific survival strategies and assembly processes. This study investigates the natural regeneration of Quercus variabilis forests in northern China, focusing on the transition from early to later growth stages. Our objectives were to (1) identify the phylogenetic and functional structures of regeneration communities at early and later stages, (2) explore their responses to environmental gradients, and (3) assess the roles of deterministic and stochastic processes in shaping community assembly. We integrated phylogenetic structure, functional traits, and environmental gradients to examine natural regeneration communities. The results revealed clear stage-dependent patterns: communities exhibited random phylogenetic and functional structures in the early growth stage, suggesting a dominant role of stochastic processes during early recruitment. In contrast, communities showed phylogenetic clustering and functional overdispersion in later growth stages, indicating the increasing influence of environmental filtering and interspecific competition as individuals developed. Generalized Dissimilarity Modeling (GDM) further revealed that dispersal limitation and pH were key predictors of phylogenetic β-diversity in the later growth stage, while total phosphorus drove functional β-diversity in the later growth stage. No significant predictors were found for β-diversity in the early stage. These findings highlight the shift from stochastic to deterministic processes during forest regeneration, emphasizing the stage-dependent nature of assembly mechanisms. Our study elucidates the stage-specific assembly rules of Q. variabilis forests and offers theoretical guidance for stage-targeted interventions in forest management to promote positive succession. Full article

Against the backdrop of global sustainable development and environmental governance, research on contractors’ green behavior has received increasing attention. However, the research progress and knowledge structure within this field remain unclear. This study, therefore, reviews the literature published between 1985 and 2005 in the Web of Science Core Collection and Scopus databases. It aims to reveal the current state of research, identify gaps, and propose future research directions. First, through bibliometric analysis, this study explores research trends, journal distribution, country distribution, author distribution, institutional distribution, and collaboration patterns. Second, social network analysis of keyword co-occurrence is conducted to identify emerging research hotspots and frontier topics. Third, content analysis complements the quantitative findings by synthesizing theoretical foundations, methodological approaches, and influencing factors. Finally, potential future research directions are outlined regarding collaboration models, thematic integration, theoretical frameworks, research methods, factors, research boundaries, contextual applications, and behavioral outcome variables. By systematically reviewing the literature on contractors’ green behavior, this study offers valuable insights for future research as well as management practices. Full article

The aim of this study was to address the issue of significant performance degradation in existing defogging algorithms under extreme fog conditions. Traditional Taylor series-based deformable convolutions are limited by local approximation errors, while the heavy-tailed characteristics of the Cauchy distribution can more successfully model outliers in fog images. The following improvements are made: (1) A displacement generator based on the inverse cumulative distribution function (ICDF) of the Cauchy distribution is designed to transform uniform noise into sampling points with a long-tailed distribution. A novel double-peak Cauchy ICDF is proposed to dynamically balance the heavy-tailed characteristics of the Cauchy ICDF, enhancing the modeling capability for sudden changes in fog concentration. (2) An innovative Cauchy–Gaussian fusion module is proposed to dynamically learn and generate hybrid coefficients, combining the complementary advantages of the two distributions to dynamically balance the representation of smooth regions and edge details. (3) Tree-based multi-path and cross-resolution feature aggregation is introduced, achieving local–global feature adaptive fusion through adjustable window sizes (3/5/7/11) for parallel paths. Experiments on the RESIDE dataset demonstrate that the proposed method achieves a 2.26 dB improvement in the peak signal-to-noise ratio compared to that obtained with the TaylorV2 expansion attention mechanism, with an improvement of 0.88 dB in heavily hazy regions (fog concentration > 0.8). Ablation studies validate the effectiveness of Cauchy distribution convolution in handling dense fog and conventional lighting conditions. This study provides a new theoretical perspective for modeling in computer vision tasks, introducing a novel attention mechanism and multi-path encoding approach. Full article

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions. Full article