Search Results (84)

In recent years, frequent emergencies such as the COVID-19 pandemic and regional conflicts have significantly heightened uncertainty risks in global industrial chains. Safeguarding industrial chains is crucial for sustaining national economic stability, and while major countries prioritize industrial chain security, establishing an effective industrial chain monitoring system remains a substantial challenge. This paper establishes a dynamic impact monitoring system (DIMS), which can dynamically monitor the risk level of the industrial chain. The DIMS consists of an industrial chain static risk assessment model (IC-SRAM) and an industrial chain dynamic event monitoring model (IC-DEMM). The IC-SRAM is used to evaluate the risks of the industrial chain itself, and the IC-DEMM is used to monitor external risk events in real time. DeepSeek is used to associate events with industrial chain nodes. The dynamic industrial chain risk value is formed based on the results of static risk assessment and dynamic risk event monitoring. Finally, an empirical analysis of the new energy vehicle industrial chain is carried out. The results show the effectiveness of the proposed DIMS and the accuracy rate of associating industrial chain nodes using DeepSeek reaches 91%. Full article

Owing to their widespread occurrence, pharmaceutical and personal care products (PPCPs) have emerged as a global environmental concern; however, research on PPCP pollution in karst tourist cities remains limited. This study investigated the concentrations and spatial distribution of 43 PPCPs in the Lijiang River Basin and identified their primary sources together with the associated ecological and health risks. In total, 43 target PPCPs were detected in the Lijiang River Basin in Guangxi, China, with concentrations ranging from 68.6 to 3170 ng/L in wastewater, 53.2 to 1400 ng/L in surface water, and 32.0 to 505 ng/L in groundwater. Caffeine (CAF), 1,7-dimethylxanthine (1,7-DIM), 4-acetaminophenol (APAP), and metformin (MFM) were the predominant contaminants across all three matrices. Notably, concentrations in tributaries exceeded those in the main stem. Principal component analysis (PCA) revealed that domestic sewage and hospital wastewater constitute the principal sources of PPCPs in the Lijiang River. Certain effluents and river reaches containing 1,7-DIM and MFM posed a moderate ecological risk (0.1 < risk quotient (RQ) < 1). The PPCPs detected in groundwater did not exceed health-based thresholds, indicating no direct risk to human health. These findings advance our understanding of PPCP contamination in karst landscapes and provide a scientific basis for water quality management in the Lijiang River Basin. Full article

Accurate prediction of dry matter intake (DMI) is essential for ration formulation, nutrient supply, and evaluation of production efficiency in lactating dairy cows. Several DMI prediction models are currently used, but most comparative studies have emphasized overall accuracy rather than whether model bias changes across biologically relevant production contexts. The objective of this study was to evaluate the context-dependent bias of widely used DMI prediction models in lactating dairy cows across classes of energy-corrected milk (ECM) and lactation stage. A literature-derived database was assembled from 135 studies consisting of 436 treatments from 6985 Holstein cows, reporting observed DMI and the variables required to implement five prediction models and evaluate their prediction error (PE): NRC2001, the Cornell Net Carbohydrate and Protein System (CNCPS), NASEM2021, Agroscope2021, and GfE2023. PE was calculated as predicted DMI minus observed DMI, such that positive values indicated overprediction and negative values indicated underprediction. Observations were classified according to ECM and days in milk (DIM). Mixed models were fitted separately for the ECM class and the lactation-stage class, with the study fitted as a random effect. PE differed among models, and the pattern of bias depended on both the ECM and the lactation-stage classes. The interaction between the ECM class and the model was significant, indicating that productive level modified model bias. The interaction between lactation-stage class and model was also significant and more pronounced, indicating marked changes in model bias across lactation stages. Across classes, NASEM2021 generally remained closest to zero, whereas GfE2023 and CNCPS showed more negative PE values in most contexts. Agroscope2021 showed a more context-sensitive pattern, and NRC2001 remained comparatively moderate across several classes. These findings indicate that the evaluation of DMI prediction models based only on global mean bias may conceal an important biological structure in PE. Context-specific evaluation, particularly across the lactation stage, may provide a more informative basis for selecting DMI prediction models for research and practical ration formulation. Full article

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved Whale Migration Algorithm (IWMA) with a Light Gradient Boosting Machine (LightGBM) is proposed to predict ketosis risk based on the milk fat-to-protein ratio (F/P) using Dairy Herd Improvement (DHI) records. The proposed IWMA enhances optimization performance through cubic chaotic initialization, elite opposition-based learning, and a Cauchy–Gaussian hybrid mutation strategy, enabling improved global exploration and convergence stability. A dataset comprising 25,155 DHI records collected from multiple commercial dairy farms over seven months was used for model development and evaluation. Experimental results demonstrate that the IWMA–LightGBM model achieves a classification accuracy of 0.8997 and a mean squared error of 0.289, consistently outperforming six benchmark optimization methods. Feature analysis identifies Herd Within Index (WHI), Energy Corrected Milk (ECM), Days in Milk (DIM), Milk Urea Nitrogen, and Foremilk as key predictors associated with metabolic risk. Overall, the proposed approach provides a robust and effective non-invasive solution for early-stage metabolic risk screening at the herd level, offering practical value for precision dairy management. It should be noted that the model is intended for risk assessment rather than clinical diagnosis of ketosis. Full article

Infrared (IR) object detection on unmanned aerial vehicle (UAV) platforms is fundamentally challenged by low signal-to-noise ratios and extremely tight onboard computational budgets. Conventional CNNs lack sufficient global context, while Transformers suffer from quadratic complexity, hindering real-time deployment. To address these bottlenecks, we propose EfficientIR-Det, a lightweight end-to-end detector featuring a holistic optimization of the backbone, encoder, and sampling mechanisms. Specifically, we design a Partial Star Network (PSN) backbone that achieves implicit high-dimensional feature expansion via element-wise multiplication to amplify weak IR signals with minimal redundancy. Furthermore, a Hierarchical Mamba (HiMamba) encoder leverages selective state-space modeling to provide linear-complexity global enhancement with superior hardware efficiency. To refine cross-scale representations, we introduce an Adaptive Gated Sampling (AGS) module and a Hierarchical Sampling Strategy (HSS) to optimize feature fusion and sampling budget allocation toward dim-small targets. On HIT-UAV, EfficientIR-Det achieves 88.4% mAP@0.5, outperforming the RT-DETR-R18 baseline by 3.3 points while reducing FLOPs and parameters by 48.9% and 44.2%, respectively. On the larger-scale DroneVehicle dataset, it consistently leads with a 74.1% mAP@0.5 and a high inference speed of 140.8 FPS. Our results offer a promising research scheme for robust, real-time infrared perception on edge-constrained UAV platforms. Full article

Mini-LED local dimming systems commonly suffer from luminance discontinuity, halo artifacts, and temporal instability in dynamic scenes. Traditional heuristic-based methods and standard convolutional neural networks often fail to capture long-range spatial dependencies and struggle to balance spatial smoothness, content fidelity, and real-time performance under hardware constraints. To address these challenges, this paper proposes SwinLightNet, an efficient adaptive backlight optimization network tailored for Mini-LED displays. Built upon a Swin Transformer framework tailored for Mini-LED backlight optimization, SwinLightNet integrates five hardware-aware design strategies: (i) a lightweight Swin variant (window size = 8, MLP ratio = 2.0) for efficient global context modeling; (ii) CNN encoder–decoder integration for multi-scale feature extraction; (iii) a partition-level alignment module ensuring spatial consistency; (iv) a backlight constraint module enforcing local luminance consistency and contrast preservation; (v) a change-aware temporal decision framework stabilizing dynamic sequences. These components synergistically resolve core limitations: global modeling suppresses halo artifacts while preserving content fidelity; alignment and constraint modules eliminate luminance discontinuity without compromising contrast; and the temporal framework guarantees flicker-free output under motion. Evaluated on DIV2K (static images) and a custom 2K-resolution video dataset (dynamic scenes), SwinLightNet demonstrates robust reconstruction quality while maintaining only 1.18 million parameters and 0.088 GFLOPs (Computational Cost). The results confirm SwinLightNet’s effectiveness in holistically addressing spatial, temporal, and hardware constraints, demonstrating strong potential for practical deployment in resource-constrained Mini-LED backlight control systems. Full article

Underground mines’ complex environments with dim lighting and high dust and humidity hamper feature extraction and reduce detection accuracy. To address this, we propose a low-light image enhancement-based target detection algorithm. Firstly, LIENet enhances low-light image quality and brightness via a dual-gamma curve and non-reference loss function-guided iterations. Secondly, the hierarchical feature extraction (HFE) method with a dual-branch structure captures long-term and local correlations, focusing on critical corner regions. Finally, HFE is combined with a feature pyramid structure for comprehensive feature representation through a top-down global adjustment. Our method, validated on a self-built dataset, outperforms other algorithms with an mAP@0.5 of 96.96% and mAP@0.5:0.95 of 71.1%, proving excellent low-light detection performance in mines. Full article

Myopia has emerged as a global public health crisis, with prevalence exceeding 80% in East Asian urban populations and rising rapidly worldwide. High myopia substantially increases the lifetime risk of sight-threatening complications, including myopic macular degeneration, retinal detachment, and glaucoma. Multiple interventions have been investigated to slow myopia progression in children. Behavioral strategies, particularly increased outdoor exposure, demonstrate protective effects against myopia onset and may modestly slow progression, whereas several historically used approaches show no clinically meaningful benefit. Spectacle lens interventions include simultaneous defocus designs (e.g., DIMS, HALT, CARE) and contrast-modulating diffusion optics (DOT) lenses; collectively, these technologies have demonstrated consistent and clinically meaningful reductions in axial elongation across randomized clinical trials. Contact lens modalities, including dual-focus soft lenses and orthokeratology, have also demonstrated substantial efficacy in slowing progression in controlled studies. Low-dose atropine remains a cornerstone pharmacological therapy, particularly at concentrations between 0.01% and 0.05%, offering significant efficacy with minimal side effects. Repeated low-level red-light therapy has shown promising short-term reductions in axial elongation, although long-term safety and rebound effects remain uncertain. Combination therapy targeting complementary optical and pharmacological pathways shows additive benefits, particularly in children inadequately controlled with monotherapy. Contemporary clinical management emphasizes risk stratification based on axial length, age-specific growth targets, and structured longitudinal monitoring. The goal of modern myopia management is not merely to slow progression, but to prevent high myopia and reduce the lifetime burden of vision-threatening complications through a proactive, individualized approach increasingly regarded as the standard of care. Full article

To address the issues of uneven population initialization, insufficient individual information interaction, and passive boundary handling in the standard Golden Jackal Optimization (GJO) algorithm, while improving the accuracy and efficiency of multilevel thresholding in artistic image segmentation, this paper proposes an improved Golden Jackal Optimization algorithm (MGJO) and applies it to this task. MGJO introduces a high-quality point set for population initialization, ensuring a more uniform distribution of initial individuals in the search space and better adaptation to the complex grayscale characteristics of artistic images. A dual crossover strategy, integrating horizontal and vertical information exchange, is designed to enhance individual information sharing and fine-grained dimensional search, catering to the segmentation needs of artistic image textures and color layers. Furthermore, a global-optimum-based boundary handling mechanism is constructed to prevent information loss when boundaries are exceeded, thereby preserving the boundary details of artistic images. The performance of MGJO was evaluated on the CEC2017 (dim = 30, 100) and CEC2022 (dim = 10, 20) benchmark suites against seven algorithms, including GWO and IWOA. Population diversity analysis, exploration–exploitation balance assessment, Wilcoxon rank-sum tests, and Friedman mean-rank tests all demonstrate that MGJO significantly outperforms the comparison algorithms in optimization accuracy, stability, and statistical reliability. In multilevel thresholding for artistic image segmentation, using Otsu’s between-class variance as the objective function, MGJO achieves higher fitness values (approaching Otsu’s optimal values) across various artistic images with complex textures and colors, as well as benchmark images such as Baboon, Camera, and Lena, in 4-, 6-, 8-, and 10-level thresholding tasks. The resulting segmented images exhibit superior peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) compared to other algorithms, more precisely preserving brushstroke details and color layers. Friedman average rankings consistently place MGJO in the lead. These experimental results indicate that MGJO effectively overcomes the performance limitations of the standard GJO, demonstrating excellent performance in both numerical optimization and multilevel thresholding artistic image segmentation. It provides an efficient solution for high-dimensional complex optimization problems and practical demands in artistic image processing. Full article

In line with the global goal of achieving climate neutrality, a flexible energy system capable of accommodating the uncertainties induced by renewable energy sources becomes vitally important. This paper investigates the electricity demand flexibility characteristics and develops demand-response (DR) control strategies for grid-interactive buildings. First, a building’s flexible loads are classified into three types, interruptible loads (ILs), shiftable loads (SLs), and adjustable loads (ALs). The load flexibility characteristics, including real-time response capabilities, the time window range, and the adaptive adjustment ratios, are investigated. Second, DR control strategies and their features, which form the basis for achieving different optimization objectives, are detailed. Finally, three DR optimization objectives are proposed, including maximizing load reduction, maximizing economic benefits, and ensuring stable load reduction and recovery. Through case studies of a residential building and an office building, the results demonstrate the effectiveness of these DR strategies for load reduction and cost savings under different DR objectives. For the residential building, our results showed that over 50% of the electricity load could be shifted, resulting in electricity bill savings of over 17.6%. For office buildings, various DR control strategies involving zone temperature resetting, lighting dimming, and water storage utilization can achieve a total electricity load reduction of 28.1% to 63.6% and electricity bill savings of 7.39% to 26.79%. The findings from this study provide valuable benchmarks for assessing electricity flexibility and DR performance for other buildings. Full article

Infrared dim and small target detection is crucial for long-range sensing. However, its deep representation learning is severely constrained by the scarcity of accurately annotated real data, and related research remains underdeveloped. Existing data generation methods based on patch synthesis or geometric transformations fail to incorporate the physical degradation mechanisms of infrared imaging systems and reasonable environmental constraints, leading to significant discrepancies between synthetic data and real-world scenarios. To address this issue, this paper proposes a novel pseudo-sample generation paradigm based on physics-informed degradation modeling and high-order constraints. First, we construct an infrared image degradation model that decouples the degradation processes of targets and backgrounds at the signal level, achieving accurate modeling of real infrared imaging while ensuring the reliability of the degradation process through information fidelity optimization. Second, an online grid-based high-order constraint strategy is designed, which synergistically integrates global semantic, local structural, and grayscale constraints based on statistical distribution consistency to generate a high-fidelity infrared simulation dataset. Finally, we build a complete self-supervised detection framework incorporating classical neural networks, customized loss functions, and two-dimensional information evaluation metrics. Extensive experiments demonstrate that the synthetic data generated by our method significantly outperforms existing simulated datasets on authenticity metrics. It also effectively enhances the generalization performance of various detectors in real-world scenarios, achieving detection accuracy superior to baseline models trained on traditional simulated data. Full article

The detection of exoplanets in space-based photometry relies on identifying periodic transit signatures in stellar light curves. The Kepler Threshold Crossing Events (TCE) catalog collects all periodic dimming signals detected by the pipeline, while the Kepler Objects of Interest (KOI) catalog provides vetted dispositions (CONFIRMED, CANDIDATE, FALSE POSITIVE). However, the pathway from raw TCE detections to KOI classifications remains ambiguous in many borderline cases. We introduce FAS-XAI, a framework that integrates Fuzzy C-Means (FCM) clustering, supervised learning, and explainable AI (XAI) to improve transparency in exoplanet candidate classification. FCM applied to TCE parameters (period, duration, depth, and SNR) reveals three meaningful regimes in the transit-signal space and quantifies ambiguity through fuzzy memberships. Linking these clusters to KOI dispositions highlights a progressive consolidation of confirmed planets within the high-SNR, medium-duration regime. A supervised XGBoost classifier trained on KOI labels and augmented with fuzzy memberships achieves strong performance (Accuracy = 0.73, Macro F1 = 0.69, ROC–AUC = 0.855), clearly separating CONFIRMED and FALSE POSITIVE objects while appropriately reflecting the transitional nature of CANDIDATES. SHAP, LIME, and ELI5 provide consistent global and local attributions, identifying period, duration, depth, SNR, and fuzzy ambiguity as the key explanatory features. Finally, stellar parameters from Kepler DR25 validate the physical plausibility of the detected regimes, demonstrating that FAS-XAI captures astrophysically meaningful patterns rather than purely statistical structures. Overall, the framework illustrates how fuzzy logic and explainable AI can jointly enhance the interpretability and scientific rigor of exoplanet vetting pipelines. Full article

Addressing the technological requirement for real-time monitoring of fish density in dim aquaculture environments, this study proposes a near-infrared (NIR) image classification method using a modified ResNet18 architecture. Initially, an NIR-Fish dataset comprising 736 high-quality annotated images (256 × 256 resolution) spanning three density scenarios (low, medium, and high density) was constructed. Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing was implemented with an 8 × 8 tiling strategy and clip limit = 4.0, significantly enhancing the discernibility of faint boundary features. A dual-channel attention module (DCAM) was embedded into the ResNet18 backbone, featuring a parallel architecture integrating Global Average Pooling (GAP) and Global Max Pooling (GMP). This design synergistically optimized local salient feature enhancement and global statistical feature fusion through parameter-shared fully connected layers (reduction ratio of 16:1). The experiments show that the classification accuracy of the proposed method on the independent test set is 80.57%, which is 4.34 percentage points higher than that of the original ResNet18. F1 scores for the three density levels were 0.8308 (low), 0.7674 (medium), and 0.8294 (high), respectively. Ablation studies confirmed the dual-channel design’s significant performance contribution, while the parameter-sharing mechanism effectively mitigated overfitting risks. By leveraging feature complementarity and lightweight design, this work overcomes the classification bottleneck for NIR images under low signal-to-noise conditions, providing a highly robust technical solution for intelligent aquaculture management. Full article

In the field of infrared small target detection (ISTD), single-frame ISTD (SISTD), using only spatial features, cannot deal well with dim targets in cluttered backgrounds. In contrast, multi-frame ISTD (MISTD), utilizing spatio-temporal information from videos, can significantly enhance moving target features and effectively suppress background interference. However, current MISTD algorithms are limited by fixed-size time windows, resulting in an inability to adaptively adjust the input amount of spatio-temporal information for different detection scenarios. Moreover, utilizing spatio-temporal features remains a significant challenge in MISTD, particularly in scenarios involving slow-moving targets and fast-moving backgrounds. To address the above problems, we propose a memory-based temporal Transformer U-Net (MTTU-Net), which integrates a memory-based temporal Transformer module (MTTM) into U-Net. Specifically, MTTM utilizes the proposed D-ConvLSTM to sequentially transmit the temporal information in the form of memory, breaking through the limitation of the time window paradigm. And we propose a Transformer-based interactive fusion approach, which is dominated by spatial features of the to-be-detected frame and supplemented by temporal features in the memory, thereby effectively dealing with targets and backgrounds with various motion states. In addition, MTTM is divided into a temporal channel-cross Transformer module (TCTM) and a temporal space-cross Transformer module (TSTM), which achieve target feature enhancement and global background perception through feature interactive fusion in the channel and space dimensions, respectively. Extensive experiments on IRDST and IDSMT datasets demonstrate that our MTTU-Net outperforms existing MISTD algorithms, and they verify the effectiveness of the proposed modules. Full article

Achieving robust visual detection under challenging conditions such as poor illumination and target deformation remains a critical challenge for computer vision systems. Although the YOLO series of object detection algorithms excel in speed and accuracy, their performance significantly degrades under non-ideal lighting conditions. To address this issue, we propose a Hybrid Dynamic Perception Network (HDPNet), a framework specifically designed for high-precision, real-time object detection in harsh environments. HDPNet integrates three core modules into the YOLOv8n architecture to form a hybrid structure. The Dynamic Illumination-aware Module (DIM) adaptively enhances features under varying illumination through global encoding and a dual-attention mechanism, the Interactive Attention Fusion Network (IAFN) optimizes cross-modal features using a lightweight Transformer-CNN interactive architecture, and the Multi-branch Decomposition Network (MDN) captures multi-scale deformation features by combining deformable convolution and sparse Transformer. Experimental results on the self-built low-light industrial express package dataset named njpackage show that our method achieves an mAP@0.5 of 86.6%, which is 4.4% higher than the baseline YOLOv8n model, while maintaining real-time inference speed of ≥45 FPS. The proposed HDPNet not only provides an effective solution for logistics automation but also offers a robust and versatile hybrid technical framework adaptable to other vision tasks facing similar challenges. Full article