Search Results (36,908)

In complex agricultural production environments, small-target pests—characterized by tiny scales, strong background confusion, and close dependence on environmental conditions—pose major challenges to precise monitoring and green pest control. To facilitate the transition from experience-driven to data-driven pest management, a multi-scale vision–sensor collaborative recognition method is proposed for field and protected agriculture scenarios to improve the accuracy and stability of small-target pest recognition under complex conditions. The method jointly models multi-scale visual representations and pest ecological mechanisms: a multi-scale visual feature module enhances fine-grained texture and morphological cues of small targets in deep networks, alleviating feature sparsity and scale mismatch, while environmental sensor data, including temperature, humidity, and illumination, are introduced as priors to modulate visual features and explicitly incorporate ecological constraints into the discrimination process. Stable multimodal fusion and pest category prediction are then achieved through a vision–sensor collaborative discrimination module. Experiments on a multimodal dataset collected from real farmland and greenhouse environments in Linhe District, Bayannur City, Inner Mongolia, demonstrate that the proposed method achieves approximately 93.1% accuracy, 92.0% precision, 91.2% recall, and a 91.6% $F_1$-score on the test set, significantly outperforming traditional machine learning approaches, single-scale deep learning models, and multi-scale vision baselines without environmental priors. Category-level evaluations show balanced performance across multiple small-target pests, including aphids, thrips, whiteflies, leafhoppers, spider mites, and leaf beetles, while ablation studies confirm the critical contributions of multi-scale visual modeling, environmental prior modulation, and vision–sensor collaborative discrimination. Full article

Mycotoxin contamination of grains during storage and transportation represents a significant threat to global food security. Conventional detection methods exhibit limitations in terms of real-time monitoring. This study presents a compact smart gas sensing system for mycotoxins, facilitating non-destructive testing of corn infected with fungi by analyzing the volatile organic compounds (VOCs) emitted during fungal growth. It also facilitates the precise quantitative detection of Aflatoxin B₁ (AFB₁). Additionally, a dual-branch convolutional neural network (DB-CNN) model has been developed to conduct an in-depth analysis of the temporal and spatial characteristics of VOCs signals. The system achieves 100% accuracy in identifying grains (corn, peanuts, wheat, and rice) infected with Fusarium graminearum and Aspergillus flavus by extracting the characteristic fingerprint spectra of fungal VOCs. In the quantitative analysis, the DB-CNN exhibits good performance (RMSE = 1.0292 μg/kg, R² = 0.9994). In addition, the designed detection system supports wireless transmission and can be connected to a smartphone for data transfer, thereby facilitating data storage and remote monitoring. The entire detection process is completed within 4 min. This study provides an innovative technical foundation for dynamic real-time monitoring of fungal contamination in the food supply chain, contributing to early warning systems and quality control measures. Full article

As a critical step in industrial quality control, surface defect detection in aluminum materials remains challenging for minor defects despite advances in deep learning. To address this, this paper proposes an enhanced YOLOv8-based model, BFI-YOLO, that incorporates a Bidirectional Multi-scale Residual Network. Specifically, we design a Bidirectional Multi-scale Feature Pyramid Network (BM-FPN) based on BiFPN to strengthen cross-scale feature fusion. The parameter-free SimAM attention module is embedded to enhance subtle defect responses while suppressing background texture interference, without introducing additional computational overhead.Furthermore, we develop a Multi-scale Residual Convolution (MSRConv) module to capture defects of varying sizes on aluminum surfaces comprehensively. MSRConv utilizes multi-scale convolutional kernels to adapt to cross-scale defect features and retains shallow details via residual connections, thereby strengthening the model’s representation of fine defects. Extensive experiments on the public TAPSDD dataset show that BFI-YOLO achieves a precision of 91.3%, a recall of 89.8%, and mAP@0.5 of 92.1%, with only 1.8 M parameters. Compared to the baseline, BFI-YOLO reduces parameters by 40% while increasing mAP@0.5 by 4.2%, effectively balancing detection accuracy and lightweight performance. Optimized for resource-constrained industrial platforms such as embedded systems and mobile robots, BFI-YOLO meets real-time monitoring requirements while achieving competitive detection accuracy, providing an efficient and practical solution for metal surface defect detection. Full article

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone (P₄).  Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming. Full article

Microplastic (MP) pollution represents a significant environmental threat, impacting aquatic ecosystems and human health. This review examines critical elements of MP risk assessment, including exposure pathways, properties (polymer type, size, and shape), bioaccumulation, and ecological and health effects. It underscores the challenges of quantifying MP exposure and identifying pollutants, as well as gaps in understanding pollutant adsorption/desorption and biofilm impacts. MPs serve as carriers for organic pollutants, heavy metals, and chemical additives, potentially magnifying toxic effects. Emerging contaminants, such as pharmaceuticals, exacerbate these risks. Laboratory research is crucial to trace MPs through food chains from primary producers to humans and assess bioaccumulation and health impacts. Current assessments, however, are insufficient to provide comprehensive ecological risk evaluations. The review highlights the need for improved methodologies to assess MPs’ fate, trophic transfer, and long-term ecological effects. MPs often release harmful additives like plasticizers and flame retardants, necessitating studies to differentiate the impacts of polymers and additives. It emphasizes integrating MP toxicity data into risk models while fostering collaboration among scientists, policymakers, and communities. The paper advocates for a comprehensive framework combining advanced analytical methods and environmental monitoring to refine risk assessment models. These efforts aim to strengthen public awareness, support informed environmental policies, and promote sustainable practices to mitigate MP pollution impacts. Addressing these research gaps will significantly enhance the scientific understanding of MP risks and guide effective management strategies for environmental and human health protection. Full article

Large deformations in deep soft rock roadways primarily stem from low rock strength under high in situ stress and intense mining disturbance. This renders stability control a critical challenge in tunneling support engineering. Utilizing Xinhe Coal Mine’s deep soft rock tunnel as a representative case, this study integrates field monitoring, laboratory experimentation, and numerical simulation to investigate how excavation and grouted rock bolting influence surrounding rock stability. Building upon field-observed deformation mechanisms and support failure patterns, constitutive models for FLAC^3D’s embedded cable and beam elements were modified to achieve high-fidelity simulation of grouted support systems. Numerical models simulating diverse support schemes were established to analyze roadway displacement fields, plastic failure development, and structural behavior of support components, ultimately identifying the optimal rehabilitation solution. The research results indicate that the numerical simulation outcomes of the original support scheme exhibit good agreement with field observations in terms of roadway deformation patterns, deformation magnitudes, and occurrences of bolt/cable fractures. This demonstrates that the adopted refined numerical simulation methodology and parameters are reasonable and exhibit high reliability. Considering both surrounding rock stability and cost control, Roadway Rehabilitation Scheme S1 was identified as the optimal support solution. Its specific parameters are pre-grouting + full-section rock bolts (diameter 22 mm, length 2.4 m, spacing 0.8 m, row spacing 1.6 m) + full-section grouted cables (diameter 22 mm, length 6.2 m, spacing 1.0 m, row spacing 1.6 m). Full article

Urban wetlands in coastal cities are under growing strain from urban growth, climate change, and governance that is often fragmented. This study evaluates the condition of the freshwater dune lakes located in the Veracruz–Boca del Río–Medellín conurbation in Mexico, a protected corridor made up of 33 dune lakes that is increasingly pressured by urban expansion. We used an interdisciplinary approach that combined ecological monitoring, legal analysis, and participatory management tools. Fieldwork included 24 h monitoring of dissolved oxygen, measurements of Biochemical Oxygen Demand (BOD₅) in representative systems, a diachronic review of the legal evolution of five Natural Protected Areas (NPAs), and community workshops to jointly design interventions. The results showed strong day–night swings in oxygen (4.0–14.8 mg/L) linked to vegetation dynamics, with nighttime hypoxia posing risks for aquatic fauna. BOD₅ ranged from 4.8 to 150.3 mg/L, pointing to severe organic pollution in the most degraded system. The legal review identified repeated patterns of environmental regression, expressed through reductions in protected polygons, the legalization of irregular settlements, and the fragmentation of protected areas through judicial processes. In response, we propose a hybrid management model that brings together riparian restoration, Sustainable Urban Drainage Systems (SUDS), green infrastructure, and participatory monitoring, emphasizing a key 100 m buffer zone. This integrated strategy aims to improve flood regulation, reduce urban heat island effects, and enhance water quality, while also reinforcing community stewardship and legal protection. We conclude that conserving these urban wetlands effectively requires adaptive approaches that connect landscape-scale and local-scale actions, which are essential for climate adaptation in rapidly urbanizing coastal regions. Full article

Convolutional neural networks (CNNs) can achieve high accuracy in photovoltaic (PV) fault recognition from RGB imagery, yet their decisions may rely on shortcut cues induced by heterogeneous backgrounds, viewpoints, and class imbalance. This work presents a multi-method explainability audit on the Kaggle PV Panel Defect Dataset (six classes), comparing five architectures (Baseline CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0). Explanations are obtained with LIME superpixel surrogates (reported together with kernel-weighted surrogate fidelity), occlusion sensitivity (quantified via IoU@Top10% against consistent proxy masks, Shannon entropy, and Hoyer sparsity), and Integrated Gradients evaluated by deletion–insertion faithfulness and a Faithfulness Gap. While ResNet50 yields the best predictive performance, EfficientNetB0 shows the most consistent faithfulness evidence and stable panel-centered attributions. The analysis highlights class-dependent vulnerability to context cues, especially for the Clean and damaged classes, and supports using quantitative explainability diagnostics during model selection and dataset curation to mitigate shortcuts in vision-based PV monitoring. Full article

Digital twins are increasingly used to support monitoring, prediction, and decision-making in complex cyber–physical systems; however, most existing digital twin implementations remain domain-specific, model-centric, and weakly integrated with human expertise. The aim of this study is to examine how digital twins can be designed as reusable cognitive architectures capable of consistent reasoning, semantic interpretation, and human-centered decision support across heterogeneous application domains. To achieve this aim, the paper proposes the reusable cognitive digital twin (RCDT) paradigm, which combines a reusable architectural core containing structural, behavioral, functional, and cognitive invariants with a cognitive orchestration layer implementing four coordinated reasoning modalities: structural, generative, analytical, and operational. The methodology is architectural and conceptual, supported by formal operator-based modeling and illustrated through two contrasting case studies—a safety-critical aviation system and a large-scale smart city environment. The results demonstrate that the same reusable cognitive modules and evaluation indices can be instantiated across both domains, enabling explicit management of semantic consistency, scenario adequacy, and decision confidence, as well as systematic integration of human expertise. These findings indicate that RCDTs provide a transferable and interpretable cognitive foundation for intelligent digital ecosystems, extending traditional digital twin capabilities beyond domain-bound and purely data-driven approaches. Full article

A primary bottleneck in deploying Fiber Bragg Grating (FBG) sensors lies in their inherent dual sensitivity to thermal and mechanical variations, which mandates robust decoupling mechanisms for precise parameter extraction. To address this persistent cross-sensitivity issue, this study introduces a novel interrogation scheme that integrates a Convolutional Neural Network with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture. Instead of relying on conventional peak-tracking algorithms or isolated central wavelengths, our proposed data-driven strategy directly mines structural features from the full reflection spectra, thereby substantially mitigating cross-interference errors. The experimental results reveal that the coefficients of determination (R²) for strain and temperature prediction reach 99.37% and 99.75% each, while the root mean square errors (RMSEs) are 13.51 µε and 1.42 °C, respectively. The proposed method requires only a single FBG sensor, which reduces the sensor requirements, showing great potential in sensing applications requiring low costs and high adaptability. In addition, in some special environments, temperature information cannot be obtained, so we utilize another reference FBG to realize the temperature compensation. Meanwhile, we proposed a spectral differencing method (SDM) by differencing the spectra of the two FBGs to obtain the spectra containing only strain information and sent them as a dataset for model training, with a 4-times improvement in accuracy over traditional compensation methods. Finally, we also explored the application of the system for distributed FBGs, achieving an absolute peak wavelength interrogation precision of approximately ±0.02 nm. The system is expected to be applied in the field of structural health monitoring, which is promising even in harsh environments. Full article

Stress can cause many disorders, including mental and physical ones, if it persists. To take timely and effective early intervention measures, mental stress levels must be carefully monitored. This study investigates the rapidly growing topic of mental stress detection, focusing on the primary goals and mechanisms of existing detection frameworks. The main objectives and mechanisms will be highlighted. This study examines physiological sensors, stressors, algorithms, monitoring methods, and validation tools used to assess and classify mental stress. The study targets physiological sensors. Wearable sensors are becoming more popular because they can continuously monitor physiological responses in human-like environments. This allows them to reveal relevant stress patterns across various work environments. Numerous physiological sensors are used regularly. Galvanic skin response (GSR), electrocardiogram (ECG), photoplethysmography (PPG), electroencephalography (EEG), and pupil diameter camera systems are examples of these sensors. The combination of these sensors provides a wealth of cognitive and autonomic response data for stress detection. This review examines AI-based methods for interpreting complex physiological data. Machine learning and ensemble models are emphasized for improving stress classification accuracy and reducing incorrect classifications. In addition, this article discusses stressors used to induce reliable physiological responses. Validated self-report instruments are being reviewed as benchmarking tools for objective sensor-based measurements. STAI and PSS-10 are examples. These instruments demonstrate a strong correlation between stress and anxiety and physiological health outcomes. In conclusion, this review discusses future research avenues, focusing on advanced artificial intelligence-driven approaches and sophisticated sensors. These developments aim to better define stress levels and physiological factors that have not been thoroughly studied. Full article

Ensuring the stability of the surrounding rock is the primary objective in the construction of an underground powerhouse at a hydropower station. Real-time monitoring, stability assessment, and evolutionary trend prediction of surrounding rock deformation and support structure stress are essential for maintaining rock mass stability. Using safety monitoring data and numerical simulation, the evolutionary behaviour of surrounding rock deformation and rock bolt stress during construction of the Kala Hydropower Station underground powerhouse was analysed. Surrounding rock stability and its future state were evaluated. Deformation in the first to third layers was mainly controlled by excavation disturbance and local geological conditions. The crown within the influence zone of the F152 fault exhibited the maximum deformation of 14.60 mm, whereas deformation in other areas was relatively small. Surrounding rock deformation in the cavern remained safe. Rock bolt stress showed spatio-temporal consistency with deformation, with maximum values concentrated in fault-cutting areas. The proportion of anchor bolts with stress below 200 MPa was 96.3%, indicating that the overall stress on the rock bolts in the cavern was in a safe state. Numerical simulation results predict that significant deformation during subsequent excavation and support will be concentrated between faults F152 and F75. The maximum surrounding rock deformation may occur in the fifth-layer sidewall affected by the F75 fault. Relatively high rock bolt stress is expected in the fifth to seventh layer sidewalls influenced by the F152 fault. This study identifies potential locations and development characteristics of stability deterioration during subsequent construction, providing guidance for construction design. The results serve as a reference for surrounding rock stability evaluation and prediction in similar underground powerhouse projects. Full article

The structural and geotechnical characteristics of railroad tracks change abruptly at transition zones. At these locations, a change from ‘rigid’ to ‘flexible’ track conditions or the opposite leads to amplified dynamic responses, large deformations, accelerated track deterioration, and increased maintenance expenses. Researchers have conducted numerous field and numerical studies into track transitions’ behavior; however, their investigations are often limited by point-based and short-term measurements and assumptions that overlook critical mechanisms in track transitions. This review presents current sensor-centric knowledge achieved by integrating insights from field instrumentations and numerical modellings of transition zones. The objective is to expose the overlooked behavioral aspects of track transitions and identify the limitations of conventional monitoring systems. To address these gaps, this review introduces optical fiber sensors (OFSs) as an emerging technology for track condition monitoring. Focusing on recent OFS applications, this study demonstrates how OFSs can improve the quantity and quality of field data through spatial continuity, multiplexing, and higher sensitivity, thus marking a significant practical improvement. This review also outlines OFS-based monitoring challenges, such as sensor durability, measurement quality, temperature-strain cross-sensitivity, and lack of a standardized data interpretation framework. Altogether, this work’s novelty is in connecting transition zone behavior, monitoring limitations, and the inherent potential of OFS systems. Full article

Epimedium pubescens, a shade-tolerant medicinal plant, currently faces supply shortages. To investigate the regulatory mechanisms of shading intensity on its growth and quality, this study established four treatments under a Phoebe zhennan plantation: inter-row artificial shading (0% shading, S-0; 50% shading, S-50; 75% shading, S-75) and natural canopy shading (S-93). When monitoring environmental factors, photosynthetic parameters, biomass, and total flavonol glycoside content, significant differences among treatments were only observed regarding solar radiation. Compared with inter-row treatments, S-93 reduced the maximum net photosynthetic rate and per-plant biomass by 33%–86% and 35%–71%, respectively. Structural equation modeling revealed that understory radiation indirectly influenced yield by regulating the vapor pressure deficit and net photosynthetic rate (R² = 0.95). Economic assessments, based on hectare-scaled yield (converted from plot units) and input costs (seedlings, land rental, labor), indicated that the 75% inter-row shading treatment applied from July to October (S-75) was optimal, generating a net annual income of 56,924.5 USD·ha⁻¹. This study provides a theoretical basis for the understory cultivation of E. pubescens. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article