Search Results (35,737)

Software-Defined Networking (SDN) provides fine-grained control over network architectures, yet integrating intrusion detection systems (IDSs) into the control plane frequently introduces prohibitive computational overhead. This issue is compounded by the fact that existing machine learning models, typically trained on static benchmark datasets, often degrade under real-time polling conditions and unpredictable traffic bursts. To bridge this gap, this paper evaluates an ultra-compact five-feature polling scheme (F1–F5) designed to preserve statistical symmetry between control-plane monitoring and telemetry overhead within a dynamic Mininet–Ryu testbed. The experimental framework incorporates 15% background noise, and a 10% stealth attack overlaps across a 120 s dynamic trace. Four distinct classifiers—Random Forest (RF), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were evaluated across frame-by-frame snapshot and windowed prediction tasks. Empirical findings reveal that tree-based ensembles consistently outperform deep learning approaches, with RF attaining an overall accuracy of 97.57% and DT achieving 96.74%, compared to 90.77% for MLP and 90.73% for LSTM. Analysis of the time-series logs demonstrates that RF’s orthogonal decision boundaries successfully isolate transient, high-intensity threats such as WebAttack and PortScan vectors without needing memory-intensive recurrent architectures. Ultimately, pairing minimal feature extraction with lightweight tree ensembles offers an optimal balance between low control-plane latency and high detection efficacy. Full article

Pathogenic fungi and their synergistic interactions with bark beetles, leading to vascular dysfunction, physiological stress, and eventual tree mortality, increasingly threaten forest ecosystems. Because fungal colonization often precedes visible macroscopic symptoms, early detection remains a critical yet challenging task in forest health monitoring. This study proposes a real-time deep learning-based object detection framework for identifying harmful fungi in proximity to host trees to support early intervention strategies. A custom dataset comprising 8900 images was constructed to represent two classes: Healthy and Unhealthy trees, where fungal presence is detected either directly on the tree or within its immediate ecological vicinity (e.g., near root systems). A fine-tuned YOLOv11 detection architecture is developed and augmented with a squeeze-and-excitation (SE)-like attention mechanism to enhance texture-sensitive feature representation. The model is trained and evaluated using precision, recall, F1-score and mean Average Precision (mAP). Experimental results demonstrate an overall mAP@0.5 of 0.825, with class-wise average precision values of 0.926 (Healthy) and 0.724 (Unhealthy). The Healthy class achieved classification accuracy of 0.92, while 0.71 of Unhealthy instances were correctly detected. F1-Confidence and recall-Confidence metrics indicate that optimal operational performance occurs within a confidence threshold range of 0.30–0.35, balancing false positives and false negatives. Despite the approximately balanced class distribution (50.6% Healthy and 49.4% Unhealthy), detection performance for the Unhealthy class was comparatively lower because of its greater intra-class variability, heterogeneous fungal appearance, and subtle visual manifestations. Findings demonstrate the feasibility of deploying real-time object detection models for early-stage fungal surveillance and highlight the importance of confidence calibration for operational disease monitoring systems. Full article

Aims: International diabetes guidelines recommend inpatient glycaemic management protocols for bolus enteral feeding in people with diabetes to improve clinical outcomes. This study aims to evaluate before and after hospital-wide implementation of an inpatient bolus enteral feeding protocol: (1) the incidence of hyperglycaemia (>13.9 mmol/L) and hypoglycaemia (<4.0 mmol/L), (2) medication prescribing practices and capillary blood glucose monitoring and (3) health care professionals’ knowledge and confidence levels. Methods: We implemented an inpatient glycaemic management protocol for bolus enteral feeding developed by a multidisciplinary team of diabetes nurse educators and endocrinologists and approved by the institutional medical board in July 2024. This before-and-after quality improvement study was conducted over 3 months across eight inpatient wards. Adult inpatients were consecutively enrolled if they met the inclusion criteria: (1) a documented diagnosis of diabetes mellitus, (2) receiving bolus enteral feeding and (3) treatment with glucose-lowering medication. Patients listed as critically ill were excluded. Nurses working in the pilot wards were also recruited. Before implementing the protocol, diabetes nurse educators trained inpatient nurses on understanding and executing the protocol for administering capillary blood glucose monitoring and medications for patients with diabetes on enteral feeding. Nurses’ pre- and post-knowledge levels and perceived confidence were assessed using a structured questionnaire. Electronic medical records were reviewed to evaluate the incidence rates of hypoglycaemia and hyperglycaemia before and during the 3 months following protocol implementation. We also assessed adherence to protocol-recommended capillary blood glucose monitoring frequencies based on the diabetes medication regimen. Results: A total of 31 patients were observed during the 6-week baseline period and 28 patients following protocol implementation. A total of 192 clinical care episodes were audited, comprising 78 in the pre-intervention phase and 114 in the post-intervention phase. The incidence of hyperglycaemia decreased from 43.6% to 10.5%, while hypoglycaemia decreased from 3.8% to 2.6%. After adjusting for protocol adoption rates, protocol implementation was associated with significantly lower odds of hyperglycaemia (odds ratio [OR] 0.22, 95% CI [0.07, 0.65], p = 0.006). A significant increase in appropriate nursing practices was observed post-intervention (p < 0.001). Adoption of the protocol by nurses decreased the odds of hyperglycaemia by 70% (p = 0.014). Nurses’ knowledge scores improved significantly from baseline to 3 months post-implementation (p < 0.001). Conclusions: Implementation of a standardised inpatient glycaemic management protocol for PWD receiving bolus enteral feeding was associated with reduced rates of hyperglycaemia and hypoglycaemia. Larger-scale studies are warranted to evaluate the effectiveness and sustainability of wider implementation in improving clinical outcomes. Full article

The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change, including more frequent and intense heatwaves, droughts, and floods. Consequently, reliable climate information and decision-support tools are essential for strengthening resilience and promoting sustainable management. To address these needs, the Portuguese Institute for the Sea and Atmosphere (IPMA) developed two complementary climate service platforms for mainland Portugal: AgroClima and DataClima. The first provides observations from IPMA’s meteorological network, ECMWF forecasts, and agroclimatic indicators such as temperature, precipitation, soil water, and so-called agroclimatic warnings. The second offers historical climate information including WRFv4.2 simulations dynamically downscaled from ERA5 (1981–present), in situ observations (1941–present), and climate normals. Evaluation of the WRFv4.2 regionalization against IPMA observations shows a systematic underestimation of precipitation and air temperature, while mean wind speed is generally overestimated. Despite these biases, the downscaled WRFv4.2 dataset demonstrates sufficient accuracy to support operational climate services, providing valuable help for environmental monitoring, climate adaptation, and decision-making in agriculture and water resource management across Portugal. Full article

Failures in cloud-native systems can disrupt service availability and system reliability, while their early symptoms are often weak and dispersed across metrics, logs, traces, and interdependent services. Existing methods commonly flatten heterogeneous telemetry or model monitoring variables without preserving service identities. We propose LTFP, a lead-time-aware failure-prediction framework whose graph nodes represent services. LTFP uses modality-specific temporal encoders and gated fusion to form service states, as well as an edge-weight-aware Graph Attention Network to propagate these states over a sparse hybrid graph constructed from known dependencies and training-fitted correlations. Joint graph-level and node-level heads predict whether a failure will occur within a configured future window and rank likely responsible services. We evaluate LTFP on seven subsets from three representative cloud-native systems. Comparisons with representative source-code baselines are reported at the pipeline level, with each method retaining its original learning objective and input configuration. At the 600 s prediction-window setting, LTFP obtains a macro-average window-level precision, recall, and F1 of 92.0%, 90.4%, and 90.5%, respectively. Together with the localization and ablation results, these findings support the effectiveness of service-centered multimodal modeling under the evaluated protocol. Full article

Asphalt pavements undergo progressive mechanical changes during service life due to traffic loading, temperature variations, moisture and material ageing. Embedded strain sensors can support in-situ pavement performance monitoring, but their response is strongly affected by experimental variables that must be identified before reliable long-term ageing indicators can be established. This study establishes an in-situ baseline mechanical monitoring framework for asphalt pavements using embedded resistive strain transducers. KM-100HAS sensors were installed in an asphalt test section and evaluated through controlled field campaigns. A 17-point cross-pattern loading procedure was used to validate sensor location and orientation after construction. Load-free monitoring windows were analysed to estimate strain–temperature sensitivity and assess thermal correction of static loading–recovery tests. The results showed that loading position strongly conditions the measured strain response. Passive monitoring indicated that strain–temperature sensitivity depends on both temperature level and sensor location. In the mechanical tests, normalization of the recovery branch and logarithmic fitting over the first 200 s provided a consistent recovery-shape descriptor. The resulting slope, $b_{\mathrm{l}\mathrm{o}\mathrm{g}200}$, showed a strong linear relationship with the recovery percentage after 10 min (R² = 0.855). The proposed workflow provides a standardized baseline protocol for asphalt pavement monitoring and its mechanical evolution. Full article

Accurate retrieval of ocean wave spectra from Synthetic Aperture Radar (SAR) images is important for understanding wave energy distribution and supporting large-scale ocean-wave monitoring. However, existing SAR-based wave retrieval methods often focus on scalar wave parameters and pay limited attention to the physical consistency of spectral energy reconstruction. In this study, we propose a physics-guided texture-enhanced Swin Transformer, named PGT-Swin, for retrieving one-dimensional wave frequency spectra from SAR images. The proposed method first constructs multi-channel SAR representations by combining intensity-enhanced images with Gray-Level Co-Occurrence Matrix (GLCM)-based texture features. A Swin Transformer backbone is then used to capture both local wave textures and global periodic structures. In addition, physics-guided spectral constraints are introduced to preserve total spectral energy and frequency-distribution consistency. Experiments were conducted using collocated Sentinel-1 SAR images and one-dimensional frequency spectra derived from CFOSAT SWIM products. The results show that PGT-Swin can effectively reconstruct wave spectra and derive reliable integral wave parameters. For SWH retrieval, the model achieves an MSE of 0.0014 and an R² of 0.9591. For MWP retrieval, it achieves an MSE of 0.0295 and an R² of 0.6659. These results demonstrate the effectiveness of PGT-Swin for SAR-based one-dimensional wave frequency-spectrum retrieval within the evaluated SWIM-referenced setting. Full article

Oil palm plantations play an important role in agricultural production and carbon storage in tropical regions. The accurate estimation of aboveground carbon (AGC) is essential for sustainable plantation management, climate change mitigation, and carbon monitoring. Although field measurements provide reliable estimates, they are often time-consuming, labor-intensive, and costly, particularly over large plantation areas. Recent advances in remote sensing and machine learning offer efficient alternatives for AGC estimation using satellite imagery. In this study, we developed a machine learning framework for AGC estimation in oil palm plantations using Sentinel-2 multispectral imagery and Sentinel-1 synthetic aperture radar (SAR) data. Field measurements were integrated with spectral variables, vegetation indices, and SAR-derived parameters extracted from satellite data. A hybrid feature selection approach combining Pearson correlation, mutual information and mRMR was used to identify the most relevant variables. Six machine learning algorithms were evaluated, including Linear Regression, Random Forest, XGBoost, Gradient Boosting, LightGBM, and Extra Trees. Because the 160 observations comprise sixteen 10 m × 10 m grid cells nested within ten 40 m × 40 m field plots, model performance was assessed with leave-one-plot-out cross-validation: all sixteen cells of a plot were held out together, and the hybrid feature selection was repeated inside every fold using only that fold’s training plots. Performance was measured on pooled out-of-fold predictions using R², root mean squared error (RMSE), and average absolute relative error (AARE%). Under this spatially independent design the combined Sentinel-1 + Sentinel-2 dataset gave the highest accuracy (R² = 0.7950, RMSE = 4.14 t C ha⁻¹, AARE = 33.53%), followed by Sentinel-1 alone (R² = 0.7631, RMSE = 4.46 t C ha⁻¹) and Sentinel-2 alone (R² = 0.6108, RMSE = 5.71 t C ha⁻¹). Linear Regression and Extra Trees were the most robust models, whereas the boosted ensembles did not generalize to unseen plots. Repeating the evaluation with an ungrouped random split of the same data inflated R² by up to 0.70, showing that a large part of the accuracy obtainable under that design reflects within-plot spatial autocorrelation rather than predictive skill. These findings indicate that optical-SAR imagery combined with machine learning can provide useful AGC estimates in oil palm plantations, and that spatially independent validation is essential for reporting them honestly. The proposed framework can be used to support plantation-scale carbon mapping, monitoring, and carbon stock assessment, subject to further calibration and independent validation across additional plantations. Full article

Coffee is deeply embedded in Saudi culture and is among the most consumed beverages worldwide. However, contamination by toxic heavy metals may represent an underrecognized public health concern, particularly in regions already burdened by diabetes mellitus and cardiovascular disease. This study evaluated concentrations of lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), and nickel (Ni) in 21 commercial coffee samples representing seven product categories marketed in Saudi Arabia. Samples were prepared using EPA Method 3050B acid digestion and analyzed by inductively coupled plasma mass spectrometry (ICP-MS). Health risks were assessed through estimated daily intake (EDI), hazard quotient (HQ), hazard index (HI), and incremental lifetime cancer risk (ILCR) using USEPA exposure parameters and Saudi-specific coffee consumption data. All metals were detected in every sample. Mean concentrations (mg/kg dry weight) were 1.638 for Pb, 0.560 for Cd, 0.622 for Cr, 0.536 for Ni, and 0.034 for As. Cadmium exceeded the European Union maximum limit for roasted coffee in all samples, while elevated Pb levels were observed in dark roast and Yemeni coffees. Although the overall non-carcinogenic risk remained below the accepted threshold (HI = 0.438), the combined ILCR for As, Cd, and Pb was 5.84 × 10⁻⁵. Inclusion of a conservative screening-level chromium contribution, calculated by assuming that all measured total Cr was present as Cr(VI), increased the upper-bound total ILCR to 9.38 × 10⁻⁵. This value remained within, but approached the upper boundary of, the risk-management range applied in this study. These findings highlight the need for strengthened monitoring and regulatory control of coffee products in Saudi Arabia. Full article

Accurate semantic segmentation is essential for long-term fixed-view monitoring, where seasonal, illumination, weather, and environmental changes alter local appearance while the global scene layout remains relatively stable. To address this structure–appearance modeling problem, we propose SPMES, a Semantic Prior-Guided Period-Aware Multi-Expert Segmentation framework. SPMES comprises a Global Expert that learns stable semantic-prior maps from the complete training set, a Semantic-Guided Fusion Module that injects these priors into the input representation, and period-specific experts that model recurring appearance characteristics according to acquisition time. Experiments on a long-term fixed-view monitoring dataset collected for this study evaluate SPMES with U-Net, U-Net++, U²-Net^†, Swin-Unet, and Mamba-UNet. Compared with the corresponding baselines, SPMES obtains better point estimates across all six evaluation metrics for each backbone, although the magnitude of the changes varies across architectures and metrics. Ablation studies show that individual configurations exhibit metric- and backbone-dependent effects, whereas the complete integration of global semantic priors, semantic-guided fusion, and period-specific learning provides the best overall balance. These results support the effectiveness and backbone-level compatibility of SPMES within the studied monitoring setting. Full article

Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses. Full article

Wide-area monitoring and accurate assessment of potential geohazards (PGHs) based on remote sensing will provide a crucial foundation for geohazard prevention and mitigation. Current remote sensing methods for PGH identification and evaluation require extensive manual effort and lack intelligence throughout the process. To effectively integrate multisource remote sensing data, we propose an automated method for identifying and assessing PGHs across a wide area. This approach integrates InSAR deformation, high-resolution optical remote sensing, terrain, and vector data of ground features to enable automated delineation of unstable zones, automatic identification of potentially threatened objects (PTOs), automatic screening of PGHs, and risk assessment. The proposed method is tested in the Hequ–Baode–Pianguan (HBP) region of Shanxi province. Using the DS-InSAR technique, we process 94 Sentinel-1 SAR images covering the HBP region from 2020 to 2024 to estimate surface stability. We automatically detect the boundaries of 161 active deformation areas (ADAs) in HBP. A deep learning model based on DeepLabV3+ processes optical remote sensing images of the study area at 0.5 m resolution to automatically identify all PTOs. By integrating terrain data and spatial relationships among PTOs and ADAs, we develop an algorithmic model to identify 90 PGHs and classify them into external-threat, internal-threat, and internal-external-threat geohazard zones. Finally, a risk matrix is created for an automatic geohazard risk assessment, producing results for all PGHs in the study area. This developed method will support wide-area screening and prioritization of potential geohazards on the Loess Plateau and improve PGH investigation capabilities. Full article

Accurate and efficient rice pest detection is paramount for ensuring food security and enhancing agricultural production efficiency. Traditional manual pest monitoring methods fall short of meeting the precision and efficiency demands of modern agriculture. To address the challenge of deploying high-precision object detection models on resource-constrained edge devices, we propose an efficient, lightweight rice pest detection model, termed PestEdgeFeatureFusion-Net (PEFF-Net), and implement a comprehensive edge-side offline intelligent monitoring system. PEFF-Net integrates Edge Feature Extraction Stem (EFStem), the Edge Semantic Fusion Module (ESF), and the Lightweight Cross-layer Feature Fusion Output Module (LCFO). By streamlining deep feature maps and strengthening edge feature perception, the model significantly reduces parameter overhead while enhancing multi-scale feature fusion capabilities. Experimental results demonstrate that on the Z-RP12 dataset containing 5000 images, PEFF-Net has 2.12 M parameters and achieves a ${\mathrm{m}\mathrm{A}\mathrm{P}}_{0.5}$ of 90.6%, providing a favorable balance between detection accuracy and model compactness. We employ the Jetson Orin Nano Super 8 GB as the core hardware platform and leverage TensorRT for INT8 quantization acceleration. The optimized model achieves 31 FPS with a mean latency of approximately 32.1 ms on the Jetson Orin Nano Super 8 GB. An independent cross-camera field evaluation further supports the feasibility of the proposed edge-side detection system under the tested conditions. Full article

Confronted with severe global challenges, including water scarcity, excessive use of chemical fertilizers and pesticides, and heavy metal contamination in soils, conventional agricultural technologies exhibit marked limitations in integrated water–fertilizer management and non-point source pollution control. Leveraging their excellent water retention capacity, intelligent sustained-release properties, and environmental responsiveness, hydrogels offer innovative solutions to advance sustainable agricultural development. This review comprehensively outlines the fundamental types, crosslinking mechanisms, and key functional properties of hydrogels, with a focused discussion on their agricultural deployment as high-efficiency soil conditioners, fertilizer vectors, and pesticide carriers; it deciphers the microscopic water-holding mechanisms under the tristate water model, delineates the divergent water-uptake and retention behaviors between ionic and non-ionic hydrogels, and clarifies the cyclic water-holding and release mechanisms of hydrogels during soil amelioration. Thise paper further synthesizes hydrogel-enabled environmental remediation applications, in which heavy metals and pesticide residues in soils and aquatic systems are removed via functional-group coordination adsorption or photocatalytic degradation; concurrently, hydrogels have been shown to activate plant systemic immunity through calcium-signaling pathways, thereby inducing broad-spectrum antiviral defense responses. Moreover, hydrogels can be integrated into precision agriculture frameworks to enable real-time monitoring of crop physiological status and to support targeted irrigation and fertilization management. This work also evaluates the role of hydrogels in promoting seed germination, root system development, crop metabolic regulation, and stress resilience, while introducing tailored application strategies across distinct plant growth stages. Their documented economic advantages include water conservation, enhanced crop yields, reduced dependence on synthetic fertilizers, and lower labor costs. Nevertheless, the large-scale implementation of hydrogels continues to face multifaceted challenges—particularly poor degradability and latent ecological risks, as conventional polyacrylamide (PAM)-based gels resist soil mineralization and retain potentially neurotoxic monomers, leaving a critical gap in multi-annual field data concerning their non-target interference with native soil aggregate evolution, pore distribution, and rhizospheric carbon–nitrogen footprints. Mechanistically, many hydrogels with tensile strengths below 1 MPa are highly susceptible to three-dimensional network collapse under high-salinity osmotic shock and tillage mechanical stress, exhibiting a precipitous drop in water retention after more than three wet–dry cycles due to deficient long-term structural stability. Compounding these technical gaps are elevated production costs and low farmer adoption, driven by the absence of texture-specific performance thresholds—such as an available water increment ≥ 40% for sandy soils—and the lack of established life-cycle cost models and farmer incentive mechanisms for bio-based hydrogels. Moving forward, hydrogel technology should pivot toward materials innovation and cost-reduction engineering to broaden its applicability, employ ≥3-year, multi-habitat regional trials to delineate ecological benefit–risk boundaries, and ultimately position hydrogels as pivotal enablers of sustainable, green agricultural paradigms. Full article

This study examines the agricultural energy transition in 25 European Union Member States over 2005–2024, focusing on energy intensity, fossil-fuel dependence, and carbon intensity. It integrates multi-regional Kaya–LMDI decomposition, Tapio decoupling analysis, and σ- and conditional β-convergence models to identify the components associated with changes in direct energy-related agricultural CO₂ emissions, assess their relationship with real agricultural gross value added, and determine whether national performance gaps are narrowing. The results show that declining energy intensity and fossil dependence were the main emissions-reducing components, while changes in country structure and carbon intensity partly offset these gains. Within the EU-25 analytical sample, the baseline comparison indicates a shift from recessive decoupling before 2020 to strong decoupling over 2020–2024. Sensitivity tests confirm the robustness of post-2020 strong decoupling, although the characterization of the pre-2020 period is sensitive to sample composition. Conditional β-convergence is observed for all three indicators, whereas σ-convergence is found only for energy intensity; fossil dependence and carbon intensity continue to exhibit substantial cross-country dispersion. Bias-corrected estimates preserve the direction of conditional catch-up but indicate slower adjustment, particularly for fossil dependence. The study’s novelty lies in jointly evaluating emissions decomposition, decoupling, and distributional change while distinguishing relative catch-up towards country-specific trajectories from convergence towards a common EU level. The findings identify energy efficiency, fossil-energy substitution, and differentiated national conditions as relevant areas for CAP-related monitoring and policy consideration. The conclusions concern specifically the energy-related component of agricultural decarbonization. Full article