Search Results (9,374)

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Sweet cherry cracking severely impairs its commercial value and causes huge economic losses, and the accurate real-time detection of fine cracking defects remains a challenging small-target detection task. Traditional manual sorting and conventional machine vision methods suffer from low efficiency and poor robustness, while existing YOLO-based models have limitations in multi-scale feature fusion, local feature discrimination and spatial information retention for cherry cracking detection, and their effectiveness in natural production environments has not been statistically validated. To address these issues, this study proposes YOLO-CY for cherry cracking defect detection. Three key modules were optimized: the C3k2_AdditiveBlock was designed to enhance multi-scale feature extraction, the C2PSA_CGLU module improved the discriminability of local crack features via refined channel attention, and the Efficient Up-Convolution Block replaced traditional upsampling to reduce spatial information loss. Experiments were conducted on a self-constructed dataset of 3662 cherry images acquired on a real sorting line under natural ambient light. The results showed that YOLO-CY achieved an mAP50 of 94.88% and an mAP50-95 of 64.92%, with precision and recall reaching 93.90% and 90.81%, respectively, significantly outperforming mainstream lightweight YOLO models and two-stage detectors. Ablation experiments verified the synergistic effect of the three improved modules, and the model only had a marginal increase in parameters (2.62 M) and GFLOPs (6.60), maintaining lightweight characteristics. YOLO-CY can accurately detect fine, low-contrast and pedicel-overlapping cracks and is suitable for real-time detection on automated cherry-sorting lines, providing a technical solution for intelligent cherry quality inspection. Full article

Monitoring marine organisms plays a vital role in biodiversity conservation, marine environmental management, and fisheries resource management. However, the underwater environment is often low-light and turbid, leading to indistinct target boundaries. Moreover, the wide variety of marine organisms—with significant differences in color, scale, texture, and morphology—can easily result in missed detections. To address these challenges, this paper proposes a multi-class marine organism detection method using multi-scale attention-enhanced You Only Look Once 11 nano (YOLO11n). The method incorporates the Convolutional Block Attention Module (CBAM) into the YOLO11n network, enabling the model to better focus on key feature regions while effectively suppressing background noise interference in complex marine environments. In addition, the model is trained using the Complete Intersection over Union (CIoU) loss function, which enhances bounding box regression accuracy, especially in handling targets of varying scales. The effectiveness of the proposed method is validated on the publicly available BrackishMOT dataset. The proposed model achieves an overall mAP@0.5 of 0.481, computed as the average AP across six organism categories. Category-wise results indicate stronger performance on visually distinguishable targets, such as Jellyfish, Starfish, and Small fish, with AP values of 0.808, 0.678, and 0.677, respectively. In contrast, performance remains limited for rare or visually ambiguous categories. These results suggest that the proposed method is effective for multi-class marine organism detection, particularly when discriminative visual features are present. Full article

In this study, we report the synthesis and characterization of three Cu(I) complexes bearing functionalized dipyridylamine ligands and DPEphos. Structural analysis confirms a distorted tetrahedral coordination environment around the metal center. Photophysical studies in DMSO show similar absorption profiles (λ_abs ≈ 341–343 nm) with ligand-centered and MLCT transitions, while emission spans the visible region (λ_emi = 410–483 nm) and is strongly influenced by ligand substitution, with the CF₃ derivative displaying a marked red shift. Emission is insensitive to oxygen and exhibits short lifetimes (τ ≈ 14.9–15.3 ns), suggesting short-lived ¹MLCT excited states. Biological evaluation in A375 melanoma cells reveals that all complexes exhibit low-micromolar cytotoxicity under dark conditions (IC₅₀ = 3.33–4.92 μM). Notably, only the CF₃-substituted complex shows a significant light-induced enhancement of activity upon irradiation at 390 nm (IC₅₀ = 1.18 μM), indicating photoactivation. Full article

Concrete crack segmentation in bridge health monitoring is crucial for ensuring the safety and longevity of infrastructure. However, this task is complicated by challenges such as weak contrast, background interference, and multi-scale crack structures, which hinder traditional methods’ accuracy. This study introduces a novel Boundary-Sensitive Hybrid Attention Network (BSA-Net) designed to address these issues by combining a hierarchical Transformer encoder (Hiera-A), a multi-scale context module (Light-ASPP), and a boundary-aware decoder (BAD). The hierarchical encoder effectively captures multi-scale features, while Light-ASPP enhances the network’s ability to aggregate contextual information with minimal computational cost, making it suitable for large-scale applications. The dual-branch decoder explicitly decouples the learning of semantic segmentation and boundary prediction, ensuring more accurate boundary detection and crack continuity. The extensive experiments on multiple benchmark datasets demonstrate that BSA-Net consistently outperforms existing crack detection models, particularly in complex, noisy environments. The model achieves competitive performance in terms of segmentation accuracy, boundary clarity, and recall rates, particularly for fine-scale and weak contrast cracks. The results indicate that BSA-Net not only enhances the performance of crack segmentation in real-world conditions but also provides a scalable and reliable solution for automated infrastructure monitoring and defect detection. Full article

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments remains a significant challenge, as targets are frequently obscured by high-frequency wave clutter, and feature distributions are destabilized by covariate shifts caused by illumination. To address these limitations, this study proposes YOLO11-MCN, a real-time detection framework that integrates two architectural components specifically designed for water-surface monitoring. The Multi-Scale Contextual Attention (MSCA) module distinguishes target signatures from background noise by aggregating contextual information across heterogeneous receptive fields, thereby suppressing false positives generated by waves. The Channel Normalization Attention Mechanism (CNAM) addresses illumination instability through feature statistic calibration based on Group Normalization, effectively mitigating covariate shifts induced by extreme lighting variations. Furthermore, these components are complemented by a high-resolution P2 detection head, which recovers the geometric details of small-scale targets typically lost during downsampling. Extensive experiments conducted on a dataset of 5812 images demonstrate that YOLO11-MCN achieves an mAP@0.5 of 92.7%, outperforming the YOLO11n baseline by 5.9 percentage points. Robustness evaluations confirm that MSCA and CNAM significantly reduce missed detections under severe wave clutter and backlighting conditions. With a recall of 90.5%, an inference speed of 94 FPS on desktop hardware, and a compact footprint of 3.89M parameters and 14.8 GFLOPs, the proposed framework offers a robust and efficient solution for intelligent water-surface surveillance systems within the single-class detection paradigm evaluated in this study, with strong potential for edge-device deployment following platform-specific optimization. Full article

The optical complexity of shallow Case 2 waters challenges remote sensing accuracy due to the non-linear behaviour of optically active constituents. This study evaluates the spectral divergence between the target-derived effective attenuation ($K_{\mathrm{e}\mathrm{f}\mathrm{f}}$) and the ambient scalar attenuation coefficient ($K_0$) across 12 Baltic Sea locations. Using hyperspectral radiometry and K-Means clustering, three optical water types (OWTs) were identified. We demonstrate that the historical static approximation based on the diffuse attenuation coefficient ($K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ ≈ 2$K_{\mathrm{d}}$) is systematically biased in scattering-dominated environments. Our empirical results yielded a regional relationship of $K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ = 2.33$K_0$ ($R^2$ = 0.65); however, residual analysis reveals that linear multipliers fail to capture non-linear light decay. Random Forest regression identified total suspended matter (TSM) as the primary driver of $K_{\mathrm{e}\mathrm{f}\mathrm{f}}$ variance (28.0%), confirming that “geometric rejection” of scattered photons artificially inflates signal loss in turbid waters. This divergence is most pronounced in the 500–650 nm range, where low absorption facilitates multiple scattering events. We conclude that active remote sensing requires a sensor-fusion approach, utilising passive OWT classification to dynamically parameterise active attenuation models. Full article

Due to dust, haze and uneven lighting conditions, images captured in coal mines frequently suffer severe quality degradation. Traditional dehazing methods typically overlook color characteristics and employ single algorithms, and deep-learning-based approaches require substantial training data and demand high hardware specifications, which restricts their dehazing performance and efficiency. This research proposes an efficient image dehazing framework. This method integrates bright and dark channel information to derive contrast feature values based on their linear differences. These values reflect dust concentration levels in the environment. By incorporating dust sensor data, the adaptive scaling coefficient and dust compensation terms are established. The adaptive scaling coefficient serves as a dynamic pixel selection ratio during ambient light estimation, effectively preserving the brightest pixel points. The global color mean functions as the criterion for determining image color characteristics, distinguishing between color images and low-light grayscale images to enable different dehazing approaches. This process achieves state verification and information complementarity between visual perception and dust measurement. The weighted fusion of bright and dark channels yields more accurate estimation for ambient light and transmission. Additionally, a weighted guided filter is designed with dust compensation terms incorporated. Ablation studies were conducted to validate the effectiveness of this method in enhancing image features. Finally, comparative experiments were performed using a self-constructed coal mine hazy image dataset, along with SOTS-indoor and SOTS-outdoor datasets. Experimental results demonstrate that, compared with other state-of-the-art methods, this method effectively removes haze while restoring image features and details, exhibiting superior stability, adaptability, and computational efficiency. Full article

Optimizing plant density and vegetative growth duration is important for improving productivity in controlled-environment medicinal cannabis cultivation. Although both factors strongly influence canopy development and yield, their combined effects under modern high-intensity LED lighting, and particularly their consequences for cannabinoid uniformity across the canopy, remain insufficiently characterized. This study examined how planting density and vegetative duration influence plant growth, yield, and cannabinoid concentration in Cannabis sativa L. (strain ‘Fat Banana’) grown under controlled environment conditions, high-intensity LED lighting and precision fertigation. Two vegetative durations (10 and 28 days) were evaluated in separate but identical controlled-environment chambers under broad-spectrum high-intensity LED lighting and automated precision fertigation on rockwool substrate. The 10-day regime compared 8, 14 and 18 plants m⁻²; the 28-day regime compared 6, 8 and 10 plants m⁻². Each combination was replicated across two independent cultivation cycles, and because density levels differed between regimes, direct between-regime comparisons were restricted to the shared density of 8 plants m⁻². Extending the vegetative phase from 10 to 28 days increased plant height, stem diameter and internodal length. Area-based yield increased strongly with density, reaching 1091 g m⁻² at 18 plants m⁻² under the 10-day regime and 1009 g m⁻² at 10 plants m⁻² under the 28-day regime. Apical biomass exceeded basal biomass, but total THC concentration did not differ significantly among planting densities, vegetative durations or canopy positions. Higher planting densities combined with shorter vegetative periods can therefore increase area-based productivity while maintaining stable THC concentration under high-intensity LED cultivation. Full article

The Automatic Identification System (AIS) generates massive volumes of real-world ship trajectory data, providing a critical foundation for maritime ship-type classification. However, existing methods often struggle to simultaneously capture long-range temporal dependencies, maintain computational efficiency, and ensure model interpretability, making accurate multi-class classification challenging in real-world maritime environments. To address these limitations, this study proposes a robust and efficient hybrid framework that integrates a Feature Transformer module for deep temporal feature extraction with a LightGBM model for ensemble classification. The multi-head self-attention within the Feature Transformer captures long-range dependencies in preprocessed AIS sequences to generate compact 64-dimensional trajectory fingerprints. These deep representations are concatenated with 103 carefully designed kinematic, geometric, statistical, frequency-domain, and segment-level features and fed into a LightGBM classifier for final ship-type identification. We evaluate the framework on a real-world AIS dataset of 2196 trajectories collected between 2019 and 2023, covering 14 ship types under a natural long-tail distribution. Across five random seeds, the proposed hybrid model achieves 78.06% ± 1.15% accuracy (95% CI) and 74.09% ± 1.82% Macro-F1 (95% CI), significantly outperforming Transformer-only (65.09% accuracy) and LightGBM-only (66.85%) baselines, with paired statistical tests confirming the improvement (McNemar χ² = 172.07, p < 10⁻³⁹ vs. Transformer; χ² = 92.24, p < 10⁻²¹ vs. LightGBM). The hybrid model offers ultra-fast inference at 0.051 ms per trajectory on GPU at batch size 128 (≈19,500 samples/s), and provides instance-level interpretability via SHapley Additive exPlanations (SHAP) analysis. These properties make the framework practical for near-real-time maritime traffic monitoring and decision support. Full article

Underground coal mine environments commonly suffer from insufficient illumination, high dust concentrations, and cluttered backgrounds, which substantially degrade the accuracy of conventional object detection algorithms. To address these issues, this paper proposes LLE-YOLO, a detection network built upon YOLOv11n. At the input stage, an Adaptive Low-Light Enhancement Module (ALEM) is introduced, which integrates Retinex decomposition, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and brightness-dependent Gamma mapping to dynamically select the optimal enhancement strategy according to the global luminance. Furthermore, a Degradation-Aware Efficient Multi-Scale Attention (DEMA) module is proposed, which incorporates Contrast-Aware Modulation (CAM), an asymmetric dilated convolution group, and a Degradation-aware Spatial Gate (DSG) into the EMA channel-grouping and cross-spatial learning framework, thereby strengthening multi-scale personnel detection while keeping the parameter count tractable. On the publicly available DsDPM66 dataset, which covers 66 coal mine sites and 105,096 annotated images, LLE-YOLO achieves an mAP@0.5 of 83.7%, representing gains of 8.1 percentage points over YOLOv11n and 5.2 percentage points over the GCB-YOLOv11 baseline, while the recall increases from 71.2% to 78.2%. Under extremely dark scenarios (<30 lux), the mAP@0.5 is further improved by 15.3 percentage points. Ablation studies and Grad-CAM visualizations confirm the contribution of each module, offering a practical engineering reference for intelligent underground monitoring systems. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

A substantial proportion of construction accidents is associated with unsafe worker behavior. Identifying their underlying mechanism is vital for designing effective interventions. As prior studies could not capture complex nonlinear interactions among organizational and individual factors, this study leverages machine learning (ML) techniques, which can capture complex relationships by handling large datasets, and can identify patterns in worker behavior. The study proposes an explainable ML model to interpret key determinants of safe behavior. The data were collected from 425 construction workers in Saudi Arabia. Multiple ensemble and benchmark ML algorithms—including random forest (RF), categorical boosting, decision jungle, light gradient boosting machine, support vector machine, and adaptive boosting—were implemented and compared. The results indicate that the RF model achieved the best predictive performance, outperforming several competing models. To enhance the model’s interpretability, explainable artificial intelligence (XAI) techniques were applied to reveal the interaction of key predictors influencing workers’ behaviors. The results demonstrate that safety communication, risk perception, and supportive work environment are the most influential determinants shaping safety behavior. As a key novelty, this study introduces an ML-based approach for predicting construction workers’ safety behavior and applies XAI techniques to systematically interpret the key determinants of safety behavior. The results also provide valuable insights for safety managers and offer data-driven guidance to enhance the effectiveness of safety interventions. Full article

Background: Children and teenagers use electronic devices daily, especially smartphones. The use of these devices exposes children and adolescents to excess blue light, which can alter the structures of the eye, especially the retina. As a protective mechanism, the macular region contains pigments represented by lutein, zeaxanthin, and meso-zeaxanthin. In this study, we aimed to analyze the relationship between the Macular Pigment Optical Density (MPOD) levels in the macula and the time spent on smartphones in children and adolescents. Methods: Fifty-seven children and teenagers aged between 8 and 18 were evaluated, with a total of 114 eyes. The patients included in the study were divided into two groups: those who spent less than two hours a day on the device and those who exceeded this period. To determine the amount of macular pigment, the Heterochromatic Flicker Photometry technique was used. Results: We found a statistically significant difference in screen time between weekdays and weekends in favor of the latter. We compared the different refractive categories with respect to pigment levels and screen time and found no significant differences between groups. When comparing the patients with respect to environment, we found a slight difference in macular pigmentation in the favor of rural areas and also in the screen time which was shorter in rural areas. We found a strong association at all levels between longer screen time (both weekdays and weekend) and lower macular pigment quantities for both eyes. When comparing the groups with more/less than 2 h of screen time, the MPOD was lower for both eyes in the group with over 2 h screen time. Conclusions: In this study we demonstrated that smartphone use is a risk factor leading to a decrease in MPOD in children and adolescents. The amount of lutein in the retina, brain and serum are correlated, therefore MPOD can be considered a natural biomarker of lutein and zeaxanthin levels in the body. Full article