Search Results (12,325)

Developing earth-abundant photocatalysts that operate efficiently under visible light remains a central challenge in sustainable aerobic oxidation chemistry. We synthesized a binuclear cobalt(II) structure (Co₂) in which two redox-active metal centers are bridged by a polypyridine scaffold to integrate light-harvesting and catalytic functions within a single low-nuclearity unit. The complex exhibits a strong absorption band below 450 nm, undergoes facile charge separation upon photoexcitation, and channels molecular oxygen (O₂) toward superoxide radical anion (O₂^•–) under blue-light irradiation. Spectroscopic and mechanistic studies indicate that the polypyridine framework governs photon capture and excited-state delocalization, whereas the proximal Co(II) sites mediate the subsequent single-electron transfer to O₂. Driven by this dual-site synergy, Co₂ selectively oxidizes a broad scope of thioethers to the corresponding sulfoxides in yields exceeding 95%, with no over-oxidation to sulfones detected. The catalyst retains its structural integrity over five successive runs without measurable activity loss. By confining complementary photophysical and redox functions within a discrete bimetallic unit, this work establishes a design strategy for noble-metal-free, visible-light-driven organic transformations. Full article

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives criteria weights, the Analytic Hierarchy Process (AHP) ratings evaluate alternatives, and a risk assessment stage consolidates the final ranking. The primary insights of this research include: (i) the development of a structured supplier evaluation model that encompasses dimensions like closed-loop integration, end-of-life management, material efficiency, and waste management into a multi-criteria perspective; (ii) applying BWM to derive consistent criteria weights, clarifying how circular performance attributes shape supplier prioritization; (iii) applying AHP ratings and risk assessment to consolidate the evaluation into a final ranking of alternatives; and (iv) demonstrating the operational feasibility and applicability of the framework through a real-world case analysis, providing empirical evidence for assessing circular supplier performance in industrial environments. Full article

Driving simulators have become essential tools for accelerating the development of advanced driver assistance systems (ADASs) and autonomous vehicles (AVs) by enabling safe, repeatable, flexible, and cost-effective experimentation across increasing levels of vehicle automation. Despite their growing adoption in both academia and industry, the recent literature lacks a comprehensive review that captures recent advancements and the expanding role of simulators in both feature-level ADAS development and fully autonomous driving research. This paper addresses this gap by presenting a systematic review of the evolution of driving simulators and their critical contributions to automotive research, testing, and validation. A structured taxonomy of contemporary simulators is introduced, encompassing fidelity, physical configuration, scale, licensing, and system integration strategies. Key application domains are examined, including driver-centred behaviour and human–machine interaction studies, traffic modelling and control, vehicle dynamics and powertrain development, and the testing of ADAS and autonomous driving subsystems across perception, planning, control, and vehicle-to-everything (V2X) communication. This review highlights driving simulators as foundational enablers for the safe, efficient, and scalable deployment of increasingly automated vehicle technologies. Full article

Forest ecological product value realization (FEPVR) seeks to convert forest ecosystem services into identifiable, accountable, compensable, tradable, and financeable value returns through institutional and market arrangements. Existing studies have mainly emphasized aggregate evaluation or conversion efficiency, with less attention to the structural relationship between ecological supply capacity and value-capture capacity. This study develops a dual-dimensional framework of use-value realization (UVR) and exchange-value realization (EVR), and constructs a city-level panel of 147 policy-practice sample cities (prefecture-level and above) in China over 2019–2023. An entropy-weighted composite index and an entropy-weighted TOPSIS model are applied to measure FEPVR mechanism development, structural configurations, and relative closeness to the sample-defined ideal state. The results show that the mean composite score increased from 0.194 in 2019 to 0.337 in 2023, while the coefficient of variation declined from 0.561 to 0.398, indicating overall improvement and narrowing intercity disparities. Global Moran’s I remains positive and significant throughout the study period, indicating significant and positive spatial autocorrelation in FEPVR mechanism development. The UVR–EVR decomposition reveals substantial structural divergence: the HH, HL, LH, and LL configurations include 29, 33, 25, and 60 cities, respectively. TOPSIS results further show that EVR relative closeness increased markedly, whereas UVR relative closeness declined slightly, indicating that institutional and market-based value-capture capacity expanded faster than the ecological supply base. Robustness checks suggest that the rise in EVR is strongly associated with institutional-entry indicators, and should therefore be interpreted as the expansion of value-capture instruments rather than direct evidence of realized market performance or ecological improvement. The findings provide a descriptive evaluation of FEPVR mechanism development in cities with documented policy or practice foundations, and should not be generalized as the average condition of all Chinese cities. Full article

The transition towards sustainable aluminum manufacturing is commonly assessed through recycling rates, energy savings, and resource efficiency, but its distributive effects across global value chains remain insufficiently examined. This study evaluates whether recycled aluminum value chains contribute to both circularity and north–south redistribution, or whether they reproduce unequal patterns of value capture, industrial upgrading, employment quality, and trade dependency. The analysis combines UN Comtrade trade data for HS 7601–7616, OECD ICIO 2025 value added indicators, ILOSTAT labor statistics, and UN SDG data for the 2018–2020 three-year average. Eighty economies are classified into four groups: advanced industrial economies, emerging industrial economies, lower-middle-income economies, and low-income economies. A composite indicator linked to SDGs 8, 9, 10, and 12, with SDG 17 incorporated only as a trade dependency context, is constructed from normalized industrial, circular material flow, distributive, and job-quality variables. The results show a clear north–south hierarchy: advanced economies concentrate a larger share of exports in aluminum manufactures, while low-income economies remain more dependent on scrap flows. Group A captures most chain value added, whereas Groups C and D retain only marginal shares. Labor productivity falls sharply from advanced to low-income economies, while working poverty increases substantially. By contrast, circularity scores vary less strongly across groups, suggesting that participation in circular material flows does not necessarily imply equitable industrial upgrading. This study shows that circularity in recycled aluminum value chains does not automatically generate redistribution and provides a replicable framework for distinguishing material circularity from distributive justice. Full article

Image segmentation in complex scenes remains challenging due to intensity inhomogeneity, intricate textures, and noise interference. Traditional active contour models (ACMs) offer topological adaptability while suffering from over-segmentation and boundary leakage under such conditions. In this paper, we propose MFJD-Seg, a novel ACM that integrates morphological fitting with an energy formulation derived from Jeffreys divergence for robust and efficient image segmentation. Morphological erosion and dilation are applied to construct foreground and background fitting images, which capture fine-grained structural features while suppressing background interference. Subsequently, a symmetric discrepancy consistent with Jeffreys divergence is leveraged to quantify the statistical difference between the original image and the fitting representations, enabling the compact construction of an unbiased energy function. An arctangent energy constraint and mean filtering are further incorporated to stabilize contour evolution and suppress redundant artifacts. Extensive experiments on BSDS, ADE20K, and COCO datasets show that MFJD-Seg achieves the best mIoU and mDSC in comparisons with five representative ACMs and five mainstream deep learning segmentation models, improving ACM baselines by up to 4.8% in both metrics while maintaining the highest FPS among ACMs and competitive speed against deep learning counterparts. These results verify the superior segmentation capabilities of MFJD-Seg in challenging imaging scenarios. Full article

The preparation of platelet-rich plasma (PRP) requires precise density-based centrifugation of anticoagulated whole blood to achieve an optimal hematologic bioformulation while enhancing platelet recovery efficiency. Commercial PRP systems exhibit substantial heterogeneity in processing architecture, with variable platelet yields and inconsistent cellular composition profiles. In this clinical PRP device evaluation, 70 sequential samples sourced from two independent clinical facilities were used to evaluate the performance characteristics of the XCELL 60 mL single-spin centrifugation platform. Two different PRP preparations were consistently prepared as per physician preferences: PRP-1 and PRP-2. The main differences between these two preparations were the concentration of leukocytes and reduction in red blood cells. The system was evaluated based on critical PRP performance metrics. The results demonstrated the following: (1) A consistent 8-fold increase in platelet concentration relative to baseline whole blood was achieved. (2) The average platelet capture rate (PCR) was 83%. (3) The total available platelets (TAPs) in the PRP specimen produced from both groups combined were 10.8 ± 2595 billion platelets within a final product volume of 6 mL. (4) Hematocrit values were reduced to <2–6% across sites (reduction of 94% and 84% in RBCs, respectively). Finally, (5) a customizable leukocyte content (20.9–25.4 × 10⁹/L) was achieved without comprising platelet yield. This single-spin centrifugation architecture achieved performance parity with historically preferred double-spin systems while reducing the processing time and number of preparation steps. Engineering analysis established that high-precision platelet recovery and bioformulation control are achievable through optimized single-spin centrifugal design, enabling standardized therapeutic dosing for autologous regenerative medicine applications. Full article

Background/Objectives: Laboratory automation is a key strategy for increasing productivity and reducing sample turnaround time (TAT), a common indicator of laboratory performance. However, owing to the statistical distribution of TAT values, conventional descriptors such as mean, standard deviation, and percentiles cannot capture the processing history of individual samples. In this study, sample flow within a highly automated laboratory system was analyzed by analogy with the Brownian motion of molecules in solution, using an ad hoc modified Dynamic Light Scattering (DLS) correlation function. Methods: Seven processing histories, each consisting of 1000 samples and representing different TAT scenarios, were generated, and the corresponding correlation functions were calculated. Each sample was assumed to remain correlated with its initial state (value = 1) until its TAT was reached; thereafter, once the result was produced, the sample was considered uncorrelated and its status value became 0. The correlation function was defined as the normalized progressive sum, over time, of the status values of all analyzed samples at each time point. Results: The DLS-inspired autocorrelation function enabled the derivation of parameters describing both overall system performance and sample processing status. These parameters provide quantitative indicators for near-real-time monitoring of automation chain efficiency and reveal system features that are not accessible through conventional TAT statistics. Conclusions: This approach allows the definition of measurable metrics describing the system’s capacity to buffer and mitigate operational disruptions at both the global and individual-sample levels. The proposed framework provides a novel tool for evaluating, monitoring, and comparing the performance of laboratory automation systems. Full article

Fall-from-height (FFH) detection is a critical component in wearable safety systems, particularly in environments where high-intensity movements can lead to frequent false positives. Conventional approaches based on simple thresholding of acceleration signals often fail to reliably distinguish FFH events from non-fall activities due to overlapping signal characteristics. This paper proposes a data-driven FFH detection method that integrates multiple complementary features into a unified score-based model. The proposed approach first performs structured peak detection to extract candidate impact events while significantly reducing the number of samples requiring further processing. Each candidate is then evaluated using pre-peak structure, post-impact stability, and pressure variation, which respectively capture structural, temporal, and physical characteristics of FFH events. Based on statistical analysis, feature-wise score contributions are designed to reflect their discriminative strength, and the final FFH decision is performed using an additive scoring mechanism. This formulation enables flexible handling of ambiguous cases while preserving strong FFH characteristics. Experimental results demonstrate that the proposed method maintains 100% recall at the selected decision threshold while significantly reducing false positives from non-FFH activities. In addition, the peak detection stage reduces more than 99% of raw samples, enabling efficient on-device processing suitable for wearable systems. The proposed method also includes quantitative analysis of latency characteristics. Although FFH inference latency is influenced by asynchronous pressure sensing, the delay remains bounded and predictable, and most detections are completed within a practical time range for real-time wearable safety applications. Overall, the proposed method achieves a practical balance between detection sensitivity, false-positive suppression, computational efficiency, and real-time feasibility, demonstrating its applicability to wearable safety systems. Full article

Three-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area. Binocular stereo vision provides a low-cost and easily deployable solution for the single-view 3D reconstruction of watermelons. However, watermelons present highly similar surface textures, and as typical spheroid-like objects, the excessive angle between surface normals of edge regions and the camera optical axis leads to insufficient feature representation. Consequently, directly applying existing stereo matching algorithms often introduces matching ambiguities, and lightweight networks struggle to balance real-time performance with matching accuracy. This study focuses on the high-precision single-view point cloud generation of Kirin watermelons. To address these issues, we first construct a cross-modal, high-precision Kirin watermelon stereo matching dataset. Building upon the Fast-ACVNet+ architecture, we then propose MI-ACVNet, a lightweight stereo matching network tailored for high-precision watermelon point cloud acquisition. In the feature extraction stage, a Multi-Scale Stereo Feature Extraction (MSFE) module is adapted. By incorporating the re-parameterized network MobileOne and Epipolar-Enhanced Coordinate Attention (E2CA), MSFE improves the discriminative capability for weak and similar textures without compromising inference speed. For cost computation, a Coarse-to-Fine Cascaded Residual Correction (C2F-CRC) strategy is incorporated to construct a fine-grained cost volume via sub-pixel interpolation, enhancing the network’s ability to capture subtle surface fluctuations. Furthermore, a Semantics-Guided Region-Aware Loss (SGRA-Loss) is formulated, leveraging semantic masks to apply differentiated supervision weights across edge, center, and background regions to significantly improve edge matching accuracy. Ablation studies validate the effectiveness of the MSFE, C2F-CRC, and SGRA-Loss components. Compared to the baseline model, the full MI-ACVNet reduces the End-Point Error (EPE) by 19.5% and the Bad-0.5 error rate by 34.5% in the watermelon region. Furthermore, when compared against five mainstream algorithms (StereoNet, AANet, HSMNet, LightStereo-L, and NMRF-swint), MI-ACVNet achieves state-of-the-art performance: EPE and Bad-0.5 are reduced to 0.091 pixels and 1.159%, respectively, with a single-frame inference time of only 46 ms. The average depth error of the reconstructed point clouds is merely 0.26 mm. By ensuring both real-time efficiency and high-precision depth estimation, this method demonstrates promising potential for deployment in industrial Kirin watermelon sorting lines, driving sorting equipment toward higher precision and intelligence. Full article

The global adoption of electric vehicles (EVs) highlights the critical role of lithium-ion battery packs in ensuring safety and performance, while voltage outliers as precursors to thermal runaway pose significant risks. Existing fault detection methods suffer from limited adaptability, poor uncertainty quantification, and inadequate handling of long-term temporal dynamics. To address these gaps, this study proposes a Probabilistic Hierarchical Adaptive Framework (PHAF) for early, uncertainty-aware detection of impending voltage outliers. PHAF integrates three core innovations: (1) the Weighted Outlier Depth (WOD) metric, which fuses Boltzmann-weighted voltage deviations and gradient-based thermal penalties to sensitively capture electro-thermal anomalies, especially under thermal stress (>45 °C); (2) the Learnable Spectral Convolution Network (LSCN), a novel architecture that combines adaptive spectral modulation and dual-path convolutions to model long-range frequency patterns and local temporal dependencies in voltage sequences; and (3) a hierarchical multi-model system that dynamically selects specialized models (LSCN, GRU, and LSTM) across four prediction horizons (160–40 min), leveraging quantile regression for uncertainty quantification and an early-termination mechanism to optimize computational efficiency. Evaluated on real-world data from 60 AITO EVs, PHAF achieves 95.4% classification accuracy for Level 1 (early-stage) faults at the 160 min horizon, >90% accuracy for critical Level 3 faults within 80 min, and a maximum AUC of 0.943 for long-term anomaly detection. This framework enables a transition from passive remediation to active prevention of battery thermal runaway, providing reliable, confidence-aware monitoring for safety-critical EV applications. Full article

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U²-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies. Full article

Underwater optical sensor networks are essential for fish monitoring, yet imagery is often affected by illumination variability, low contrast, and complex backgrounds. Attention mechanisms are vital for feature representation in deep networks, yet existing approaches often struggle with spatial information loss and limited multi-scale interaction under such challenging conditions. This paper introduces Convolution to Interactive Capture and Recalibration Enhancement (C2ICARE), a lightweight attention module designed to overcome these challenges. The principal contribution of C2ICARE is the adaptation of memory interaction principles into an edge-oriented attention framework that enhances feature discrimination while maintaining computational efficiency. The architecture employs three core innovations: a 1:3 memory-feature split to preserve context while reducing cost, parallel multi-scale depthwise convolutions (3 × 3 and 7 × 7) for fine-grained and broad feature extraction, and a cross-branch interaction mechanism coupled with a ConvNeXt-style feed-forward network that avoids dimensionality reduction. Experimental results on an underwater fish dataset demonstrate that YOLO26n with C2ICARE achieves a mean average precision (mAP@0.5:0.95) of 0.7033, outperforming Coordinate Attention (+3.8%), FasterBlock (+1.7%), and CBAM (+0.4%) while adding only 0.05M parameters and 0.16 GFLOPs. Multi-objective Pareto Frontier analysis confirms that C2ICARE provides an effective balance between accuracy, efficiency, and generalization for resource-constrained deployment. EigenCAM visualizations further validate that the model focuses on biological morphology rather than background noise. Its lightweight design enables seamless integration with underwater sensor networks and fog platforms for real-time fish detection in aquaculture, commercial fisheries, and scientific research. Future work will investigate broader marine applications and cross-platform deployment scenarios. The code is available on GitHub. Full article

To address the challenges of severe optical attenuation and dynamic feature extraction for moving target depth retrieval in complex underwater remote sensing environments, this paper proposes a dynamic target depth estimation method based on multi-source data fusion. Taking optical RGB imagery and neuromorphic vision (NeuroIV) data as joint inputs, the proposed method constructs a three-channel feature extraction and fusion network. By leveraging a hypergraph structure, it establishes association weights between dynamic (temporal) and static (spatial) nodes to capture spatiotemporal correlations. To efficiently process the high-dimensional multi-modal data, the traditional dot-product attention is replaced with element-wise multiplication, significantly reducing computational complexity. Furthermore, a lightweight deformable attention pyramid (DAP) and diffusion model is introduced to refine depth image edges, effectively suppressing discontinuities and abruptness in the estimation results. Compared to single-modality optical imagery, the fused multi-modal data yields a superior signal-to-noise ratio and foreground contrast, achieving an improvement of over 20% in the MAE index. These results validate the effectiveness and superiority of the proposed multi-modal fusion strategy for dynamic target observation and depth retrieval in aquatic environments. Full article

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide. Full article