Search Results (2,433)

Aquaculture is the fastest-growing food production sector globally. As its largest producer, China plays a pivotal role in ensuring aquatic food supply and supporting the blue economy. Despite its massive scale, a systematic understanding of the geographic distribution, structural composition, and drivers of China’s aquaculture value chain remains limited. We comprehensively characterized the sector’s composition, spatiotemporal evolution, and structural dynamics. We compiled and analyzed over 2.85 million enterprise registration records from the TianYanCha database, applying rigorous industry classification, spatial mapping, correlation analysis, and bottleneck assessment with natural and socioeconomic variables. Results show that policy reforms, notably the 2013 Company Law amendment and 2016 aquaculture certification measures, drove sharp increases in enterprise registrations, particularly in retail and farming. Enterprises are highly clustered in the Yangtze River Basin, Pearl River Delta, and southeastern coast, with inland expansion along major river systems. Strong interdependencies exist among sectors, while wholesale remains numerically scarce, forming a structural bottleneck. Standardization levels are low. Foreign investment, though under 5%, concentrated in processing and distribution, contributed to advanced technologies in the 1990s–2000s. These findings highlight rapid formalization, regional clustering, and structural imbalances, suggesting that enhancing formalization and addressing intermediary bottlenecks could improve sector resilience and efficiency. Full article

The development of modern machining and manufacturing industry puts forward higher requirements for the machining accuracy of machine tools. The thermal error of the machine tool spindle directly affects the accuracy of the machined workpiece. To improve the accuracy of thermal error prediction, this paper conducts temperature field analysis for the thermal error of the machine tool spindle and employs the Whale Optimization Algorithm (WOA) to optimize the temperature field parameters, aiming to establish a spindle temperature field model. This approach avoids the problem that traditional measurement methods cannot obtain the temperature of key rotational positions of the spindle and provides a new method for the selection of temperature-sensitive points in the thermal error measurement process. Initially, a spindle Product of Exponentials (POE) error model is constructed to map the five errors of the spindle to three-dimensional vectors in the machine tool space. Subsequently, the Whale Optimization Algorithm (WOA) is used to optimize the physical parameters of the spindle, and the optimal spindle temperature field model is determined. The calculated spindle thermal error data and temperature field model data are input into the OLGWO-SHO-CNN model for training. Finally, a case study is carried out on a machining center, and the trained model is used to perform compensation verification under constant and variable speed conditions, respectively. The experimental results show that under the constant speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 77.2%, 73.1%, and 88.7%, respectively; under the variable speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 74.7%, 78.2%, and 88.0%, respectively. The compensation results indicate that the established spindle temperature field model and the OLGWO-SHO-CNN model have good robustness and accuracy. Full article

Aircraft turnaround efficiency is a key determinant of the sustainability of air transport systems. Each stage of ground handling—passenger disembarkation, baggage handling, refuelling, and ancillary services—contributes to the total turnaround time, with direct implications for airport capacity, operating costs, and environmental performance. Using empirical records from ground operations, the study characterizes the duration and variability of individual activities and identifies the main process bottlenecks. Building on this evidence, a comparative PERT-COST protocol with explicit threshold rules (quantized billing steps for selected resources) is developed and applied across predefined scenarios (remote versus gate, day versus night, low versus high fuel uplift, with versus without a second baggage team) under both linear and threshold cost models. The protocol aligns with ITS-enabled decision support by mapping stochastic activity times to cost-of-crashing functions and by providing harmonized performance metrics: final time T, total cost ∑ΔC, and efficiency η (EUR/min). The results show that moderate time reductions are attainable at reasonable cost, whereas aggressive targets that lie below the structural minimum are infeasible under current constraints; gate stands reduce the attainable minimum time but increase the marginal price near the minimum, and night operations raise costs without improving that minimum. These findings delineate the most productive intervention range and inform operational choices consistent with sustainability objectives. Full article

Effective training is essential for addressing the continuous requirement for enhancing productivity and safety in construction. Virtual reality (VR) has emerged as a powerful tool for simulating site environments with high fidelity. While previous studies have explored the potential of VR in construction training, there is potential to incorporate advanced construction theories, such as lean principles, which are critical for optimizing work processes and safety. Thus, this study aims to develop an integrated VR-lean training system that integrates lean principles into traditional VR training, focusing on improving productivity and ergonomic safety—two interrelated challenges in construction. This study developed a virtual training environment for scaffolding installation, employing value stream mapping—a key lean tool—to guide trainees in eliminating waste and streamlining workflows. A before-and-after experimental design was implemented, involving 64 participants randomly assigned to non-lean VR or integrated VR-lean training groups. Training performance was assessed using productivity and ergonomic safety indicators, while a post-training questionnaire evaluated training outcomes. The results demonstrated significant productivity improvements in integrated VR-lean training compared to non-lean VR training, including a 12.3% reduction in processing time, a 21.6% reduction in waste time, a 20.8% increase in productivity index, and an 18.4% decrease in number of errors. These gains were driven by identifying and eliminating waste categories, including rework, unnecessary traveling, communication delays, and idling. Additionally, reducing rework contributed to a 7.2% improvement in the safety risk index by minimizing hazardous postures. A post-training questionnaire revealed that training satisfaction was strongly influenced by platform reliability and stability, and user-friendly, easy-to-navigate interfaces, while training effects of the integrated training were enhanced by before-session on waste knowledge and after-training feedback on optimized workflows. This study provides valuable insights into the synergy of lean principles and VR-based training, demonstrating the significant impact of lean within VR scenarios on productivity and ergonomic safety. The study also provides practical recommendations for designing immersive training systems that optimize construction performance and safety outcomes. Full article

This paper presents a real-time quarry truck monitoring system that combines deep learning and license plate recognition (LPR) for operational monitoring and weighbridge reconciliation. Rather than estimating load volumes directly from imagery, the system ensures auditable matching between detected trucks and official weight records. Deployed at quarry checkpoints, fixed cameras stream to an edge stack that performs truck detection, line-crossing counts, and per-frame plate Optical Character Recognition (OCR); a temporal voting and format-constrained post-processing step consolidates plate strings for registry matching. The system exposes a dashboard with auditable session bundles (model/version hashes, Region of Interest (ROI)/line geometry, thresholds, logs) to ensure replay and traceability between offline evaluation and live operations. We evaluate detection (precision, recall, mAP@0.5, and mAP@0.5:0.95), tracking (ID metrics), and (LPR) usability, and we quantify operational validity by reconciling estimated shift-level tonnage T against weighbridge tonnage T* using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R², and Bland–Altman analysis. Results show stable convergence of the detection models, reliable plate usability under varied optics (day, dusk, night, and dust), low-latency processing suitable for commodity hardware, and close agreement with weighbridge references at the shift level. The study demonstrates that vision-based counting coupled with plate linkage can provide regulator-ready KPIs and auditable evidence for production control in quarry operations. Full article

For garment manufacturing, an efficient and precise assessment of ergonomics is vital to prevent work-related musculoskeletal disorders. This study creates a computer vision-based algorithm for fast and accurate risk analysis. Specifically, we introduced SE and CBAM attention mechanisms into the YOLO network and integrated the optimized modules into the HRNet architecture to improve the accuracy of human pose recognition. This approach effectively addresses common interferences in garment production environments, such as fabric accumulation, equipment occlusion, and complex hand movements, while significantly enhancing the accuracy of human detection. On the COCO dataset, it increased mAP and recall by 4.43% and 5.99%, respectively, over YOLOv8. Furthermore, by analyzing key postural features from worker videos of cutting, sewing, and pressing, we achieved a quantified ergonomic risk assessment. Experimental results indicate that the RULA scores calculated using this algorithm are highly consistent and stable with expert evaluations and accurately reflect the dynamic changes in ergonomic risk levels across different processes. It is important to note that the validation was based on a pilot study involving a limited number of workers and task types, meaning that the findings primarily demonstrate feasibility rather than full-scale generalizability. Even so, the algorithm outperforms existing lightweight solutions and can be deployed in real-time on edge devices within factories, providing a low-cost ergonomic monitoring tool for the garment manufacturing industry. This helps prevent and reduce musculoskeletal injuries among workers. Full article

This scoping review evaluates the literature on options for planar solid oxide cell (SOC) performance optimization, with a focus on applied fabrication methods and design enhancements. Literature identification, selection, and charting followed PRISMA-ScR guidelines to ensure transparency, reproducibility, and comprehensive coverage, while also enabling the identification of research gaps beyond the scope of narrative reviews. We analyze the influence of fabrication methods on cell and component characteristics and evaluate optimization approaches identified in the literature. Subsequent discussion explores how design innovations intersect with fabrication choices. The surveyed literature reveals a broad spectrum of manufacturing methods, including conventional processes, thin-film deposition, infiltration, and additive manufacturing. Our critical assessment of scalability revealed that reduction in operating temperature, improving robustness, and electrochemical performance are the main optimization objectives for SOC designs. Regarding production cost, production scale-up, and process control, inkjet, electrophoretic deposition, and solution aerosol thermolysis appeared to be promising manufacturing methods for design enhancements. By combining the PRISMA-ScR evidence map with a synthesis focused on scalability and process control, this review provides practical insights and a strong foundation for future SOC research and scale-up, also for evolving the field of proton-conducting cells. Full article

Abandoned mines, as emblematic heritage spaces in the process of deindustrialization, preserve collective production memory and serve as vital symbols of local identity. However, current redevelopment practices primarily emphasize physical restoration while overlooking the visual expression and interactive communication of regional culture. This study introduces an augmented reality (AR)–based visual narrative framework that integrates regional culture to bridge the gap between spatial renewal and cultural regeneration. Drawing on semiotics and spatial narrative theory, a multidimensional “space–symbol–memory” translation mechanism is constructed, and a coupling model linking tangible material elements with intangible cultural connotations is established. Supported by technologies such as simultaneous localization and mapping (SLAM), semantic segmentation, and level of detail (LOD) rendering, a multilayer “position–perception–presentation” module system is designed to achieve stable anchoring of virtual and physical spaces and enable multilevel narrative interaction. Through task-oriented mechanisms and user co-creation, the system effectively enhances immersion, cultural identity, and learning outcomes. Experimental validation in a representative mine site confirms the feasibility of the proposed framework. While the study focuses on a single case, the modular and mechanism-based design indicates that the framework can be adapted to cultural tourism, educational communication, and community engagement applications. The key innovation lies in introducing an iterative “evidence–experience–co-creation” model, providing a new methodological reference for the digital reuse of abandoned mines and the sustainable preservation of industrial heritage. Full article

This study aimed to develop a deep learning-based application for the automatic detection of nutritional deficiencies in coffee plants through the analysis of in-field leaf images. Images were collected from farms in the Shipasbamba district and classified into six deficiency types: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe). A total of 2643 leaves were labeled and preprocessed for model training. Several YOLO architectures were evaluated, with YOLO11x achieving the best performance after 100 epochs, reaching a precision of 88.98%, recall of 88.54%, F1-Score of 88.76%, and mAP50 of 92.68%. An interactive web application was developed to allow real-time image upload and processing, providing both graphical and textual feedback on detected deficiencies. These results demonstrate the model’s effectiveness for automated diagnosis and its potential to support coffee growers in timely, data-driven decision-making, ultimately improving nutrient management and reducing production losses. Full article

Blossom-end enlargement (BEE) is a physiological disorder in cucumbers (Cucumis sativus L.) that affects postharvest quality and results in commercial loss due to reduced product value. Pre-cooling using modified atmosphere packaging (MAP) has been encouraged as a preventive method of BEE; however, BEE can still be observed under actual distribution conditions. This study reexamined the process from harvesting in midsummer to arriving at the market (550 km) and storage, while considering the impact of packaging materials, packaging methods, and human factors on BEE occurrence. More than 18 h were required from harvest to delivery at the pre-cooling warehouse at the common shipping site; however, despite using a refrigerated truck, the temperature inside the packaging increased again during transportation. The temperature then dropped during 24 h of pre-cooling; however, it did not reach 10 °C, the appropriate storage temperature for cucumbers. MAP suppressed the occurrence of BEE compared to conventional film packaging; however, the BEE index varied greatly between individuals who performed the packaging. We determined that tying both ends of the packaging film increases the degree of airtightness as individual differences decrease and is more effective at suppressing BEE. Porous mineral-containing film (PM) packaging, which generates a modified atmosphere (MA), significantly suppressed BEE compared to conventional perforated film (C). In 2019 transport trials, the BEE index at 6 DAH for C film was 77.3, while for PM film it was only 12.0. Furthermore, we found that the effectiveness of PM film was significantly affected by human-related operational factors. The novel packaging method of tying both ends of the film (PM-T) provided the most consistent BEE suppression and lowest BEE index regardless of the packaging worker, demonstrating its superior potential in standardizing airtightness and minimizing human-related operational variability. Full article

In industrial production, defect detection for automotive headlight lenses is an essential yet challenging task. Transparent glass defect detection faces several difficulties, including a wide variety of defect shapes and sizes, as well as the challenge of identifying transparent surface defects. To enhance the accuracy and efficiency of this process, we propose a computer vision-based inspection solution utilizing multi-angle lighting. For this task, we collected 2000 automotive headlight images to systematically categorize defects in transparent glass, with the primary defect types being spots, scratches, and abrasions. During data acquisition, we proposed a dataset augmentation method named SWAM to address class imbalance, ultimately generating the Lens Defect Dataset (LDD), which comprises 5532 images across these three main defect categories. Furthermore, we propose a defect detection network named the Transparent Glass Defect Network (TGDNet), designed based on common transparent glass defect types. Within the backbone of TGDNet, we introduced the TGFE module to adaptively extract local features for different defect categories and employed TGD, an improved SK attention mechanism, combined with a spatial attention mechanism to boost the network’s capability in multi-scale feature fusion. Experiments demonstrate that compared to other classical defect detection methods, TGDNet achieves superior performance on the LDD, improving the average detection precision by 6.7% in mAP and 8.9% in mAP50 over the highest-performing baseline algorithm. Full article

Blow spinning is a low-cost and versatile method that permits the large-scale production of fibrous membranes. However, polysaccharides that show numerous merits such as biocompatibility and biodegradability often have a low spinnability due to their high chain rigidity and low ability to form sufficient entanglements. Here, we report the fabrication of polysaccharide micro-fibrous membranes from sodium alginate/polyethylene oxide solutions formulated in solvent mixtures of water and ethanol. The shear and extensional rheological responses of the solutions are characterized, and parameters including specific shear viscosity, reptation time, extensional relaxation time, and maximum stretch ratio are correlated with the concentrations of polymer, polyethylene oxide, and ethanol. It is found that flexible polyethylene oxide and poorer solvent ethanol can synergistically delay the chain relaxation during stretch and increase the stretchability of the solutions. A processability map of the solutions for blow spinning is constructed, enabling the fabrication of fibrous membranes with a fiber diameter of ~1 μm, tensile strength of 4.89 MPa, elongation at break of 15.24%, and Young’s modulus of 45.43 MPa. This study presents a new strategy to fabricate sodium alginate-based membranes, which should provide insights into the design of other polysaccharide membranes with specific functions and applications. Full article

Quadrotors have been under development for over a century. The first quadrotors were large, heavy, and difficult to control aircraft operated by a single pilot. The first quadrotors remained in the prototype stage due to accidents, budget cuts, and failure to meet military standards. Production of manned quadrotors ceased in the 1980s. Since the 2010s, manned quadrotors have been used as air taxis, achieving greater success. The development of quadrotor unmanned aerial vehicles (UAVs) began in the 1990s. Their small size, low cost, and ease of control have made them advantageous. Advances in hardware and software technologies have expanded the use of quadrotor UAVs. Today, quadrotor UAVs are used in various fields, including surveillance, aerial photography, search and rescue, firefighting, first aid, cargo transportation, agricultural spraying, mapping, mineral exploration, and counterterrorism. This review examines the development of manned quadrotors and quadrotor UAVs in detail from the past to the present. First, the major manned quadrotors developed are described in detail, along with their technical specifications and photographs. Graphs are provided showing the weight, powerplant, flight duration, and passenger capacity of manned quadrotors. Second, the main quadrotor UAV models entering mass production are discussed, presenting their development processes, technical specifications, areas of use, and photographs. Graphs are presented showing the weight, battery capacity, flight duration, and camera resolution of quadrotor UAVs. Unlike studies focusing solely on the recent past, this review provides a broad overview of the development of quadrotors from their inception to the present. Full article

Rangeland degradation poses a serious challenge for the sustainable management of free-ranging livestock in Mediterranean wetlands. In Doñana National Park, Spain, the endangered Marismeño horse depends exclusively on natural forage, making it essential to monitor vegetation productivity and grazing suitability under increasing climate variability. This study presents a satellite-based assessment of rangeland carrying capacity to support the adaptive management of this iconic breed. A six-year time series (2015–2020) of 1242 images from Landsat 8 OLI/TIRS and Sentinel-2 (L1C/L2A) was processed using ILWIS and Python-based workflows to derive vegetation indices (GNDVI, NDMI) and model aboveground biomass, forage energy, and grazing pressure across five grazing units. Results revealed strong seasonal cycles, with biomass and nutritive value peaking in spring and declining sharply in summer. Ecotonal zones such as La Vera y Sotos acted as crucial refuges during drought-induced resource shortages. The harmonized multi-sensor approach demonstrated high reliability for mapping forage dynamics and assessing carrying capacity at fine scales. This remote sensing framework offers an effective, scalable tool for sustainable livestock management in Doñana, directly supporting biodiversity conservation and the long-term resilience of Mediterranean rangeland ecosystems. Full article

With the gradual implementation of the EU Battery Regulation and the DBP (Digital battery passport), it has become critical to determine the carbon footprint of lithium-ion battery products that contain recycled lithium resources. However, the diversity of recycling pathways substantially increases the complexity of carbon footprint accounting and DBP construction for recycled lithium batteries. This paper proposes a carbon activity based granular allocation and integration mechanism. Built on organizational operational data in EIS (Enterprise information systems) (ERP (Enterprise resource planning)/MES (Manufacturing execution system)/SCADA (Supervisory control and data acquisition), etc.) and using carbon activities as the linkage for mapping, the mechanism supports the acquisition and sound allocation of product carbon data, thereby improving the availability of carbon data and the rationality of allocation throughout the accounting process, and enabling more robust product carbon footprint calculations. Across different recycling routes, the carbon footprint results for recycled lithium resources can differ by more than 65%. When considering spodumene as the lithium source, mixing primary and recycled lithium carbonate in varying proportions can lead to up to a tenfold difference in the carbon footprint of products containing recycled lithium. Therefore, precisely tracing the carbon emission activities associated with different lithium sources is crucial for enhancing the accuracy of carbon footprint accounting, promoting the sustainable development of lithium resources, and meeting the requirements of the new Battery Regulation and the DBP. Full article