Search Results (5,984)

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Using a complex network evolutionary game model, this study examines the effects of policy incentives, certification mechanisms, price premiums, production costs, and neighborhood learning on farmers’ adoption of organic farming technologies. It aims to reveal the dynamic mechanisms of organic farming technology diffusion under subsidy policies and certification mechanisms. Numerical simulations are conducted to analyze the effects of the subsidy rate and the effectiveness of organic certification on the diffusion level of organic farming technologies. The results show that both subsidy policies and certification mechanisms can promote the diffusion of organic farming technologies; however, the effect of subsidy policies is relatively limited, whereas certification mechanisms play a more significant role. Furthermore, the effects of the subsidy rate and certification effectiveness are influenced by factors such as the proportion of consumers with a preference for organic products, increased production costs, and the organic price premium. Under different levels of bounded rationality and strategy updating rules, the combined “subsidy–certification” policy consistently outperforms single-policy scenarios, with certification mechanisms generally exerting a stronger promotional effect than subsidy policies. In addition, the initial adoption proportion and network size also affect the evolutionary outcomes of the system. A higher initial adoption proportion cannot sustain a higher steady-state diffusion level in the long run, while an increase in network size tends to weaken the effectiveness of policy interventions. Finally, this study proposes policy recommendations, including improving certification and market development mechanisms and strengthening information dissemination and technical service systems, thereby providing practical insights for promoting the diffusion of organic farming technologies. Full article

This paper reviews emerging dielectric fluids for power transformers, including natural and synthetic esters, silicone oils, gas-to-liquid oils, and nanofluids, driven by environmental regulations, fire safety concerns, and the need for extended asset life. The review synthesizes technical data from standards and field experience, including a case study of an Eskom transformer energized in 2016 with natural ester fluid. Analysis confirms these fluids offer significant benefits in fire safety, biodegradability, and dielectric performance, with the case study demonstrating natural esters’ effectiveness in preserving solid insulation. However, trade-offs involving cost, material compatibility, and operational protocols require careful management. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Potato sorting in post-harvest processing relies heavily on manual visual inspection, which is physically demanding, subjective, and insufficiently scalable for modern packing lines. This study investigates the feasibility of a low-cost RGB-based optical inspection system for automated potato quality detection using deep learning-based object detection. A controlled imaging platform was constructed using commodity hardware, and a dataset of 19,805 manually annotated instances across 1361 images was collected from two geographically distinct farm locations in Slovenia. A systematic benchmark of 25 model configurations spanning five YOLO architecture families—YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLO26—was conducted across three practical quality classes (Edible, Feed, Rotten) using a strict cross-location evaluation protocol in which models were trained on one location and tested on a completely unseen second location. All models achieved strong in-distribution performance (F1 ≥ 0.906), but showed considerable variation under cross-location conditions, with external F1 ranging from 0.792 to 0.918. The yolo26_l configuration achieved the best cross-location performance (F1 = 0.918, mAP@0.5:0.95 = 0.816, ΔF1 = 0.029), demonstrating that transferable representations are achievable under a standard supervised training protocol. Per-class analysis identified feed detection as the primary generalization bottleneck. The results confirm that affordable RGB-based sorting systems are technically feasible and highlight cross-location evaluation as an essential protocol for assessing real-world deployment readiness. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

Aiming at the characteristics of high viscosity and poor fluidity of high waxy ordinary heavy oil, a Janus silica-based nanosheet composite viscosity reducer was designed and prepared in this paper. The viscosity reducer was assembled by asymmetric Gemini viscosity reducer and silica nanosheets through dehydration condensation reaction, and its structure was verified by FT-IR, ¹HNMR, XPS and DLS. The viscosity reduction performance, emulsion stability, interfacial tension and flow performance of the viscosity reducer were systematically evaluated by taking heavy oil with wax content of 35.7% and viscosity of 237 mPa·s at 30 °C as the research object. The results showed that, at an oil-to-viscosity-reducer-solution volume ratio of 3:7 and a viscosity reducer mass fraction of 0.3%, the maximum viscosity reduction rate reached 94.5% at 30 °C, calculated relative to the viscosity of the dehydrated original heavy oil. The oil–water interfacial tension was significantly reduced, and the 24 h bleeding ratio, defined as the volume percentage of separated water relative to the initial aqueous phase volume, was only 7.3%, indicating good emulsion stability. The core flow experiment shows that the resistance coefficient is reduced to the lowest at 0.3% concentration, and the seepage capacity is significantly improved. The analysis of total hydrocarbon gas chromatography showed that the content of high-carbon wax components in the C₂₃-C₃₀ range decreased by 4.79 percentage points after treatment, indicating that the viscosity reducer preferentially interacted with high-carbon wax molecules and promoted wax-crystal dispersion, thereby weakening the three-dimensional wax-crystal network. The viscosity reducer has the synergistic effect of dispersing wax crystals, reducing interfacial tension and stabilizing emulsification, which provides a low-cost and high-performance technical approach for the efficient exploitation of high waxy ordinary heavy oil. Full article

The mitigation of anthropogenic climate change caused by fossil fuel combustion is a critical global challenge that necessitates a transition to renewable energy systems. Bioethanol represents a major renewable fuel, but first-generation production relies on edible feedstocks, which raises concerns regarding food security. Consequently, research is shifting toward second-generation bioethanol produced from abundant non-edible lignocellulosic biomass sources. This review comprehensively examines the potential of Candida krusei (synonyms: Pichia kudriavzevii, Issatchenkia orientalis) to serve as an alternative biocatalyst for second-generation bioethanol production. Compared with the first-generation bioethanol-producing yeast Saccharomyces cerevisiae, C. krusei exhibits superior physiological traits, such as thermo, acid, and inhibitor tolerances, enabling the utilization of several lignocellulosic feedstocks. This review summarizes the taxonomic and physiological characteristics of C. krusei, describes case studies on bioethanol production, and discusses strategies for reducing production costs. Furthermore, the technical and biosafety challenges associated with the industrial deployment of C. krusei are critically examined, including xylose metabolism limitations, scale-up constraints, and the management of its opportunistic pathogenic nature. A life cycle assessment perspective suggests that the unique physiological properties of C. krusei contribute to reducing greenhouse gas emissions and energy consumption throughout the entire production process, from pretreatment to downstream ethanol recovery. Full article

This study investigates the electrical properties of gold mine tailings from the Soweto mining region to assess their potential as a low-cost and sustainable backfill material for grounding systems. Samples were collected from historical mine dumps, oven-dried at 70 °C for 24 h to determine dry density and baseline moisture content, and reconstituted to controlled moisture levels of 5–25% by mass. Bulk electrical resistivity was measured using the Wenner four-electrode method in accordance with ASTM G57-06. The results reveal a strong inverse correlation between moisture content and resistivity. At low moisture content (≈5%), resistivity exceeded measurable limits, indicating poor ionic conduction, whereas increasing moisture content led to a substantial reduction in resistivity, reaching an average value of approximately 10 Ω at 25% moisture due to improved pore water continuity and ionic mobility. These findings demonstrate that moisture-conditioned gold mine tailings can achieve electrical performance comparable to that of conventional grounding enhancement materials while offering notable economic and environmental benefits. Owing to their local availability and waste re-utilisation potential, the tailings present a technically feasible and environmentally responsible solution for improving earthing performance in high-resistivity soils. Further work should examine long-term field performance, corrosion effects, and leaching behaviour. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

As urban energy demand increases and available roof space remains limited, balcony photovoltaic (PV) systems have emerged as a promising distributed renewable energy solution. This study aims to evaluate the multidimensional benefits of these systems in urban residential applications from a dual-carbon perspective. A combination of experimental tests and numerical simulations was used to investigate the effects of installation tilt angles and vertical self-shading in high-rise buildings. A comprehensive assessment model was constructed, integrating technical power generation gains, economic returns, and environmental carbon reduction benefits. The results demonstrate that when comprehensively balancing generation gains, economic viability, and structural safety, the practical optimal installation tilt angle for balcony PV systems is around 30°. The Levelized Cost of Electricity (LCOE) is calculated at 0.050–0.061 USD/kWh. Furthermore, a standard 800 W system operating under Beijing’s climate conditions can reduce carbon emissions by approximately 12.68 tons over its 25-year lifecycle. Therefore, balcony PV systems deliver significant technical, economic, and environmental benefits, serving as a highly feasible strategy to promote low-carbon and sustainable development in high-density cities. Full article

Satellites become critical to space exploration, global communication, Earth observation, and navigation. There is a growing need for satellite avionics that are highly integrated, reliable, and low-cost, which is essential for mass production and reliable on-orbit operation. This work demonstrates integrated satellite avionics with high reliability and low cost based on a monolithic programmable system-on-chip (SoPC) through highly synergistic hardware–software co-design, with successful on-orbit validation. The system highly integrates satellite management, attitude and orbit control, power management, telecontrol and telecommand (TC&TM), and data storage into a monolithic PolarFire^® SoC (System-on-Chip), and leverages an asymmetric multiprocessing (AMP) architecture. It achieves significant reductions in size, weight, power, and cost (SWaP-C) while ensuring comprehensive functionality and operational reliability. The Jilin-1 Gaofen-05A mission verified the proposed SoPC-based satellite avionics for low Earth orbit (LEO) commercial satellites. Long-term telemetry data confirms its stable operation, with a bus voltage ranging from 11.4 to 12.3 V, an average power consumption of 33.4 W, and a solar array output current of 6.2–6.5 A, all of which meet the design expectations. This work offers a feasible technical approach and engineering reference for commercial integrated satellite avionics featuring high reliability and cost efficiency. Full article

Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R² of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R² values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. Full article