Search Results (921)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

The intensifying global competition for cobalt resources and the increasing likelihood of trade decoupling and disruption are profoundly impacting the global energy transition. In a globalized trade environment, a decline in cobalt supply from exporting countries can spread through the trade network, negatively affecting demand countries. Quantitative analysis of the negative impacts of export supply declines in various countries can help identify early risks in the global supply chain, providing a scientific basis for energy security, industrial development, and policy responses. This study constructs a trade network using trade data on metal cobalt, cobalt powder, cobalt concentrate, and ore sand from the upstream (mining, selection, and smelting) stages of the cobalt industry chain across 155 countries and regions from 2000 to 2023. Based on this, an impact diffusion model is established, incorporating the trade volumes and production levels of cobalt resources in each country to measure their resilience to shocks and determine their direct or indirect dependencies. The study then simulates the impact on countries (regions) when each country’s supply is completely interrupted or reduced by 50%. The results show that: (1) The global cobalt trade network exhibits a ‘one superpower, multiple strong players’ characteristic. Congo (DRC) has a far greater destructive power than other countries, while South Africa, Zambia, Australia, Russia, and other countries have higher destructive power due to their strong storage and production capabilities, strong smelting capabilities, or as important trade transit countries. (2) The global cobalt trade network primarily consists of three major risk areas. The African continent, the Philippines and Indonesia in Southeast Asia, Australia in Oceania, and Russia, the United States, China, and the United Kingdom in Eurasia and North America form the primary risk zones for global cobalt trade. (3) When there is a complete disruption or a 50% reduction in export supply, China will suffer the greatest average demand loss, far exceeding the second-tier countries such as the United States, South Africa, and Zambia. In contrast, European countries and other regions worldwide will experience the smallest average demand loss. Full article

Background: Pre-season training is critical for developing tolerance to high physical demands in professional soccer, and nitric oxide (NO) precursors such as dietary nitrate (NO₃⁻) and citrulline malate (CM) can support performance and recovery during this demanding phase. This study aimed to examine the effects of a four-week supplementation protocol combining 500 mg of NO₃⁻ from amaranth extract and 8 g of CM (NIT + CM) on external training load and post-match recovery in professional female soccer players during pre-season. Methods: A randomized, double-blind, placebo-controlled trial was conducted with 34 female soccer players who received either the NIT + CM product or a placebo for four weeks during pre-season. Global positioning system (GPS)-derived external load was recorded throughout the intervention. Performance tests—a countermovement jump (CMJ) test and the Wingate anaerobic test (WAnT)—and blood sampling for plasma NO₃⁻ and nitrite (NO₂⁻) concentrations were conducted at baseline and the day after a competitive match. Results: The supplementation with NIT + CM increased maximal speed (Vmax) throughout training and match play. During post-match testing, the NIT + CM group exhibited a significantly smaller decline in mean (P_mean) and minimum (P_min) power during the WAnT, along with reduced power loss in both the first (0–15 s) and second (15–30 s) intervals. Plasma NO₃⁻ concentrations significantly increased from baseline in the NIT + CM group and remained elevated 24 h after the final dose, confirming sustained systemic exposure. Conclusions: Chronic NIT + CM supplementation may enhance Vmax and help preserve anaerobic performance the day after a match. These effects could reflect improved tolerance to high training loads and sustained NO₃⁻ availability during recovery. Full article

The present study aimed to characterize the total phenolic content and antioxidant activity of mesquite pods (Prosopis velutina) and evaluate the effect on meat qualities in a meat product, with a view to their application as a natural functional ingredient. Mesquite pods were subjected to chemical characterization, revealing the presence of polyphenol contents with antioxidant activity (reducing power and antiradical effect). In addition, pork patties were formulated with different levels of mesquite pods powder (MPP, 2% and 5%) and mesquite pods extract (MPE, 0.1% and 0.3%), and were compared with control (CN) samples. The proximate composition of mesquite pod powder revealed a high proportion of carbohydrates and a low fat content. Additionally, the presence of polyphenols with antioxidant activity, including antiradical and reducing power, was evident. No significant differences were observed in the pork patties’ proximate composition. During 9 days of storage at 2 °C, patties treated with MPP and MPE exhibited higher pH values and lower TBARS values compared to the CN, with MPE-0.3% being the most effective in retarding lipid oxidation. Color parameters (L*, a*, b*, C*, and h*) were positively influenced by MPP and MPE, and both treatments improved water-holding capacity and reduced cooking weight loss, especially at 5% MPP. Fracture texture analysis showed that 5% MPP enhances firmness. Sensory attributes did not differ significantly from the CN. These results indicate that MPP and MPE are promising natural ingredients for extending the shelf life and maintaining the quality of pork patties without compromising sensory acceptability. Full article

Driven by the environmental benefits associated with reduced greenhouse gas emissions, oil companies have intensified research efforts into reassessing the strategies used to meet the electrical demands of offshore production platforms. Among the various alternatives available, the deployment of onshore–offshore interconnections via High-Voltage Direct Current (HVDC) transmission systems has emerged as a promising solution, offering both economic and operational advantages. In addition to reliably meeting the electrical demand of offshore facilities, this approach enables enhanced operational flexibility due to the advanced control and regulation capabilities inherent to HVDC converter stations. Based on the use of interconnection through an HVDC link, aiming to evaluate the operation of the electrical system as a whole, this study focuses on evaluating dynamic events using the PSCAD software version 5.0.2 to analyze the direct online starting of a large induction motor and the sudden loss of a local synchronous generating unit. The simulation results are then analyzed to assess the effectiveness of both Grid-Following (GFL) and Grid-Forming (GFM) control strategies for the converters, while the synchronous generators are evaluated under both voltage regulation and constant power factor control operation, with a particular focus on system stability and restoration of normal operating conditions in the sequence of events. Full article

In the current context of environmental concerns and the search for sustainable food solutions, this study investigated the valorization of Luffa cylindrica seed press cake, a waste byproduct from oil extraction, as a functional ingredient for meat substitute formulations. The research systematically characterized the functional and bioactive properties of L. cylindrica seed press cake powder (LP) and its blends with tapioca flour (TF) at ratios of 30–70%. Techno-functional analyses included: hydration properties (water holding capacity, water absorption capacity, water absorption index, water solubility index, swelling power, oil absorption capacity); rheological characteristics; bioactive profiling through antioxidant assays (DPPH, ABTS, FRAP); and reducing sugar content determination. Meat substitute formulations were developed using an LP30/TF70 blend combined with coral lentils, red beet powder, and water, followed by a sensory evaluation and storage stability assessment. Pure L. cylindrica powder exhibited the highest water holding capacity (3.62 g H₂O/g) and reducing sugar content (8.05 mg GE/g), while tapioca flour showed superior swelling properties. The blends demonstrated complementary functional characteristics, with the LP30/TF70 formulation selected for meat substitute development based on optimal textural properties. The sensory evaluation revealed significant gender differences in acceptance, with women rating the product substantially higher than men across all attributes. The study successfully demonstrated the feasibility of transforming agricultural waste into a valuable functional ingredient, contributing to sustainable food production and representing the first comprehensive evaluation of L. cylindrica seed press cake for food applications. However, the study revealed limitations, including significant antioxidant loss during thermal processing (80–85% reduction); a preliminary sensory evaluation with limited participants showing gender-dependent acceptance; and a reliance on locally available tapioca flour, which may limit global applicability. Future research should focus on processing optimization to preserve bioactive compounds, comprehensive sensory studies with diverse populations, and an investigation of alternative starch sources to enhance the worldwide implementation of this valorization approach. Full article

In the operation of a power distribution system, miniature circuit breakers (MCBs) are subjected to the synergistic effect of electrical and mechanical stresses in service, and their operational performance is progressively degraded, which is prone to bring significant losses to the users after failures occur. In order to accurately predict the remaining electrical life of MCBs in service, MCB mechanical characterization and dynamic simulation are carried out, and the initial closing angle of MCBs is selected as the degradation characteristic quantity, so as to deeply analyze the evolutionary characteristics of the initial closing angle in the degradation of MCBs and to construct the electrical degradation model of the one-dimensional linear Wiener process in the present study. With the help of the Monte Carlo method, we carry out the electric life simulation analysis to investigate the intrinsic correlation between the degradation of electric performance and the initial closing angle, and we implement the electric life experiment under the 63 A working condition to analyze the dynamic change in the stiffening angle of the test samples. The parameters of the electrical performance degradation model are identified through the synergistic driving of the electrical life simulation data and the experimental data, the remaining electrical life prediction is realized based on the degradation data of the same batch of products, and the maximum prediction error of the proposed method is controlled within 15%. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

High-current electron beams can significantly enhance the productivity of variety of applications including medical radioisotope (RI) production and wastewater purification. High-power superconducting radio frequency (SRF) linacs are capable of producing such high-current electron beams due to the key advantage to operate in continuous wave (CW) mode. However, this requires an injector capable of generating electron bunches with high repetition rate and in CW mode, while minimizing beam losses to avoid damage to SRF cavities due to quenching. RF gating to the grid of a thermionic electron gun is a promising solution, as it ensures CW bunch generation at the repetition rate same as the fundamental or sub-harmonics of the accelerating RF frequency, with minimal beam loss. This paper presents detailed beam dynamics simulations demonstrating that an RF-gated gun operating at 1.3 GHz can generate bunches with 148 ps full width with 8.96 pC charge. Full article

Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs to be solved. In this paper, a three-phase transient model based on the level-set method is established to examine the heat transfer and melt pool behavior in PBF-LB/M. Surface tension, the Marangoni effect, and recoil pressure are implemented in the model, and evaporation-induced mass and thermal loss are fully considered in the computing element. The results show that the surface roughness and density of metal parts induced by heat transfer and melt pool behavior are closely related to process parameters such as laser power, layer thickness, scanning speed, etc. When the volumetric energy density is low, the insufficient fusion of metal particles leads to pore defects. When the line energy density is high, the melt track is smooth with low porosity, resulting in the high density of the products. Additionally, the partial melting of powder particles at the beginning and end of the melting track usually contributes to pore formation. These findings provide valuable insights for improving the quality and reliability of metal additive manufacturing. Full article

As the final stage of electrical energy delivery, distribution networks play a vital role in ensuring reliable power supply to end users. In regions with limited distribution automation, reliance on operator experience for fault handling often prolongs outage durations, undermining energy sustainability through increased economic losses and carbon-intensive backup generation. Remote-controlled switches (RCSs), as fundamental components of distribution automation, enable remote operation, rapid fault isolation, and load transfer, thereby significantly enhancing system reliability. In the process of intelligent distribution network upgrading, this study targets scenarios with sufficient line capacity and constructs a reliability-oriented analytical model for optimal RCS allocation by traversing all possible faulted lines. The resulting model is essentially a mixed-integer linear programming formulation. To address bilinearities, the McCormick envelope method is applied. Multi-binary products are decomposed into bilinear terms using intermediate variables, which are then linearized in a stepwise manner. Consequently, the model is transformed into a computationally efficient mixed-integer linear programming problem. Finally, the proposed method is validated on a 53-node and a 33-bus test system, with an approximately 30 to 40 times speedup compared to an existing mixed-integer nonlinear programming formulation. By minimizing outage durations, this approach strengthens energy sustainability through reduced socioeconomic disruption, lower emissions from backup generation, and enhanced support for renewable energy integration. Full article

In industrial production, high-power microwaves are commonly used for heating and drying processes; however, their application in measurement is relatively limited. This paper presents a power measurement system to enhance the use of microwave measurements in industry and improve the efficiency of microwave drying for PET particles. Operating at 2.45 GHz, the system integrates four-port power measurements based on the multilayer perceptron (MLP). By introducing residual connectivity, the residual network is determined to detect the height of PET particles. Experimental results show that this system can perform rapid measurements without needing a vector network analyzer (VNA), significantly improving the efficiency of microwave energy utilization in the early drying stages. Furthermore, the system offers practical and cost-efficient predictions for low-loss particulate materials. This power measurement strategy holds promising application potential in future industrial production. Full article

High-temperature fuel cells and electrolyzers, particularly molten carbonate fuel cells (MCFCs) and Molten Carbonate Electrolyzers (MCEs), are expected to play a critical role in clean power generation, hydrogen production, and integrated CO₂ separation. Unfortunately, despite their potential, these technologies have not yet reached full commercialization. The main reason for this is material degradation. In particular, the corrosion of metallic components continues to be a leading cause of performance loss and system failure. This review provides a comprehensive assessment of degradation mechanisms in MCFC and MCE systems. It examines key metallic components, such as current collectors and bipolar plates, focusing on the performance of commonly used materials, including stainless steels and advanced alloys, under prolonged exposure to corrosive environments. To address degradation issues, this review evaluates current mitigation strategies and discusses material selection, protective coatings application, and the optimization of operational parameters. Advances in alloy development, coatings, surface treatments, and process controls have been compared in terms of effectiveness, scalability, and long-term stability. The review concludes with a synthesis of current best practices and future directions, emphasizing the need for integrated, multi-functional solutions to achieve the lifetimes required for full commercialization. By linking materials science, electrochemistry, and systems engineering, this review offers directions for the development of corrosion-resistant MCFC and MCE technologies in support of a hydrogen-based, carbon-neutral energy future. Full article