Search Results (38,556)

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing. Full article

Sambucus williamsii Hance (Viburnaceae), the Korean elderberry, is widely used in herbal medicine and in the food industry. It is known to have various pharmacological effects, including antitumor, antioxidant, anti-inflammatory, and antimicrobial activities. During our search for anti-Helicobacter pylori compounds from natural resources, the methanol extract of the S. williamsii branch significantly inhibited the growth of H. pylori. Three phenolic and four lignan compounds were isolated from the methylene chloride fraction that had shown the most potent anti-H. pylori activity among the hexane, methylene chloride, ethyl acetate, butanol, and water fractions. The chemical structures were identified to be three phenolics of sylvopinol (1), dihydroconiferyl alcohol (2), and (7S,8R)-guaiacylglycerol (3) and four lignans of boehmenan (4), (7S,8S)-guaiacylglycerol β-coniferyl ether (6) and lawsonicin (7) with a new lignan, (7R,8R)-sambucanol (5), the structure of which was established by ¹H- and ¹³C-NMR, and HRESI-MS, as well as quantum chemical electronic circular dichroism (ECD) calculations. Among the isolates, compounds 3 and 4 exhibited significant anti-H. pylori activity against strains 51 and 26695. Compound 3 displayed more potent antibacterial activity with MIC values of 3.13 and 6.25 μM, and MIC₅₀ values of 28.5 and 56.8 μM against the two strains, respectively. Their inhibitory activities were higher than those of a positive control, quercetin. Furthermore, these two compounds showed moderate urease inhibitory activity. A molecular docking simulation revealed the high binding ability of 3 and 4 to the active site of H. pylori urease. These results will provide further insights into the design of more potent natural products for eradicating H. pylori. Full article

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

Making full utilisation of the synergies that exist among the various stages of industrial green production is beneficial to the realisation of the dual-carbon goal. However, the synergistic effects among the three stages of industrial green production have not yet been explored in depth from a microscopic perspective. Based on the analytic hierarchy process, the entropy weighting method, the coupled synergy degree model, the spatial autocorrelation test, and the geographically weighted regression model (GWR), the spatiotemporal evolution characteristics and driving mechanism of the synergistic effects among the three stages of industrial green production were explored by utilising the relevant data of industrial enterprises in 30 provinces of China from 2012 to 2022. The results showed that the synergistic effect of industrial green production exhibited an upward trend over time, and displayed a regional distribution characteristic of decreasing from east to west. The spatial differences in the synergistic effects of industrial green production gradually narrowed and the number of provinces with high–high (H-H) agglomerations increased. The level of digital economy development, the urbanisation level, the optimisation of industrial structure, the level of green credit, and the intensity of environmental regulation were the main driving factors for the synergistic effects of industrial green production, and there were significant spatial differences. This study provides a basis for the formulation of differentiated regional green development policies from the perspective of synergizing the various stages of industrial green production. Full article

Industrial wastewater pollution has reached acute levels in the environment; consequently, scientists are developing new sustainable treatment methods. Lignocellulosic biomass (LB) stands as a promising raw material because it originates from agricultural waste, forestry residues, and energy crop production. This review examines the application of nanomaterials derived from lignocellulosic resources in wastewater management, highlighting their distinctive physical and chemical properties, including a large surface area, adjustable porosity structure, and multifunctional group capability. The collection of nanomaterials incorporating cellulose nanocrystals (CNCs) with lignin nanoparticles, as well as biochar and carbon-based nanostructures, demonstrates high effectiveness in extracting heavy metals, dyes, and organic pollutants through adsorption, membrane filtration, and catalysis mechanisms. Nanomaterials have benefited from recent analytical breakthroughs that improve both their manufacturing potential and eco-friendly character through hybrid catalysis methods and functionalization procedures. This review demonstrates the ability of nanomaterials to simultaneously turn waste into valuable product while cleaning up the environment through their connection to circular bioeconomic principles and the United Nations Sustainable Development Goals (SDGs). This review addresses hurdles related to feedstock variability, production costs, and lifecycle impacts, demonstrating the capability of lignocellulosic nanomaterials to transform wastewater treatment operations while sustaining global sustainability. Full article

The electric arc furnace (EAF) is a key technology in the steel production industry, particularly for recycling scrap iron. It plays a crucial role in the shift to low-carbon metallurgy, responding to the growing demand for more sustainable production methods. Alongside its environmental and energy benefits, the EAF process generates significant amounts of solid by-products, including dust (EAFD) and slag (EAFS). These wastes are not only rich in base metals but also contain critical elements, which have attracted increasing scientific and industrial interest. Depending on the waste type, key metals such as zinc (from EAFD) and chromium, vanadium, and titanium (from EAFS) are targeted for recovery. This review examines the chemical and phase compositions of these wastes, various leaching techniques (often combined with pretreatment stages), and methods for final metal recovery, either in their pure form or as compounds. Key challenges in hydrometallurgical routes include chloride contamination, the dissolution of refractory zinc ferrite, and impurity management. Despite current limited industrial adoption, hydrometallurgical approaches show significant promise as efficient and environmentally friendly solutions for resource recycling, offering high-purity metal recovery. Full article

Snack foods (e.g., extruded flour-based products) are widely favored by consumers because of their convenience, affordability, and time-saving attributes. However, with the growing demand for high-quality snacks, several challenges have emerged that hinder industry development, such as relatively underdeveloped industrial standards, limited raw material diversity (primarily starch and soy protein), and, consequently, insufficient nutritional value. In this study, a novel type of puffed snack was developed using Alaskan pollock surimi and wheat flour using extrusion puffing technology. The effects of varying ratios of surimi to wheat flour (0:10, 1:9, 2:8, 3:7, and 4:6, which served as SFBC, SFB1, SFB2, SFB3, and SFB4, respectively), on the physicochemical properties, apparent morphology, microstructure, thermal stability, and protein structure of spicy strips were systematically investigated, and the interaction between extruded protein and flour mixtures was analyzed. The results indicated that increasing the proportion of surimi led to decreases in hardness, elasticity, and chewiness, whereas the moisture content and water solubility index increased. The maximum expansion rate (202.2%) was observed in the SFB1 sample. Morphological and microstructural observations further revealed that a higher surimi content resulted in a denser internal structure and a reduced degree of puffing. The protein distribution was relatively uniform, with large pores. Moreover, increased surimi content increased the proportion of immobilized water and improved the thermal stability. These findings provide valuable insights into starch–protein-complex-based extrusion puffing technologies and contribute to the development of innovative surimi-based puffed food products. Full article

This paper explores the application of deep learning to automate the traditionally manual creation of inspection recipes for machine vision scenarios requiring complex region selection, such as those found in semiconductor manufacturing. Manually selecting and cropping functional regions in ultra-high-resolution images for analysis and inspection can take anywhere from tens of minutes to hours. To address this challenge, we propose a model whose encoder integrates atrous convolution into a transformer architecture for better feature extraction. This approach is designed to improve segmentation accuracy while maintaining efficiency in processing large-scale semiconductor images. By automating the selection and cropping process, the proposed method aims to streamline quality inspection workflows, reduce manual labor, and accelerate automated optical inspection. Experimental results demonstrate that the model achieves high segmentation performance, with segmentation accuracy reaching $98\%$ and a faster model inference, making it a practical and effective solution for enabling large-scale automation in semiconductor inspection. This research highlights the potential of deep learning-based methods to transform inspection processes, ensuring higher efficiency and product quality across semiconductor manufacturing industries. Full article

Traditional crosslinked-polyethylene (XLPE) insulation suffers from high recycling costs and low efficiency due to its thermosetting properties. In contrast, thermoplastic polypropylene (PP), with advantages of melt recyclability, low energy consumption, and excellent comprehensive performance, has emerged as an ideal alternative to XLPE. This study conducts a comparative analysis of low-voltage cables insulated with PP, silane-crosslinked XLPE (XLPE-S), and UV-crosslinked XLPE (XLPE-U), focusing on production processes, mechanical properties, thermal stability, and electrical performance. Tensile test results show that PP exhibits the highest elongation at break (>600%) before aging, and its tensile strength (>20 MPa) after aging outperforms that of XLPE, indicating superior flexibility and anti-aging capability. PP exhibits a lower thermal elongation (<50%) at 140 °C compared to XLPE, and its high-crystallinity molecular structure endows better heat-resistant deformation performance. The volume resistivity of PP reaches 9.2 × 10¹⁵ Ω·m, comparable to that of XLPE-U (3.9 × 10¹⁵ Ω·m) and significantly higher than XLPE-S (3.0 × 10¹⁴ Ω·m). All three materials pass the 4-h voltage withstand test, confirming their satisfied insulation reliability. PP-insulated low-voltage cables demonstrate balanced performance in production efficiency, energy consumption cost, mechanical toughness, and electrical insulation. Notably, their recyclability significantly surpasses traditional XLPE, showing potential to promote green upgrading of the cable industry and providing a sustainable insulation solution for low-voltage power distribution systems. Full article

This study aims to analyze the technical and economic infrastructure of sheep farming enterprises operating in Türkiye. It assesses the demographic characteristics of enterprise owners, enterprise scales, production objectives, marketing strategies, and economic performance. Primary data were collected through face-to-face surveys conducted with 201 sheep farming enterprises during the 2023 production period. The sample was selected based on information provided by the Provincial Directorates of Agriculture and Forestry and the Breeding Sheep and Goat Breeders’ Associations. Data analysis was performed using SPSS 27. Categorical data related to enterprise characteristics and the demographic profiles of enterprise owners were examined. The findings indicate that the majority of enterprise owners are middle-aged or older individuals, have a low level of education, and operate predominantly within an extensive production system. The producers’ marketing methods rely mainly on direct sales. In conclusion, ensuring the sustainability of the sheep farming sector requires encouraging young producers to enter the industry, expanding educational programs, and adopting modern production techniques. Full article

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

This study presents a novel environmentally-friendly process for the selective extraction and enrichment of DHA/EPA-containing phospholipids (PL-DHA/EPA) from krill oil. The methodology leverages differential crystallization behavior between phospholipids and triacylglycerols in ethanolic solutions, exploiting their distinct freezing point thresholds to achieve precise fractionation. Response surface methodology optimization identified optimal extraction parameters: liquid-to-material ratio of 6:1 (v/w), freezing temperature of −20 °C, freezing duration of 25 h, and rotary evaporation temperature of 45 °C, yielding a final product with 39.40% PL-DHA/EPA content. Principal component analysis revealed substantial overlap in confidence ellipses among extraction methodologies, indicating effective preservation of core phospholipid signatures from the parent krill oil while maintaining critical structural characteristics and molecular species distribution. Comprehensive analysis of phospholipid fractions and heatmap analysis revealed distinctive molecular profiles compared to conventional organic solvent extraction, with selective enrichment of EPA-containing phospholipids, particularly PC-EPA and PI-EPA species. The green extraction method demonstrated comparable oxidative stability to conventional approaches, with superior protection against secondary oxidation as evidenced by significantly lower anisidine values. This sustainable approach achieves effective phospholipid enrichment while substantially reducing environmental impact through elimination of halogenated solvents, addressing the critical need for environmentally conscious technologies in marine lipid processing with potential applications in nutraceutical and functional food industries. Full article

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R² and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R² of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture. Full article

Cryogenic upgrading represents a promising route for the production of high-purity biomethane, aligning with current decarbonization goals and the increasing demand for renewable gases. This review provides a critical assessment of cryogenic technologies applied to biogas purification, focusing on process fundamentals, technological configurations, energy and separation performance, and their industrial integration potential. The analysis covers standalone cryogenic systems as well as hybrid configurations combining cryogenic separation with membrane or chemical pretreatment to enhance efficiency and reduce operating costs. A comparative evaluation of key performance indicators—including methane recovery, specific energy demand, product purity, and technology readiness level—is presented, along with a discussion of representative industrial applications. In addition, recent techno-economic studies are examined to contextualize cryogenic upgrading within the broader landscape of CO₂ separation technologies. Environmental trade-offs, investment thresholds, and sensitivity to gas prices and CO₂ taxation are also discussed. The review identifies existing technical and economic barriers, outlines research and innovation priorities, and highlights the relevance of process integration with natural gas networks. Overall, cryogenic upgrading is confirmed as a technically viable and environmentally competitive solution for biomethane production, particularly in contexts requiring liquefied biomethane or CO₂ recovery. Strategic deployment and regulatory support will be key to accelerating its industrial adoption. The objectives of this review have been met by consolidating the current state of knowledge and identifying specific gaps that warrant further investigation. Future work is expected to address these gaps through targeted experimental studies and technology demonstrations. Full article

Background: Recently, extensive use of antibiotics has increased the amount of antibiotic residues in the natural water environment. Methods: This study presents an experimental investigation into the degradation of penicillins, tetracyclines, streptomycin and chloramphenicol in aqueous solutions when exposed to 1 MeV accelerated electrons with doses of 0.1, 1, 3 and 7 kGy using HPLC-HRMS analysis. Results: It was found that electron beam irradiation with a dose of 7 kGy ensures 98–99% removal of antibiotics, with the initial concentrations ranging from 15 mg/L to 30 mg/L depending on the class of antibiotic. The mathematical model proposed in the study, which estimates the dose dependencies of the relative concentrations of antibiotics and their degradation products in aqueous solutions, reveals different decomposition rates of antibiotics of different classes due to the different radiosensitivities of antibiotics. It has been found that tetracycline has a considerably higher radiation–chemical yield compared to the other antibiotics when exposed to accelerated electrons. Conclusions: Using density functional theory in combination with the mathematical model, we have developed a novel approach to establishing a quantitative irradiation marker of antibiotic degradation as a result of irradiation, which involves finding the degradation product whose formation requires a minimum number of ionization events. Using such an approach, it is possible to establish the extent of antibiotic degradation in water after irradiation with different doses and find the optimal irradiation doses for industrial water treatment. Full article