Search Results (5,430)

The production of iron and steel plays a significant role in the anthropogenic carbon footprint, accounting for 7% of global GHG emissions. In the context of CO₂ mitigation, the steelmaking industry is looking to potentially replace traditional carbon-based ironmaking processes with hydrogen-based direct reduction of iron ore in shaft furnaces. Before industrialization, detailed modeling and parametric studies were needed to determine the proper operating parameters of this promising technology. The modeling approach selected here was to complement REDUCTOR, a detailed finite-volume model of the shaft furnace, which can simulate the gas and solid flows, heat transfers and reaction kinetics throughout the reactor, with an extension that describes the whole gas circuit of the direct reduction plant, including the top gas recycling set up and the fresh hydrogen production. Innovative strategies (such as the redirection of part of the bustle gas to a cooling inlet, the use of high nitrogen content in the gas, and the introduction of a hot solid burden) were investigated, and their effects on furnace operation (gas utilization degree and total energy consumption) were studied with a constant metallization target of 94%. It has also been demonstrated that complete metallization can be achieved at little expense. These strategies can improve the thermochemical state of the furnace and lead to different energy requirements. Full article

This study explores the challenges and theoretical transformations that the widespread application of AI technology in social governance brings to the protection of citizens’ fundamental rights. By examining typical cases in judicial assistance, technology-enabled law enforcement, and welfare supervision, it explains how AI characteristics such as algorithmic opacity, data bias, and automated decision-making affect fundamental rights including due process, equal protection, and privacy. The article traces the historical evolution of privacy theory from physical space protection to informational self-determination and further to modern data rights, pointing out the inadequacy of traditional rights-protection paradigms in addressing the characteristics of AI technology. Through analyzing AI-governance models in the European Union, the United States, Northeast Asia, and international organizations, it demonstrates diverse governance approaches ranging from systematic risk regulation to decentralized industry regulation. With a special focus on China, the article analyzes the special challenges faced in AI governance and proposes specific recommendations for improving AI-governance paths. The article argues that only within the track of the rule of law, through continuous theoretical innovation, institutional construction, and international cooperation, can AI technology development be ensured to serve human dignity, freedom, and fair justice. Full article

The geometric design of flow channels in bipolar plates is one of the critical features of proton exchange membrane fuel cells (PEMFCs), as it determines the power output of the fuel cell and has a significant impact on its performance and durability. The function of the bipolar plate is to guide the transfer of reactant gases to the gas diffusion layer and catalytic layer inside the PEMFC, while removing unreacted gases and gas–liquid byproducts. Therefore, the design of the bipolar plate flow channel is directly related to the water and thermal management of the PEMFC. In order to improve the comprehensive performance of PEMFCs and ensure their safe and stable operation, it is necessary to design the flow channels in bipolar plates rationally and effectively. This study addresses the limitations of existing bipolar plate flow channels by proposing a new coupling of serpentine and radial channels. The distribution of oxygen, water concentrations, and temperature inside the channel is simulated using the multi-physics simulation software COMSOL Multiphysics 6.0. The performance of this novel design is compared with conventional flow channels, with a particular focus on the pressure drop and current density to evaluate changes in the output performance of the PEMFC. The results show that the maximum current density of this novel design is increased by 67.36% and 10.43% compared to straight channel and single serpentine channels, respectively. The main contribution of this research is the innovative design of a new coupling of serpentine and radial channels in bipolar plates, which improves the overall performance of the PEMFC. This study provides theoretical support for the design of bipolar plate flow channels in PEMFCs and holds significant importance for the green development of energy. Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Sustainability has emerged as a prominent concern globally, extending its influence into various domains, including education. It is recognized as of utmost importance to address global environmental challenges. However, there is a critical gap in the perception of innovative teaching strategies, i.e., interdisciplinary collaboration, experiential learning, and targeted approaches, to improve sustainability literacy and its applications. This review analyzes existing environmental and sustainability education frameworks and approaches to determine desired learning outcomes and challenges associated with sustainability education. Also, it explores and identifies concepts, theories, and assumptions found within the literature review, promoting sustainability integration within engineering education. The review was conducted to facilitate the development and improvement of sustainability education within the Architectural Engineering discipline, a field known for emphasizing educational innovation and technical excellence. By synthesizing existing ideas related to sustainability and sustainable development, this work aims to guide curriculum designers and educators in fostering sustainability competencies among engineering students within the built environment. Full article

The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO₂ eq kg⁻¹ of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. Full article

Electrospun nanofiber membranes (ESNFMs) are exceptional biomaterials for tissue engineering, closely mimicking the native extracellular matrix. However, their inherent fragility poses significant handling, processing, and integration challenges, limiting their widespread application in advanced 3D tissue models and biofabricated devices. This study introduces a novel and on-mat framing technique utilizing extrusion-based printing of a UV-curable biocompatible resin (Biotough D90 MF) to create rigid, integrated support structures directly on chitosan–polyethylene oxide (PEO) ESNFMs. We demonstrate fabrication of these circular frames via precise 3D printing and a simpler manual stamping method, achieving robust mechanical stabilization that enables routine laboratory manipulation without membrane damage. The resulting framed ESNFMs maintain structural integrity during subsequent processing and exhibit excellent biocompatibility in standardized extract assays (116.5 ± 12.2% normalized cellular response with optimized processing) and acceptable performance in direct contact evaluations (up to 78.2 ± 32.4% viability in the optimal configuration). Temporal assessment revealed characteristic cellular adaptation dynamics on nanofiber substrates, emphasizing the importance of extended evaluation periods for accurate biocompatibility determination of three-dimensional scaffolds. This innovative biofabrication approach overcomes critical limitations of previous handling methods, transforming delicate ESNFMs into robust, easy-to-use components for reliable integration into complex cell culture applications, barrier tissue models, and engineered systems. Full article

As a crucial reactive oxygen species, hydrogen peroxide (H₂O₂) serves as both a physiological regulator and a pathological indicator in human systems. Its urinary concentration has emerged as a valuable biomarker for assessing metabolic disorders and renal function. While conventional colorimetric determination methods predominantly employ enzymatic or nanozyme catalysts, we present an innovative non-catalytic approach utilizing the redox-responsive properties of organic neutral radicals. Specifically, we designed and synthesized a novel radical TTM-DMODPA based on the tris (2,4,6-trichlorophenyl) methyl (TTM) scaffold, which exhibits remarkable optical tunability and oxidative sensitivity. This system enables dual-mode H₂O₂ quantification: (1) UV-vis spectrophotometry (linear range: 2.5–250 μmol/L, LOD: 1.275 μmol/L) and (2) smartphone-based visual analysis (linear range: 2.5–250 μmol/L, LOD: 3.633 μmol/L), the latter being particularly suitable for point-of-care testing. Validation studies using urine samples demonstrated excellent recovery rates (96–104%), confirming the method’s reliability for real-sample applications. Our work establishes a portable, instrument-free platform for urinary H₂O₂ determination, with significant potential in clinical diagnostics and environmental monitoring. Full article

The following investigation presents concrete cement obtained with the Viterbo O’Reilly Diaz method, introduced to quantify the concrete mixture by using an aggregate gradation method. This research uses this procedure to decrease the amount of cement in the mix, thus reducing the CO₂ footprint and production costs, which directly impact the environmental and economical sustainability of the material. The formulations used structural and general use Portland cements. As aggregates, fine sand and 3/4” gravel were included. Several characterization techniques were used, including granulometry testing for the aggregates, compression strength testing for the concrete samples, and granulometry testing for the raw materials. Compressive tests were conducted on samples after 28 days of curing, while scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS) was used to understand the microstructure. The results revealed the optimal amounts of water, cement, and aggregates. Combinations of fine and coarse aggregates were determined as well. The main novelty in this manuscript is the use of the Viterbo O’Reilly mix design method to innovatively enhance concrete mixes by analyzing material properties and behavior in detail, an unexplored method in the literature. This research considers not only strength but also durability and workability, using mathematical tools for data analysis. This data-driven approach ensures effective aggregate gradation towards sustainability when compared to other traditional methods. Full article

Blended and virtual learning have become an integral part in international higher education, especially in the wake of the COVID-19 pandemic and the European Union’s Digital Education Action Plan. These modalities have enabled more inclusive, flexible, and sustainable forms of international collaboration, such as Collaborative Online International Learning (COIL) and Blended Intensive Programs (BIPs), reshaping the landscape of global academic mobility. This paper introduces the INVITE Learning Design Framework (LDF), developed to support higher education instructors in designing high-quality, internationalized blended and virtual learning experiences. The framework addresses the growing need for structured, theory-informed approaches to course design that foster student engagement, intercultural competence, and motivation in non-face-to-face settings. The INVITE LDF was developed through a rigorous scoping review of existing models and frameworks, complemented by needs-identification analysis and desk research. It integrates Self-Determination Theory, Active Learning principles, and the ADDIE instructional design model to provide a comprehensive, adaptable structure for course development. The framework was successfully implemented in a large-scale online training module for over 1000 educators across Europe. Results indicate that the INVITE LDF enhances educators’ ability to create engaging, inclusive, and pedagogically sound international learning environments. Its application supports institutional goals of internationalization by making global learning experiences more accessible and scalable. The findings suggest that the INVITE LDF can serve as a valuable tool for higher education institutions worldwide, offering a replicable model for fostering intercultural collaboration and innovation in digital education. This contributes to the broader transformation of international higher education, promoting equity, sustainability, and global citizenship through digital pedagogies. Full article

Pyrolysis technology currently serves as a significant method for recycling and reducing waste tires. In this paper, in order to improve the heat transfer efficiency during the pyrolysis of waste tires in a horizontal rotary furnace and the yield of pyrolysis oil, the effect laws of tire particle size, rotary furnace rotation speed, enhanced heat transfer materials, and adding spiral fins on heat transfer performance and pyrolysis product distribution were studied, respectively. The innovation lies in two aspects: first, aiming at the problems of slow heat transfer and low pyrolysis efficiency in horizontal rotary furnaces, we identified technical measures through experiments to enhance heat transfer, thereby accelerating pyrolysis and reducing energy consumption; second, with the goal of increasing high-value pyrolysis oil yield, we determined optimal operating parameters to improve economic and sustainability outcomes. The results showed that powdered particles of waste tires were heated more evenly during the pyrolysis process, which increased the overall heat transfer coefficient and the proportion of liquid products. When the rotational speed of the rotary pyrolysis furnace exceeded 2 rpm, there was sufficient contact between the material and the furnace wall, which was beneficial to the improvement of heat transfer performance. Adding heat transfer enhancement materials such as carborundum and white alundum could improve the heat transfer performance between the pyrolysis furnace and the material. Notably, a rotational speed of 3 rpm and carborundum were used as a heat transfer enhancement material with powdered waste tire particles during the pyrolysis process; the overall heat transfer coefficient was the highest, which was 16.89 W/(m²·K), and the proportion of pyrolysis oil products was 46.1%. When spiral fins were installed, the comprehensive heat transfer coefficient was increased from 12.78 W/(m²·K) to 16.32 W/(m²·K). The experimental results show that by increasing the speed of the pyrolysis furnace, adding heat transfer enhancing materials with high thermal conductivity to waste tires, and appropriate particle size, the heat transfer performance and pyrolysis rate can be improved, and energy consumption can be reduced. Full article

This study investigates the encroachment risk in the Makause informal settlement by analysing resident survey data to identify key contributing factors and build predictive models. Encroachment threatens the water infrastructure through damage, contamination, and service disruptions, highlighting the need for informed, community-based planning. The data was collected from 105 residents, with responses (“Yes,” “No,” “Unsure”) analysed using descriptive statistics and a one-way ANOVA to identify significant differences across categories. The ReliefF algorithm was used to rank the importance of features predicting the encroachment risk. These inputs were then used to train, validate, and test an Artificial Neural Network (ANN) model. The Artificial Neural Network demonstrated a high predictive accuracy, achieving correlation coefficients above 95% and low mean squared errors. The ANOVA identified statistically significant mean differences for selected variables, while ReliefF helped determine the most influential predictors. A high agreement level (p > 0.900) between predicted and actual responses confirmed the model’s validity. This research introduces an innovative, data-driven framework that integrates machine learning and a statistical analysis to support municipalities and utility providers in engaging informal communities to protect infrastructure. While this study is limited to Makause and may be affected by a self-reported bias, it demonstrates the potential of Artificial Neural Networks and ReliefF in enhancing the risk analysis and infrastructure management in informal settlements. Full article

This study aims to map the scientific knowledge of bank profitability and its determinants. It identifies trends and gaps in existing research through a bibliometric analysis. To this end, 634 documents published in the Web of Science database over the last 54 years were analyzed using the bibliometric package. The results indicate an increase in the volume of publications following the 2008 financial crisis, focusing on analyzing the factors influencing bank profitability and economic growth. The Journal of Banking and Finance is the preeminent publication in this field. The literature reviewed shows that bank profitability depends on internal factors (size, credit risk, liquidity, efficiency, and management) and external factors (such as GDP, inflation, interest rates, and unemployment). In addition to the traditional determinants, the recent literature highlights the importance of innovation and technological factors such as digitalization, mobile banking, and electronic payments as relevant to bank profitability. ESG (environmental, social, and governance) and governance indicators, which are still emerging but have been extensively researched in companies, indicate a need for evidence in this area. This paper also provides relevant insights for the formulation of monetary policy and the strategic formulation of banks, helping managers and owners to improve bank performance. It also provides directions for future empirical studies and research collaborations in this field. Full article

In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation. Full article