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The application of galvanized steel is widespread across industries due to its protective zinc coating that protects against atmospheric corrosion. However, previous studies have primarily focused on long-term corrosion rates rather than the full-scale corrosion behavior of the zinc. This paper aims to study the full-scale corrosion evolution of galvanic steel under simulated marine atmospheric environment using real-time EIS measurement.

Electrochemical impedance spectroscopy (EIS) provides an advanced method in monitoring such behavior. Therefore, the EIS method has been used to conduct a comprehensive investigation on the corrosion behavior of galvanic steel in a full-time manner.

The results indicate that the corrosion process of galvanic steel can be divided into three stages: an initial stage with an increased corrosion rate, a subsequent stage with a reduced corrosion rate, and finally a third stage with the lowest and constant corrosion rate. The evolution of corrosion resistance is closely related to changes in composition and structure of the patina layer. In the initial stage, galvanized steel undergoes the formation of soluble ZnCl₂ and needle-like Zn₅(OH)₈Cl₂·H₂O, which promotes the generation and maintenance of an electrolyte layer, consequently leading to an increase in corrosion rate. With prolonged corrosion time, there is a continuous accumulation of Zn₅(OH)₈Cl₂·H₂O within the patina layer, which reduces the content of soluble components and promotes the development of a denser inner layer, thus enhancing corrosion resistance.

This work holds significance in the monitoring of corrosion, understanding the evolution of corrosion and predicting the lifespan of galvanized steel.

This article presents findings on the relationship between social interactions in remote learning environments and the perceived effectiveness of remote learning. Specifically, it examines the impact of teacher-student interactions and student-student interactions on perceived effectiveness of remote learning, with a focus on how students’ attitudes towards remote education mediate this relationship. Additionally, it explores the moderating effects of cultural context and study form (full-time vs part-time) on these dynamics.

A quantitative research design was used, employing a structured survey questionnaire to collect data from a diverse group of students from Poland, India, Indonesia and the UK. The study involved 1,883 students and analysed the data using statistical methods to assess both mediation and moderation effects. The study employed data obtained from a survey of universities in four countries conducted between 2021 and 2022.

The results emphasise the significant influence of both teacher-student and student-student interactions on students’ perceptions of the effectiveness of remote learning. Positive attitudes towards remote learning were found to mediate this relationship, amplifying the beneficial effects of social interaction. Furthermore, the study reveals that cultural context and study form moderate these relationships, with varying impacts observed across different cultural backgrounds and study arrangements. The study has implications for theory, research, policy implementation and practice in improving education programs.

The comparative analysis included only four countries, which may have affected the overall representativeness of the results. Because the study is limited to students from Poland, India, Indonesia and the UK, the findings may not fully capture the diversity of remote learning experiences across other cultural and socioeconomic contexts. As a result, there may be challenges in generalising these findings to all higher education settings globally. Future research is recommended to include a more extensive sample from additional countries and regions to validate the current results and enhance their generalisability. Therefore, it is worth continuing research in this area, considering more countries and potential moderating factors.

This research contributes original insights into the dynamics of remote learning during a global crisis, offering an understanding of how social interactions, student attitudes and contextual factors shape perceived effectiveness. These findings provide critical guidance for educators, policymakers and institutions seeking to optimise remote education strategies in diverse cultural and educational settings.

This research focuses on developing a dual-nozzle slurry-based extrusion 3D printer capable of fabricating intricate zirconia structures. The designed 3D printer combines material extrusion and photopolymerization technologies to improve material diversity, precision and cost-effectiveness.

The 3D printer design incorporates ultraviolet curing to instantly cure extruded zirconia slurry thereby, eliminating the need for a step-wise curing procedure. Printing parameters were optimized to achieve high-quality prints, and supports made of polyethylene terephthalate glycol were used for intricate geometries. The printability and mechanical properties were evaluated for two different zirconia slurry compositions: 70 / 30 and 80 / 20 powder-to-resin weight percentages. The printed green body was subjected to a two-phase sintering process.

The 3D printer fabricated structures with features subtending angles greater than 50 degrees and a filling density above 80% without any supports. Shrinkage analysis showed the 80 / 20 composition resulted in higher density parts, with shrinkage ratios of 25.23%, 26.23% and 27.26% along the X, Y and Z axes, respectively. The sintered objects displayed hardness (1525 HV) and flexural strength (117 MPa), with minimal porosity.

This study demonstrates the development of a cost-effective dual-nozzle 3D printer that can effectively fabricate functional parts with complex material compositions and geometries that can cater to the futuristic requirements of high-end industries.

This study reviews the literature that discusses the technologies that will influence fit customization (FC). The purpose of this study is to clearly understand the current progress of such technology and the potential future development path.

Research papers, publications and websites of well-known apparel companies using FC are reviewed to explore the latest technological advancements in apparel FC.

Given the advances in body measurement, pattern and fit assessment technologies, FC represents a sustainable production approach for the apparel industry.

This review examines the state of the field, looks at possible trends in the study and highlights future directions.

Although the deployment of diverse brand capabilities is essential for business-to-business (B2B) firms to adapt to changing industrial market demands, the understanding of how brand ambidexterity (BA) influences brand performance (BP) remains limited. This study aims to investigate the importance of BA in relation to BP, analyzing the mediating effect of buyer dependence (BD) and the moderating effect of the supplier’s degree of digital use (DDU) within B2B firms.

Drawing on resource dependence theory (RDT) and the literature on BA and BD, the authors developed a theoretical research model. The authors surveyed 261 pairs of firms, including suppliers and their buyers in China. After the data were collected, the authors used partial least squares structural equation modeling to analyze the data.

The findings of the study indicate that BA significantly and positively influences BP in B2B firms. Furthermore, both buyer relationship value dependence (RVD) and switching cost dependence (SCD) mediate the relationship between supplier’s BA and BP. In addition, the supplier’s DDU positively moderates the relationship between BA and buyer RVD; however, its effect on SCD is not significant.

To the best of the authors’ knowledge, this study is among the first to reveal the underlying mechanism by which BA is leveraged by B2B firms to enhance BP. In addition, by adopting a dual supplier-buyer perspective, the findings provide valuable insights for managers seeking to understand the influence of BA on interorganizational relationships.

This study aims to focus on optimization of process parameters for porosity and strength of polyamide porous bone scaffolds fabricated via selective laser sintering (SLS) process.

Taguchi’s design of experiment approach with L18 orthogonal array (OA) has been used to optimize the process parameters. Five process and four response parameters have been considered for this study. Initially, minimum size of the pores that can be depowdered was identified. Then, porous CAD models of test specimen to measure porosity and strength were designed in Solidworks® software and fabricated using EOSINT P395 m/c. Signal-to-noise ratio and analysis of variance were used to identify the optimal levels of parameters and statistical significance of the parameters.

Among five parameters, powder refresh rate, build chamber temperature and layer thickness were found to have significant influence on all the response parameters, whereas build orientation and build position were found insignificant for all the responses. The Taguchi’s confirmation test validated the results of the study with maximum deviation of 5.8% for compressive strength. Comparison of predicted and experimental values revealed a satisfactory predictability of all the developed linear regression models.

This study reveals optimal set of parameters for SLS of the polyamide porous bone scaffolds. The optimal set of parameters may be used by other researchers to get enhanced combination of strength and porosity while fabricating porous scaffolds.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

This study aims to comprehensively analyze the current developments and applications of paper-based electrochemical platforms for blood glucose detection, focusing on their potential to revolutionize point-of-care testing through cost-effective and accessible diagnostic solutions.

The review systematically examines fundamental principles of paper-based platforms, including substrate properties, fluid transport mechanisms and electrochemical detection methods. It critically evaluates recent technological advances in materials science, fabrication techniques and signal amplification strategies while analyzing various case studies demonstrating successful implementations.

Recent innovations in paper-based glucose sensors have achieved remarkable performance metrics, with detection limits reaching sub-millimolar ranges and response times within seconds. The integration of nanomaterials, particularly graphene-based composites and carbon nanotubes, has significantly enhanced sensor sensitivity and stability. Advanced enzyme immobilization techniques using layer-by-layer assembly have demonstrated sustained activity for up to 10 weeks, while novel signal amplification strategies incorporating bimetallic nanoparticles have pushed detection limits into the sub-picogram range.

This review uniquely synthesizes the latest developments in paper-based electrochemical glucose sensing, providing critical insights into the synergistic integration of advanced materials, fabrication methods and detection strategies. It offers valuable perspectives on overcoming current technical challenges and highlights emerging opportunities in smart device integration and artificial intelligence applications, serving as a comprehensive resource for researchers and practitioners in the field of point-of-care diagnostics.

The purpose of this study is to evaluate and address the energy efficiency prevalent within the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) countries, a diverse group that constitutes a significant portion of the global gross domestic product. By assessing energy efficiency, the study aims to inform strategies that foster sustainable economic growth and environmental sustainability within these nations.

This research uses a three-stage slacks-based measure data envelopment analysis (SBM-DEA) model, enhanced by stochastic frontier analysis (SFA), to assess the energy efficiency of CPTPP countries over the period from 2000 to 2015. The three-stage SBM-DEA model allows for a comprehensive analysis by first measuring energy efficiency, then adjusting for external environmental factors and statistical noise through SFA, and finally, reevaluating efficiency with adjusted inputs.

The analysis reveals significant disparities in energy efficiency among CPTPP nations, identifying both high-performing countries and those needing considerable improvements. Developed countries within the CPTPP generally exhibit higher energy efficiency levels, which can be attributed to their adoption of advanced technologies and strong policy frameworks. In contrast, developing nations demonstrate greater vulnerability to external environmental factors affecting their energy efficiency.

The research fills a gap in the literature by providing a nuanced and comprehensive evaluation of energy efficiency across a significant and economically diverse group of nations, offering valuable insights for sustainable development within the CPTPP framework.

The research in this paper aims to investigate the development of Library and Information Science in Chinese universities. Specifically, it focuses on understanding the spatial and temporal aspects of subject knowledge output and providing a more comprehensive explanation of the imbalance in subject research.

This study applies the bibliometric method to analyze 131,112 papers published by 51 universities in mainland China from 1977 to 2021, as recorded in the Chinese Social Sciences Citation Index (CSSCI). The study classifies the evolution trends of the discipline and quantifies the published article data of the universities using the index of published articles. Additionally, it examines the development status, structural situation, influencing factors and prospects of universities in different categories and regions.

The field of Library and Information Science is gaining momentum in Chinese universities, but there are significant differences in its development. While the relative gap among universities in a regional context is diminishing, the absolute gap in the category perspective is increasing. The development of Library and Information Science is influenced by various factors, including the academic environment, geographical position, scientific research projects and academic traditions. The uneven development of the discipline is maintained in the short term.

This paper proposes a new quantitative index of discipline development, the university publication index. This index allows for an examination of the temporal and spatial trends of discipline development using domestic universities as the subject of research. The paper presents an overview of discipline development through four aspects: academic participation practice, discipline governance mechanisms, education and teaching systems and discourse construction within the discipline. The theoretical support provided by this study can help facilitate innovative development in the discipline.