Search results

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.

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.

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 paper is to mesoscopically analyze the impact of parameter variations in the random pore structure on the stress distribution of layered-porous sintered silver used in high-power electronics, and to conduct a variable importance analysis of the parameter variations in the random pore structure.

Sintered silver, featuring a porous structure, improves thermal and mechanical performance by effectively absorbing stress and facilitating heat dissipation. To ensure the performance and scalability of layered-porous sintered silver, this paper uses Gaussian random fields to model the random pore structure and performs a sensitivity analysis on pore characteristic length and porosity, both of which significantly impact the stress distribution within the sintered silver layer. First, multiple sets of random pore models with varying characteristic lengths and porosities were generated using Gaussian random fields. Then, the maximum stress of the sintered silver layer containing random pores under power cycling conditions was extracted. Finally, the Morris screening method was used to perform a sensitivity analysis on the variables of the random pore structure that affect the maximum stress in the sintered silver layer. The systematic evaluation of the parameter variations in the random pore structure was conducted to assess their impacts on the maximum stress in the sintered silver layer.

Due to the high randomness of the pore structure generated by the Gaussian random field function, the maximum stress in the sintered layer fluctuates with different mesoscopic models. After systematic evaluation using the Morris screening method, it was found that the maximum stress in the sintered silver layer is most sensitive to the variation in the pore characteristic length in the x-direction. Reducing the length of pores in the x-direction can significantly decrease the stress concentration between pores in the sintered silver layer after power cycling.

This paper innovatively uses a Gaussian random field to model the mesoscopic structure of layered-porous sintered silver for high-power electronics, and applies the Morris screening method to perform variable importance analysis on the stress distribution results within the sintered silver layers. The mesoscopic study demonstrates that the maximum stress in the sintered silver layer is most sensitive to changes in the pore characteristic length in the x-direction.

This paper aims to develop a flexible manufacturing method for multimaterial sand molds to realize efficient additive manufacturing of multimaterial sand molds.

To study the influence of multimaterial sand laying process parameters on the quality of powder bed and optimize the design of multimaterial sand laying device. Numerical simulation and X-ray Computed Tomography are used to study the penetration behavior and curing morphology of resin in different sand particles.

The surface roughness and porosity of the multimaterial powder bed that meet the requirements of sand-based additive manufacturing can be obtained under the optimal printing process, that is, the sanding speed of 140.0 mm/s and sanding roller diameter of 15.0 mm. The resin penetration process of the multimaterial sand molds shows a pattern of transverse expansion and longitudinal penetration. In terms of the resin curing morphology, the maximum thickness of the resin film layer of zircon sand reaches 30.5 ± 1.0 µm, which has the best tensile property, followed by silica sand and the thinnest resin film layer of chromite sand.

In this work, a highly flexible integrated combined sand-laying device suitable for multimaterial sand-laying tests is developed, which can obtain a multimaterial powder bed that meets the needs of sand additive manufacturing. Subsequent casting print tests also verify that the program can meet the needs of multimaterial sand mold additive manufacturing.

Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co₃O₄ is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.

Herein, Co₃O₄ cathode material based on carbon cloth (Co₃O₄/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.

The results show that the Co₃O₄/CC material synthesized at 120°C has good performance. Co₃O₄/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.

The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

In the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.

Authors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.

Combining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.

This paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.

This article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.

This study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.