Integration of Mobile Interaction Technologies in Supply Chain Management for S2B2C E-Commerce Platforms

With the rapid development of e-commerce, the supplier to business to consumer (S2B2C) model, as an emerging business model, has become an essential component of modern supply chain management. However, traditional supply chain management models are increasingly inadequate to meet the demands of the fast-changing market and complex supply chain collaboration, particularly in areas such as information sharing, real-time data updates, and demand forecasting. Existing research primarily focuses on the optimization of individual supply chain components, such as inventory management, order tracking, or logistics scheduling, with limited attention given to the collaboration between parties and the overall management of the supply chain under the S2B2C model. Additionally, while some studies have proposed information-sharing mechanisms and demand forecasting models based on mobile platforms, the practicality and accuracy of existing methods are still limited in practical applications due to factors such as data processing capabilities, algorithm accuracy, and the dynamic nature of consumer behavior. Therefore, this study proposes an integrated solution for supply chain management based on mobile interaction technology within the S2B2C e-commerce platform. The aim is to enhance the intelligence, flexibility, and transparency of the supply chain through technological innovation. The core research focuses on the implementation technologies for mobile terminals in supply chain management on the S2B2C e-commerce platform, along with the design and implementation of a demand forecasting model and algorithm based on mobile applications

A Cognitive Load Theory-Based Approach to Integrating Mobile Fragmented Learning Resources

With the rapid development of mobile internet technology, mobile fragmented learning has become a mainstream learning mode due to its convenience and flexibility. However, the vast quantity of learning resources available online often varies in quality and lacks coherent organization, leading to excessive cognitive load and reduced learning efficiency. Existing research primarily focuses on resource integration based on content similarity clustering or user behavior data, while largely overlooking learners’ cognitive characteristics and the dynamic regulation of cognitive load. As a result, current integration methods fail to meet the cognitive needs of fragmented learning. To address this issue, this study proposes a resource integration framework for mobile fragmented learning grounded in cognitive load theory. On one hand, it constructs a cognitively aligned resource analysis model using design structure matrix (DSM) to rank and categorize cognitive load elements in learning materials. On the other hand, it applies DSM-based decoupling and clustering strategies to decompose resources into manageable cognitive units and support personalized aggregation. This dual approach aims to reduce intrinsic cognitive load and enhance the efficiency of resource organization. The findings offer a cognition-informed pathway for mobile learning resource design and hold significant implications for optimizing fragmented learning experiences and advancing mobile learning theory and practice

Evaluating User Experience in Learning Applications among University Students in Nigeria Using UEQ

This study evaluates the user experience (UX) of learning applications among university students in Nigeria using the user experience questionnaire (UEQ). With the rapid shift toward digital and mobile learning platforms in higher education, understanding students’ perceptions of usability, engagement, and overall satisfaction has become crucial. The study surveyed 397 university students to assess six key UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The findings revealed that the learning management system (LMS) users have a positive experience with it and use it frequently. The novelty category, on the other hand, has the lowest moodle mean score. We posit that these results are acceptable since the student aims to access the LMS to learn. The findings provide valuable insights for educators, developers, and policymakers aiming to optimize e-learning applications for improved usability and engagement. This study contributes to the broader conversation on enhancing digital learning experiences in developing regions

A Hybrid Model for Alzheimer’s Disease Classification Based on Neural Network Architectures Enhanced by GAN Model

Alzheimer’s disease (AD) is a neurodegenerative disorder marked by progressive cognitive decline, making early and accurate diagnosis vital for timely intervention. This study explores the efficacy of combining generative adversarial networks (GANs), convolutional neural networks (CNNs), and vision transformers (ViTs) for AD classification using magnetic resonance imaging (MRI) data. GANs were employed to generate synthetic brain images, addressing data scarcity by augmenting the dataset. CNNs were then used for feature extraction, accelerating model training, and mitigating overfitting. These extracted features were subsequently fed into ViTs, known for their ability to capture spatial dependencies in image data. Experimental results demonstrated that the proposed GAN-CNN-ViT fusion model achieved high accuracy (96%) and robustness, outperforming traditional machine learning (ML) and deep learning approaches. GAN-generated synthetic images enhanced dataset generalization, improving ViT performance in distinguishing AD patients from healthy controls. Comparative analyses validated the superiority of this approach over recent methods in AD classification. This framework underscores the potential of deep learning techniques in advancing neuroimaging-based disease diagnosis. It holds significant promise for early AD detection, ultimately contributing to improved patient outcomes and quality of life through the integration of cutting-edge computer vision and ML methodologies in medical applications

Evaluating the Impact of Mobile-Based Generative AI Tools on Visual Design Education

Mobile-based generative artificial intelligence (AI) tools have been increasingly adopted in visual design education. These tools have introduced multidimensional impacts on traditional educational models. However, significant gaps remain in current research regarding their application in visual design education. Existing studies have predominantly addressed the general educational use of generative AI tools, while the specific affordances of mobile platforms and the disciplinary characteristics of visual design have not been sufficiently integrated. Furthermore, research methodologies have largely relied on surveys and interviews, with acceptance modeling limited to surface-level analysis. Some critical factors have been underexplored, resulting in constrained accuracy and comprehensiveness in predicting user acceptance. Centering on the context of visual design education, this study investigated the acceptance of mobile-based generative AI tools among teachers and students. The study was conducted in two parts. First, an acceptance model was constructed by incorporating technological attributes, pedagogical demands, and disciplinary background to identify key influencing factors and their underlying mechanisms. Second, based on the proposed model, scientific prediction methods were employed to dynamically forecast acceptance levels among different types of teachers and students across various teaching stages. This study aims to provide a theoretical foundation for the deep integration of mobile-based generative AI tools in visual design education, thereby supporting the refinement of instructional strategies and fostering the cultivation of high-caliber design professionals equipped to adapt to technological transformation

Development of Innovative Mobile QR-EFI Simulator in Problem-Based Teaching Factory (PBTF) Model to Enhance Students' 4C Skills

This study addresses the urgent need to bridge the competency gap between vocational education graduates and the rapidly evolving demands of the modern automotive industry, particularly in the increasingly complex electronic fuel injection (EFI) systems. Conventional learning approaches have proven insufficient in equipping students with the necessary technical and non-technical skills to adapt to current technologies. Therefore, this study aims to develop and validate a mobile-based EFI simulator integrated with QR code technology, enabling interactive visualization of the components, data flow, operational processes, and diagnostic procedures of the EFI system in an easily understandable manner. The simulator’s effectiveness is further evaluated through its integration within the Problem-Based Teaching Factory (PBTF) model to enhance students’ 4C skills: collaboration, communication, creativity, and critical thinking. Employing a Research and Development (R&D) approach with the 4-D model (Define, Design, Develop, Disseminate), the study involved 50 Automotive Engineering students at FT-UNP, divided into experimental and control groups. Validity analysis using SEM-PLS confirmed high validity of the simulator, especially in visualization aspects, while effectiveness testing showed that the experimental group using the simulator scored significantly higher in 4C skills than the control group. These results confirm that integrating the Mobile QR-EFI Simulator within the PBTF model significantly improves students’ critical and creative skills, bridges the gap between theory and practice in vocational automotive education, and meets the demand for interactive learning tools aligned with modern industry developments

Effectiveness of Gamification in Mobile and Interactive Learning: Analysis of Approaches and Outcome

The use of new approaches in pedagogy, which is partly due to the digitalisation of this process, creates the language for the further evolution of the field. The purpose of the paper is to study the effectiveness of using gamification in the educational process and to investigate different research approaches and their results. The proposed study is based on a systematic review of scientific literature. Certain scientific methods were used: content analysis of professional literature. The results of the study indicate that today the possibilities of combining the traditional use of gamification and the digital environment are actively used. The study findings indicate positive effects of gamification include improved motivation for learning activities, the benefits of developing skills and abilities, the development of communication and teamwork skills, and psychological relief. The difficulty in using gamification is price. It is shown that the importance of implementing various gamification models – role-playing games, story-based learning, quests, simulations, virtual reality, etc. gamification plays a positive role primarily in the motivational component of learning. The conclusions emphasise the further prospect of studying gamification through the prism of its potential evolution. The contribution of the study lies in its systematic review of various gamification approaches in education

AI-Driven Innovations in Adult EFL Learning: Exploring Potentials and Practicalities

In a worldwide world, adults must master numerous languages, which is difficult. Recent brain research reveals that high proficiency can be attained with minimal practice, while artificial intelligence (AI) advances offer targeted and efficient language education. This study analyzes how AI techniques such as active learning, intelligent tutoring, and natural language processing can improve adult EFL acquisition. AI can improve learning, feedback, engagement, motivation, and outcomes compared to traditional techniques. However, interpersonal communication is crucial. The study uses instructor-learner questionnaires to emphasize the cautious acceptance of AI technologies and the need for ethical frameworks and balanced integration with human educators. This study evaluates and compares attitudes, defines the best balance between human-human and human-AI, identifies best practices for ethical risk, age matters, and adult self-determination, and guides AI integration and framework development. Adapting these technologies to decrease risk and maximize autonomy is crucial. For the following few steps, stakeholders and joint research to develop credible AI guidelines for adult EFL teaching are essential. This study examines the views of 30 EFL teachers and 35 adult learners regarding using AI to teach English. The goals are to assess expected benefits, compare attitudes, and define ethical difficulties. Questionnaires examined AI impacts such as technology use, evaluation, and instigating variables. 100% of teachers expected engagement improvements. Student attitudes toward such assessment adoption were neutral, with 57% neutral on its validity. Statistical analysis using analysis of variance (ANOVA) revealed significant differences in attitudes regarding independence restriction and constraint (p < 0.05). AI augments instruction, not replaces it. Including adult learners in AI development, merging AI with traditional approaches, and using AI-augmented pedagogy to promote linguistic competency while addressing contextual and social restrictions are key recommendations

Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy

With the advent of 5G and 6G technologies and the growing ubiquity of the Internet, the Mobile Agent (MA) paradigm is increasingly seen as a promising alternative to the conventional client-server model. MAs, which are software entities capable of moving and processing data across different systems, offer potential efficiencies in data management. However, their operation in dynamic and mobile environments can lead to challenges, such as incomplete or delayed tasks. This study addresses these issues by focusing on reducing the relocation time of MAs. A numerical procedure and a streamlining strategy were developed to expedite the transfer of an agent from the source to the target hub. Utilizing the itinerary design pattern and the Ant Colony Optimization (ACO) algorithm, implemented via the Java Agent Development Framework (JADE), this study sought the most efficient path for the MA. The proposed algorithm demonstrated a significant improvement, selecting the optimal path in just 271.511 seconds. This performance represents a substantial enhancement over previous approaches using the master-slave design pattern with either the Genetic Algorithm (GA) or the Node Compression Algorithm (NCA). The implications of this improvement are far-reaching, potentially enhancing the efficiency and reliability of data management systems in a variety of applications

The Application of Problem-Based Learning in Soft Skills Courses: An Experiment in Classes with Multidisciplinary Students in Vietnam

Soft skills are essential if graduates are to meet the demands of the 21st-century workforce. This represents a major challenge for higher education programs, which need to adopt teaching methods that effectively equip students with these essential skills. This study evaluates the impact of a problem-based learning approach to curriculum design on soft skills for multidisciplinary students. The elective course, which attracts a diverse cohort of students, is delivered in a blended learning format. Using a mixed-methods research approach, the study collected data via questionnaires from 140 multidisciplinary students split between experimental and control groups, supplemented by in-depth interviews conducted after the course. This paper describes a proposed teaching process based on problem-based learning and details the implementation of an experimental lesson on time management as part of the soft skills curriculum. The results indicate that problem-based learning not only enhances the development of soft skills but also encourages student initiative and creativity by improving individual and teamwork skills in both online and face-to-face learning environments. Based on these findings, the study recommends further research to broaden the application of problem-based learning in higher education contexts