A Mobile Technology-Based Framework for Digital Libraries: Bridging Accessibility and Personalized Learning

With the rapid development of mobile technology, digital libraries have become critical platforms for supporting education and academic research. However, traditional digital libraries face significant challenges in terms of accessibility on mobile devices and personalized learning support. In particular, existing technologies and research have yet to comprehensively address the need for meeting diverse reader requirements and enhancing the convenience and flexibility of information access. Against this backdrop, a mobile technology-based digital library framework was proposed in this study, aimed at improving the reader experience and promoting personalized learning through accessibility-assistance tools and personalized learning path recommendation systems. Specifically, the study focuses on two core components: (a) the design of accessibility-assistance tools for mobile digital libraries, ensuring that diverse readers, particularly those with special needs, can easily access information; and (b) the development of personalized learning path recommendation methods, integrating readers’ interest points with learning themes to achieve more precise and intelligent learning support. By integrating mobile technology with intelligent recommendation algorithms, innovative applications within the digital library domain were explored, with the goal of offering new insights into enhancing the quality of digital library services and learning efficiency

Predictive Analytics in Mobile Education: Evaluating Logistic Regression, Random Forest, and Gradient Boosting for Course Completion Forecasting

This study aimed to compare the effectiveness of three predictive algorithms—logistic regression, random forest, and GBM—in predicting course completion using user engagement data from online learning platforms. By analyzing engagement metrics such as session duration, session frequency, and quiz scores, the study sought to identify the most effective model for forecasting course completion, providing insights into which aspects of student behavior were most predictive of success. Logistic regression emerged as the best overall performer, achieving the highest accuracy (52.13%) and F1-Score (56.17%), indicating its balanced approach to predicting course completion and non-completion. Random forest and gradient boosting machines (GBM) showed strengths in specific areas; random forest maintained a good balance between precision and recall, while GBM excelled in recall, identifying students likely to complete courses but with lower precision, leading to more false positives. The findings have practical implications for educational technology, particularly in designing personalized learning paths and targeted interventions to support at-risk students. The study also acknowledged limitations, including the dataset’s focus on engagement metrics without demographic context and the potential for model-specific biases. Future research should explore additional predictive features, larger datasets, and more advanced algorithms to enhance the robustness and applicability of predictive models in real-time educational settings

Exploring the Role of Course Teams in Helping Graduate Teaching Assistants Navigate Day-to-Day Teaching

Graduate teaching assistants (GTAs) significantly contribute to undergraduate education at US universities, particularly in engineering. As novice teachers, they require adequate pedagogical training. This training mostly relies on a few pedagogy-focused workshops or courses taken by all teaching assistants at a university and faculty and peer mentoring. However, GTAs still need relevant course- and context-dependent pedagogical support. Recognizing the importance of such assistance, this study explores the support provided by course teams, comprising the instructor(s) and teaching assistants, to GTAs in navigating day-to-day teaching. Data were collected over a semester-long period in the form of periodic interviews and weekly reflections from seven GTAs teaching different engineering courses at a large US university. Findings suggest that regular interactions with course teams help GTAs participate in a community of practice. This experience helps them more effectively fulfill their day-to-day teaching responsibilities related to course preparation and delivery, and manage teaching tasks alongside other professional and personal responsibilities. Moreover, as GTAs navigate these responsibilities with the help of course teams, they also learn valuable academic skills required of future faculty

Emerging Technologies in Learning: A Bibliometric Analysis of Technology Integration and Applications

Smart learning, a field marked by rapid evolution and innovation, leverages emerging technologies to address modern educational challenges, transforming teaching methodologies and driving significant progress. This study analyzes the impact of key technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and generative AI, between 2010 and 2024 using bibliometric and content analysis methods. Drawing from Scopus and Web of Science (WoS) databases, it highlights how these innovations foster personalized learning environments and dynamic educational content. The findings reveal exponential growth in research on AI and the IoT in education since 2015, with major contributions from Chinese and American researchers. The study profiles influential researchers, leading institutions, and pioneering countries, offering insights into the evolving landscape of smart education. By examining trends and the interplay between technology and educational reform, the paper underscores the importance of data-driven strategies for designing and implementing adaptive learning systems. It also anticipates future challenges and opportunities, proposing a framework to guide ethical integration of these technologies into education, ensuring they enhance global learning outcomes in an increasingly digital world

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

High Performance of LSTM on Dengue Shock Syndrome Detection Using DNA Sequence Encoding Methods

Dengue fever (DF) is a significant global health challenge, affecting approximately 390 million people annually and imposing substantial public health and economic burdens. Accurate DNA sequence classification is crucial for identifying genetic factors in diseases such as DF. However, many machine learning (ML) models for disease detection rely on basic encoding methods such as one-hot encoding, which fail to fully exploit the sequential and contextual nature of DNA data. To address this limitation, this study applies long short-term memory (LSTM) networks, a neural architecture adept at handling sequential data, to classify DNA sequences for detecting dengue and dengue shock syndrome (DSS). The study evaluates three encoding techniques— one-hot encoding, term frequency-inverse document frequency (TF-IDF), and Word2Vec— using datasets of 3,458 DNA sequences sourced from genomics repositories. Preprocessing included the removal of non-ACGT sequences and duplicates to ensure data integrity, followed by under-sampling to address class imbalance. Experimental results demonstrate that the LSTM model with Word2Vec encoding achieved the highest accuracy (0.98), significantly outperforming other encoding techniques. Word2Vec captures contextual and semantic relationships within DNA sequences, enabling superior classification performance. These findings highlight the potential of combining advanced encoding techniques with LSTM networks to improve the accuracy of disease detection models. The study’s approach offers promising implications for genomic diagnostics, particularly in resource-limited settings, and lays the foundation for future research into applying similar methodologies to other diseases or datasets

Two Success Stories as Result of the Horizon Europe Shift-Hub Project

This paper reflects on two success stories emerging from the Shift-Hub project, a publicly funded European Commission initiative. The first success story revolves around the concept and implementation of ‘DemoDays,’ designed to connect supply and demand in smart health innovation. Initially a contractual obligation, the project team chose to take ownership, transforming DemoDays from simple KPIs fulfilment into meaningful, sustainable events. They prioritised demonstrating tangible solutions over mere presentations, focusing on the principle of ‘to see is to believe’. Online format, concise two-hour duration, and a focus on recurring demonstrations contributed to their success. This approach emphasized commitment and risk-taking, contrasting with the common practice of risk aversion and diffused responsibility. The second success story, still in progress, concerns the exploration of smart health ecosystems. Departing from superficial definitions, the team identifies the complex and unique nature of individual ecosystems, recognizing that ‘every ecosystem is different in their own way’. Their research, focusing on mental health and cardiovascular disease ecosystems, highlighted the importance of context-specific analysis over generalized best practices

Beyond Learner Reaction: Measuring the Impact of Leadership Development at The Ivey Academy

This paper provides an in-depth exploration of The Ivey Academy’s transition from traditional satisfaction-based evaluations to a more comprehensive impact evaluation approach in leadership development. Recognizing the limitations of relying solely on participant satisfaction, The Ivey Academy adopted a modified framework inspired by the Kirkpatrick Model, which evaluates satisfaction, learning, application, and longterm impact. This framework utilizes a range of data collection tools, including surveys, interviews, and action plans. The paper details the implementation process, from securing stakeholder engagement to designing effective surveys and overcoming the challenges of resistance and operational limitations. A key focus of the paper is on the impact survey results from the first term of open enrollment programs, which demonstrate significant improvements in workplace behavior and leadership strategies among participants. Additionally, it highlights the challenges in ensuring data comparability across diverse programs and audiences. Looking ahead, the paper discusses future directions for The Ivey Academy, emphasizing the refinement of the evaluation process, expanding impact measurement, and exploring standardization across various leadership development programs. This approach underscores The Ivey Academy’s commitment to driving realworld change through leadership education, offering valuable insights for other institutions aiming to adopt similar evaluation practices

Assessing the Teachers’ Readiness for Integrating Augmented Reality in K-12 Education: A Comparative Analysis

The integration of augmented reality (AR) into educational environments will depend on its perceived effectiveness in enhancing teaching practices and the attitudes toward the use of this technology. Therefore, the main objective is to investigate the teachers’ attitude and motivation to adopt AR in educational settings, which also looks at a cross-cultural context. Furthermore, this research reveals different aspects that have an impact on teachers’ attitudes toward adopting AR in the teaching process. To investigate this, we conducted a study with 87 K-12 teachers belonging to two different education systems, i.e., Sweden and Palestine. The mixed-methods approach enhances the validity of the study and provides a broad understanding through numerical data, while qualitative insights offer deeper explanations of the findings. The results indicate a statistically significant difference in teachers’ attitudes about AR, with a mean 3.99 for teachers coming from Palestine showing a more positive attitude towards AR-supported learning. Therefore, it is important for educational institutions and application developers to consider a range of learning and teaching methods, as well as specific needs, throughout the process of developing and incorporating AR into the curriculum

Computational Evaluation of Dental Adhesive for Four Direct Restorative Procedures

Direct restoration is recovering the damaged tooth within the mouth by filling the cavity on the tooth using filling material. Therefore, the filling material and location of a cavity are essential in determining the durability of the restored part. This study aims to determine the stress distribution in the teeth using finite element analysis (FEA) with lithium disilicate as a filling material. The binding strength created between tooth enamel and lithium disilicate is different for each restoration class with varying locations of a cavity. In this study, ANSYS Engineering Simulation Software was employed to analyze the stress distribution for four types of classes of direct restoration (class 1, 2, 5, and 6). The analyses were made by applying vertical force on the tooth crown with 600N magnitude. The results show class 1 was 121.2 MPa which is the lowest maximum von mises stress value. The results obtained are beneficial to increase the understanding of the behavior of lithium disilicate as a filling material and the quality of tooth restoration