Search Results (1,052)

This study explores the effectiveness of Technology-Assisted Vocabulary Learning (TAVL) using student-created video learning materials within a tertiary-level English as a Foreign Language (EFL) flipped classroom. By leveraging the flipped classroom model, which allocates classroom time for interactive activities and shifts instructional content delivery outside of class, the research investigates how student-produced videos can enhance vocabulary acquisition and retention. Conducted with 47 university students from a Translation and Translation Studies course, the study aims to fill a gap in empirical evidence regarding this innovative approach. Quantitative analysis revealed that students who created and utilized videos (Group 1) showed the highest improvement in vocabulary scores, followed by those who only used the videos (Group 2), with the control group relying on traditional teacher-led methods showing the least improvement. Qualitative feedback highlighted that video creators experienced deeper engagement and better vocabulary retention, while users appreciated the videos’ visual and auditory elements but faced challenges with vocabulary overload. The findings suggest that incorporating student-created videos into the curriculum fosters a dynamic and collaborative learning environment, offering practical implications for enhancing vocabulary instruction through technology-enhanced pedagogical practices. Future research should focus on optimizing video production processes and integrating these methods with traditional teaching for comprehensive vocabulary learning. Full article

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that impacts social, communication, and emotional skills, presenting significant challenges in learning and social interaction. Traditional teaching approaches often fail to engage children with ASD, highlighting the need for innovative solutions. This study investigates the potential of virtual reality (VR) visual novels, incorporating social stories, as a tool to enhance social skills in children with ASD Level 1. Through a comprehensive literature review, the research evaluates VR environments that blend the interactive, choice-based structure of visual novels with immersive social narratives. Key aspects such as empathy, communication, and emotional regulation are analyzed to assess whether VR-based social stories provide better learning outcomes compared to conventional 2D methods. The findings aim to inform about the application of VR technologies in educational interventions, demonstrating how immersive learning experiences can promote essential social competencies in children with ASD. Full article

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems. Full article

This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy. Full article

This systematic literature review studies student entrepreneurial competence and its impact on sustainable development in the context of higher education in China. The objectives of this study are to identify the key factors affecting student entrepreneurial competence, recent research trends, and the role of student entrepreneurial competence in sustainable development. Using the PRISMA model, relevant literature from 2016 to 2025 was screened from databases such as Scopus, Web of Science, and ScienceDirect, and a total of 11 empirical studies from peer-reviewed journals were analyzed. The results show that entrepreneurial competitions, entrepreneurship education, experiential learning, interdisciplinarity, entrepreneurship policies, teachers, entrepreneurial curricula, the entrepreneurial environment, and morals all influence student entrepreneurial competence. In addition, this study reveals the role of student entrepreneurial competence in promoting Sustainable Development Goals (SDGs), specifically in promoting innovation, creating employment opportunities, and enhancing college students’ sense of social responsibility. This study systematically identifies the core factors that affect student entrepreneurial competence in Chinese universities and provides practical suggestions for optimizing entrepreneurial education policies, improving student entrepreneurial competence, and promoting sustainable entrepreneurship. This study provides a theoretical basis for policymakers and university administrators and proposes strategies for optimizing entrepreneurial education that can be used as a reference, providing the Chinese experience for other emerging Asian economies to learn from, thereby expanding the global academic discussion on sustainable entrepreneurial education. Full article

The integration of advanced technologies, such as the Metaverse, has the potential to revolutionize the retail industry and enhance the shopping experience. Understanding consumer behavior and leveraging machine learning predictions based on analysis can significantly enhance user experiences, enabling personalized interactions and fostering overall engagement within the virtual environment. In our ongoing research effort, we have developed a consumer behavior framework to predict interesting buying patterns based on analyzing sales transaction records using association rule learning techniques aiming at improving sales parameters for retailers. In this paper, we introduce a validation analysis of our predictive framework that can improve the personalization of the shopping experience in virtual reality shopping environments, which provides powerful marketing facilities, unlike real-time shopping. The findings of this work provide a promising outcome in terms of achieving satisfactory prediction accuracy in a focused pilot study conducted in association with a prominent retailer in Saudi Arabia. Such results can be employed to empower the personalization of the shopping experience, especially on virtual platforms such as the Metaverse, which is expected to play a revolutionary role in future businesses and other life activities. Shopping in the Metaverse offers a unique blend of immersive experiences and endless possibilities, enabling consumers to interact with products and brands in a virtual environment like never before. This integration of cutting-edge technology not only transforms the retail landscape but also paves the way for a new era of personalized and engaging shopping experiences. Lastly, this empowerment offers new opportunities for retailers and streamlines the process of engaging with customers in innovative ways. Full article

The transformative function of data-driven leadership in higher education institutions (HEIs) is becoming crucial for advancing sustainable development. By integrating data-driven decision-making with Sustainable Development Goals (SDGs), particularly SDG4 (quality education) and SDG10 (reduced inequalities), EIs can improve the efficacy, inclusivity, and employability of their graduates. To examine this influence, this study implements a systematic literature review (SLR) that adheres to the PRISMA standards and integrates empirical and theoretical insights regarding data-driven leadership in HEI governance, teaching, and learning strategies. The results indicate that combining data analytics into decision-making processes improves institutional efficacy, aligns curricula with the market demands, strengthens student outcomes, and cultivates an inclusive and sustainable academic environment. Moreover, this study introduces a conceptual model connecting sustainable development and data-driven decision-making, offering a structured framework for HEIs to navigate digital transformation responsibly. In addition, this model also emphasizes the importance of balancing technology, ethics, and human-centric leadership in developing educational institutions that are prepared for the future. Ultimately, these insights provide practical advice for academic leaders and policymakers aligning HEI strategies with global sustainability objectives. By advocating for innovative, inclusive, and data-driven leadership, HEIs can promote long-term societal transformation and higher education excellence. Full article

This study presents an innovative approach to automated coral reef monitoring using satellite imagery, addressing challenges in image quality assessment and correction. The method employs Principal Component Analysis (PCA) coupled with clustering for efficient image selection and quality evaluation, followed by a machine learning-based cloud removal technique using an XGBoost model trained to detect land and cloudy pixels over water. The workflow incorporates depth correction using Lyzenga’s algorithm and superpixel analysis, culminating in an unsupervised classification of reef areas using KMeans. Results demonstrate the effectiveness of this approach in producing consistent, interpretable classifications of reef ecosystems across different imaging conditions. This study highlights the potential for scalable, autonomous monitoring of coral reefs, offering valuable insights for conservation efforts and climate change impact assessment in shallow marine environments. Full article

The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where they occur. This research presents a structured approach to developing a taxonomy for open government data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset of 697 OGD use cases, we employed various machine learning techniques—including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP)—to extract thematic categories and construct a structured taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health, Infrastructure, Education, Innovation, Governance, and Environment, each with specific subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond governance and transparency, influencing education, sustainability, and public services. Our approach provides a scalable and data-driven methodology for categorizing OGD impact areas compared to previous research that relies on predefined classifications or manual taxonomies. However, the study has limitations, including a relatively small dataset, brief use cases, and the inherent subjectivity of taxonomic classification, which requires further validation by domain experts. This research contributes to the systematic assessment of OGD initiatives and provides a foundational framework for policymakers and researchers aiming to maximize the benefits of open data. Full article

This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making. Full article

At present, remote sensing serves as a key approach to track ecological recovery after fires. However, systematic and quantitative research on the research progress of post-fire remote sensing remains insufficient. This study presents the first global bibliometric analysis of post-fire remote sensing research (1994–2024), analyzing 1155 Web of Science publications and using CiteSpace to reveal critical trends and gaps. The key findings include the following: As multi-sensor remote sensing and big data technologies evolve, the research focus is increasingly pivoting toward interdisciplinary, multi-scale, and intelligent methodologies. Since 2020, AI-driven technologies such as machine learning have become research hotspots and continue to grow. In the future, more extensive time-series monitoring, holistic evaluations under compound disturbances, and enhanced fire management strategies will be required to addressing the global climate change challenge and sustainability. The USA, Canada, China, and multiple European nations work jointly on fire ecology research and technology development, but Africa, as a high wildfire-incidence area, currently lacks appropriate local research. Remote sensing of the environment and remote sensing and forests maintain a pivotal role in scholarly impact and information exchange. This work redefines post-fire remote sensing as a nexus of ecological urgency and social justice, demanding inclusive innovation to address climate-driven post-fire recovery regimes. Full article

This study addresses the challenges of real-time channel state estimation and adaptive parameter adjustment in dynamic LoRa networks, where the existing methods often fail to adapt efficiently to highly variable channel conditions. This study presents an innovative approach for real-time channel state estimation and adaptive parameter adjustment in long-range (LoRa) networks in dynamic Internet of Things (IoT) environments. When these types of networks are used in dynamic IoT environments, they are known to face challenges in the two above-mentioned areas. In our approach, a hybrid feature extraction method that integrates statistical analysis with domain-specific knowledge is utilized for real-time data labeling, focusing on the signal-to-noise (SNR) and received signal strength indicator (RSSI) metrics. This approach employs an adaptive sliding window technique for efficient processing of recent data. Subsequently, a multi-task long short-term memory (LSTM) neural network is introduced for the simultaneous prediction of multiple channel states. This multi-task model employs an online incremental learning approach to enhance the real-time performance and responsiveness of the model within dynamic environments. It also incorporates a confidence measure for estimated states to increase the prediction reliability. Finally, based on the confidence measure predictions and channel state estimation, the system dynamically adjusts the LoRa parameters, including the spreading factor, coding rate, transmission power, and bandwidth. Our results demonstrate that the confidence-based adaptive strategy coupled with adaptive sliding window processing and incremental learning effectively balances performance optimization with stability in challenging IoT scenarios. This study contributes a robust, data-driven approach for real-time channel state estimation and adaptive parameter control, addressing the unique challenges of IoT networks in dynamic environments. Our approach achieved a packet delivery ratio of 100%, reduced energy consumption to 0.07987 Joules per packet, and demonstrated a prediction accuracy between 97.70% and 97.9% for estimating the different channel states. This innovative framework provides significant improvements in channel state estimation, communication reliability, adaptive parameter control, and computational efficiency, thereby ensuring robust performance in IoT environments at the same time. Full article

Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings. Full article

This study focuses on the innovative application of HCI and XR technologies in behavioral skills training (BST) in the digital age, exploring their potential in education, especially experimental training. Despite the opportunities these technologies offer for immersive BST, traditional methods remain mainstream, with XR devices like HMDs causing user discomfort and current research lacking in evaluating user experience. To address these issues, we propose the spatial reality display (SRD) method, a new BST approach based on spatial reality display. This method uses autostereoscopic technology to avoid HMD discomfort, employs intuitive gesture interactions to reduce learning costs, and integrates BST content into serious games (SGs) to enhance user acceptance. Using the aluminothermic reaction in chemistry experiments as an example, we developed a Unity3D-based XR application allowing users to conduct experiments in a 3D virtual environment. Our study compared the SRD method with traditional BST through simulation, questionnaires, and interviews, revealing significant advantages of SRD in enhancing user skills and intrinsic motivation. Full article

This study advances artificial intelligence by pioneering the classification of human emotions (for patients) with a healthcare mobile robot, anticipating human–robot interaction for humans (patients) admitted in hospitals or any healthcare environment. This study delves into the challenge of accurately classifying humans emotion as a patient emotion, which is a critical factor in understanding patients’ recent moods and situations. We integrate convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multi-layer perceptrons (MLPs) to analyze facial emotions comprehensively. The process begins by deploying a faster region-based convolutional neural network (Faster R-CNN) to swiftly and accurately identify human emotions in real-time and recorded video feeds. This includes advanced feature extraction across three CNN models and innovative fusion techniques, which strengthen the improved Inception-V3 for superior accuracy and replace the improved Faster R-CNN feature learning module. This valuable replacement aims to enhance the accuracy of face detection in our proposed framework. Carefully acquired these datasets in a simulated environment. Validation on the EMOTIC, CK+, FER-2013, and AffectNet datasets all showed impressive accuracy rates of 98.01%, 99.53%, 99.27%, and 96.81%, respectively. These class-wise accuracy rates show that it has the potential to advance the medical environment and measures in the intelligent manufacturing of healthcare mobile robots. Full article