Online-Journals.org (International Association of Online Engineering)
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Strategy and Practice for Improving Supply Chain Management through Mobile Interaction Technology
Get PDFWith the rapid development of the global economy and the continuous advancement of information technology, supply chain management is facing increasingly complex challenges. Traditional supply chain management methods often struggle to meet rapidly changing market demands and efficient material allocation needs, and there is an urgent need for new technological means to enhance their flexibility and response speed. Mobile interactive technology, with its ability to transmit information across time and space, has gradually become an important innovative tool in the field of supply chain management. By introducing mobile interaction technology, various links in the supply chain can achieve more immediate and efficient communication and collaboration, thereby promoting the optimization and improvement of the entire supply chain. Although many studies have explored the application of mobile technology in the supply chain, most of them focus on the analysis of single links or specific cases, lacking a systematic and comprehensive perspective. In response to this research gap, after systematically analyzing the multidimensional impact of mobile interaction technology on supply chain management, corresponding strategy and practical framework were proposed in this study. The research content of this study mainly includes four aspects: a) the analysis of the impact of supply chain materials; b) the analysis of the impact of mobile interaction on improving supply chain management; c) the analysis of the sustained impact scope of mobile interaction in supply chain collaboration activities; d) the adjustment strategy for the priority of supply chain collaboration activities. This study not only provides a new perspective for supply chain management theory but also provides specific guidance and decision support for the application of technology in enterprise practice, which has high academic value and practical significance
Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE
Get PDFThe Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objective is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach
Real-Time Optimization of VMD in Healthcare Embedded Systems Using Parallel Processing with OpenMP
Get PDFVariational mode decomposition (VMD) is an advanced signal processing technique used to analyze photoplethysmogram (PPG) signals to extract vital physiological indicators such as heart and respiratory rates. However, implementing VMD on embedded systems presents challenges due to limited computational resources and the need for real-time performance. This paper investigates the optimization of VMD using OpenMP to improve the performance of healthcare-focused embedded systems. We first describe the naive VMD implementation and identify computational bottlenecks. We then optimize the algorithm using OpenMP by parallelizing critical sections, including iterative updates and loop structures, within a homogeneous CPU architecture. We evaluate performance based on metrics such as processing time, efficiency, and the accuracy of heart rate extraction. Experimental results demonstrate that the optimized VMD algorithm achieves a near-linear speedup, with processing times reduced by up to 9.45 times compared to the naive single-threaded version while maintaining efficient resource utilization. This optimization enables real-time signal processing in healthcare applications, improving the performance and reliability of medical devices for patient monitoring and care
Optimizing Off-Chain Storage in Blockchain of Things Systems: Implementing Dockerized IPFS for Enhanced Efficiency
Get PDFThe InterPlanetary File System (IPFS) offers decentralized storage and data sharing, which are critical for the functionality of Blockchain of Things (BCoT) systems. Despite its advantages, IPFS faces challenges such as scalability, latency, and resource management issues that hinder its effective integration into existing blockchain infrastructures. This study explores the implementation of Docker containerization to enhance IPFS performance within BCoT environments. An experimental testbed was established, comprising an IPFS node and an IPFS Cluster peer deployed as Docker containers, to evaluate the latency of file operations across various sizes and analyze containerization’s impact on data storage and retrieval efficiency. The proposed Dockerized IPFS implementation demonstrates substantial performance improvements over traditional systems, achieving latency reductions of up to 75% for small files (1–256 KB) and a three-fold decrease for larger files (64 MB). Specifically, write operations were reduced from 1000 ms to 300 ms, while read operations improved by 40%, decreasing from 2500 ms to 1500 ms. Additionally, the containerized approach yielded lower latency than previous standalone IPFS deployments. The study emphasizes the significance of dynamic resource allocation in optimizing resource utilization, thereby enhancing the overall performance of IPFS Clusters within BCoT frameworks. By leveraging Dockerized IPFS, BCoT systems can achieve more efficient off-chain storage solutions, facilitating improved data management and interoperability in decentralized applications
Research on Influencing Factors of Corporate Credit and Credit Availability in SME Industrial Cluster
Get PDFIn this study, firms in the enterprise cluster were selected for investigation, focusing on the current situation and demand characteristics of small and medium-sized enterprise clusters, and making descriptive statistics from the scale, use and cost of enterprise loans in small and medium-sized enterprise clusters. Further empirical analysis of the factors affecting the credit availability of small and medium-sized enterprises clusters, through data analysis of the factors affecting the credit availability of small and medium-sized enterprise clusters and the influence mechanism of influence, analyzes the results of the empirical test combined with the actual situation and finally puts forward policy suggestions. The study has the following conclusions: First, small and medium-sized enterprises cluster financing demand is relatively common. Traditional financial institutions such as banks are still the main sources of funds; second, the large capital demand and long-term capital demand of small and medium-sized enterprise clusters have been suppressed. Financial institutions such as banks have always provided relatively small credit lines. The credit cycle is also relatively short and cannot meet the financing needs of the SME cluster; third, SME cluster financing is affected by guarantee institutions, government policies, their own financial status, and bank-enterprise relations. Some factors are the direct effects; some factors are manifested as indirect effects; government policy also has a regulatory effect; fourth, ensuring credit availability to SME clusters can be driven from a number of factors
Deciphering Ransomware: Strategic API Usage and Behavioral Patterns for Advanced Detection Techniques
Get PDFRansomware has emerged as a critical cybersecurity threat, inflicting severe financial and operational damage across industries. Traditional signature-based detection systems struggle to detect zero-day and evolving ransomware strains, as they rely on known signatures that cannot capture new tactics. In contrast, behavioral detection methods analyze ransomware actions and patterns, making them more effective. Integrating artificial intelligence (AI) can further improve detection rates; however, effective AI models require diverse, up-to-date, and representative data. Previous research has often focused on isolated aspects of ransomware behavior. Our study addresses these gaps by providing a publicly accessible, up-to-date dataset covering multiple ransomware variants and families, including polymorphic and obfuscated strains not comprehensively explored in prior literature. Additionally, our approach identifies extensive ransomware metrics, including network interactions, registry modifications, file system changes, and low-level API call patterns, enabling real-time detection of malicious activities. Through a comprehensive behavior-based analysis of over 200 recent ransomware samples using the Cuckoo Sandbox platform and custom Python scripts, our study provides cybersecurity practitioners with valuable data and actionable insights, supporting faster responses, improved threat detection, and a proactive stance against evolving risks
Mobile eLearning Application with Chatbot and AI as Student Academic Mentor
Get PDFThe gap in education is centered on access to inter-active and accessible educational tools for all, however, it is changing thanks to globalization and technological advancement with new tools that promote continuous learning. A prototype of a mobile application with an artificial intelligence (AI) chatbot for academic mentoring has been developed. For this purpose, the ADDIE methodology was used, as it provides a structured and flexible framework that allows the development to be adjusted to the changing needs of the project. The prototype facilitates access to academic resources, resolves concerns immediately and offers personalized support to optimize the educational experience. The quality of the prototype was evaluated by experts and users, who appreciated its technology, relevance, integration, usefulness, satisfaction and mentoring, obtaining an average score of 4.47 and 4.48 respectively, which reflects a high level according to the criteria established to improve the educational experience
Personalized and Interactive Mobile Learning in Early Childhood Education: A Bibliometric Study (2015–2024)
Get PDFOver the past decade, early childhood education (ECE) has undergone unprecedented transformations driven by rapid advancements in artificial intelligence (AI) and big data, as well as mobile and interactive technologies. Emerging technologies have profoundly reshaped the ECE landscape, fostering innovations in educational models, teaching methodologies, and learning experiences. This study conducts a bibliometric analysis to explore the evolution and innovation of emerging mobile and interactive models (EMIM) in ECE, with a specific focus on the application of personalized learning, adaptive learning, blended learning, intelligent tutoring, and AI-assisted learning on the foundation of EMIM. Through a bibliometric review of relevant literature over the last decade, this paper examines trends in annual publications, leading research sources, national and regional contributions, author collaborations, and the thematic evolution of research topics. The findings reveal a general upward trend in publication output in the emerging mobile and interactive models in early childhood education (ECE-EMIM) domain, with personalized learning and the application of mobile-enabled AI technologies emerging as central research themes. The United States, China, and European countries lead the field, with increasing cross-national collaboration. Moreover, research topics have progressively shifted from foundational technological exploration to more sophisticated personalized and intelligent learning models based on EMIM. As technological advancements continue, ECE-EMIM is poised to further drive global innovation in preschool education
Bridging the Industry 4.0 Skills Gap: An Immersive Augmented Reality Mobile Learning Approach for Vocational Education
Get PDFVocational high schools encounter substantial challenges in preparing graduates who can meet the demands of the 21st century. This study developed and assessed an innovative mobile learning model based on augmented reality (ARMOL) to enhance vocational students’ competencies, using both the ADDIE model for instructional design and the SEM-PLS method to evaluate effectiveness. The urgency of this research stems from the disparity between vocational graduates’ skills and industry needs, coupled with augmented reality’s (AR) potential to offer a more immersive and interactive learning experience. By employing the ADDIE model, the research successfully created a valid, practical, and effective AR-based mobile learning model. This model enhances students’ cognitive, affective, and psychomotor competencies and fosters 21st-century skills such as critical thinking, creativity, collaboration, and communication. The SEM-PLS analysis revealed significant improvements in creativity and critical thinking, while effects on communication and collaboration were less pronounced. These findings suggest that while the ARMOL model effectively enhances certain competencies, further refinement is needed to fully address all 21st-century skills. This innovative learning model offers a promising solution for elevating the quality of vocational education and preparing graduates for global competition
Development of Software that Supports the Improvement of Mathematical Skills in Children
Get PDFSeveral applications have currently been developed to assist children in learning mathematics. However, many of these applications were created in different languages or designed for distinct social contexts, and some are now outdated. Given the factors mentioned above, a mobile application was created to help children develop mathematical skills. Through expert testing, an application was developed that meets both content and usability standards, ensuring that users feel comfortable while using it. The software was validated through multiple tests, yielding promising results