Online-Journals.org (International Association of Online Engineering)
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Enhancing Digital Healthcare through 5G Integration Using a Slotted Bow-Tie 4 × 1 Patch Antenna Array
Get PDFThis paper explores the transformative potential of integrating fifth generation (5G) mobile communication technology into digital healthcare. The advanced features of 5G, such as high data speed, minimal delay, and extensive device connectivity, can enhance healthcare applications, including remote surgeries, teleconsultations, wearable device applications, and big data management. A novel high return loss and high gain slots Bow-Tie microstrip patch antenna array for 5G applications is proposed to support this integration. The antenna design process, simulations, and measurements are detailed, highlighting the antenna’s performance at a frequency of 5.8 GHz. The study concludes that the synergistic combination of 5G technology and the proposed antenna design can significantly improve digital healthcare delivery
Digital Health Record Systems or Applications in the Management of Type 2 Diabetes: A Literature Review
Get PDFThis literature review examines the integration of digital health record systems in the management of diabetes mellitus (DM), particularly type 2 diabetes, and highlights the urgent need for improved patient access to health information through technology. The use of information technology (IT) in the management of diabetes has shown remarkable results, such as increased medication adherence (12.8%–39%), lower HbA1c (0.49%–8%) levels, and lower blood pressure (47.2%–30.8%) levels. It also explores the benefits of mobile health (mHealth) applications, electronic health records (EHR), and personal health records (PHR) in improving self-management and healthcare support via social networks. It emphasizes the need for regular monitoring and communication facilitated by robust IT solutions that enable patients to access and share their health data, ensure effective communication, and support health monitoring. Unlike other reviews, we focus exclusively on proposals that facilitate interaction with medical records for automatic access to patient data. Our main contribution focuses on identifying critical needs to improve diabetes management through technology
Mobile Robotics Training Kit: Enhancing Learning Achievement, Practical Skills, and Problem-Solving Skills of Industrial Electrical Engineering Students
Get PDFThe complexity of microcontroller learning that must combine theoretical concepts and real practices makes it difficult for many students to master the competencies of microcontroller control systems. In addition, it is difficult to achieve practical skills that students must master without a training kit that can interpret the application of microcontroller control systems. Thus, the purpose of this study is to examine the effectiveness of mobile robotic training kits to improve student learning achievement, practical skills, and problem-solving skills. The true-experiment research method used in this study with the number of participants consisted of 76 students, who were divided into 38 experimental groups and 38 control groups randomly selected. The results of this study show that the mobile robotic training kit is significantly effective in improving students' learning achievement, practical skills, and problem-solving skills in the field of microcontroller control systems. This research provides empirical evidence of the importance of implementing a mobile robotics training kit in the learning process to improve students' competencies and prepare them with competencies relevant to the needs of the world of work
Online Engineering Education and Regional Growth: Innovation, Digitalization, and Policy
Get PDFBuilding upon traditional research on engineering education, innovation, and economic growth, this study introduces additional control variables such as air quality and talent concentration and extends the analytical scope to include underdeveloped regions in western China, thus advancing beyond conventional research paradigms. The research investigates the impact of high-quality engineering education on regional economic sustainability across nine diverse Chinese regions from 2014 to 2024. Grounded in regional innovation systems theory, the study examines both the direct effects of educational investment on economic outcomes and indirect effects mediated through innovation, digital transformation, and industrial upgrading. Findings indicate that financial investment in engineering education significantly promotes regional economic growth, although effects differ notably across regions. Developed areas in China experience economic growth primarily driven by innovation, whereas regions such as Henan and Heilongjiang rely more on industrial modernization. Additional control variables, including R&D personnel density and air quality, further influence these relationships. The study emphasized the importance of zoning to promote optimization of school investment
Industry 4.0 and Supply Chain Resilience: A Comprehensive Analysis of Technological Impacts
Get PDFThis study proposes a novel framework to assess the impact of Industry 4.0 (I4.0) technologies on supply chain resilience (SCRE) enhancement. Recognising the dynamic nature of technological advancements and their influence on resilient supply chain (SC), the framework employs an integrated approach. Initially, relevant I4.0 technologies were identified through literature. The I4.0 technologies are then evaluated against critical SCRE network design requirements using integrated multi-criteria decision-making (MCDM) techniques. Results highlight node criticality as a paramount factor in SCRE, while ranking artificial intelligence (AI) as the most impactful I4.0 technology, followed by autonomous vehicles (AV) and digital twin (DT). This study provides a robust and quantifiable roadmap for understanding the role of specific I4.0 technologies in bolstering SCRE
Sentiment Analysis and Topic Modelling for Academic Integrity in the Era of AI
Get PDFThis study explores the sentiments and discussion topics of X/Twitter users regarding academic integrity in the era of artificial intelligence (AI). The approach incorporates sentiment analysis and topic modelling to reveal the public perspective on academic integrity issues, including plagiarism, online exams, and AI usage. Our study aims to provide a framework for exploring topics and findings related to the trend of academic integrity in the era of AI. In sentiment classification, Naive Bayes, support vector machine (SVM), and Random Forest algorithms are combined with vectorization techniques such as Count Vectorizer, Word Level TF-IDF, N-Gram TF-IDF, and Character Level TF-IDF. The results show that Naive Bayes with Count Vectorizer provides the best performance on imbalanced data. For the topic modelling, NMF proved to be the most effective in generating specific topics, such as plagiarism and AI detection, with the highest coherence scores. This study also examines the crucial role of each preprocessing step in enhancing data quality, which significantly impacts classification and topic modelling performance. The findings are expected to provide new insights into sentiment analysis and a deeper understanding of academic integrity issues in the era of artificial intelligence
Exploration of the Employment Quality Evaluation Method for Local University Graduates Based on PROMETHEE
Get PDFCurrent evaluation methods for college graduate employment quality mainly employ techniques such as the analytic hierarchy process (AHP) and factor analysis (FA), which tend to be similar in analytical approach and have certain limitations. This paper utilizes the PROMETHEE method with priority function characteristics, introduces possibility comparison interval hesitant fuzzy numbers, and evaluates the employment quality of local college graduates from three dimensions: overall employment quality, social influence, and overall satisfaction. An improved PROMETHEE decision-making method considering attribute correlation is proposed, and its stability and superiority are demonstrated, providing support for the system of employment quality evaluation methods
Plithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems
Get PDFPlithogenic-based decision models are more effective in designing optimal solutions to intricate problems. This study work proposes an integrated decisioning model conjoining plithogeny and machine learning algorithms. This study considers the decision-making problem of selecting smart and sustainable materials for the effective functioning of renewable energy systems. The decisioning model has ten evaluation criteria and considers alternatives for materials subjected to five categories of photovoltaic, thermoelectric, piezoelectric, phase change, supercapacitor, and electrochromic. This work employs the algorithm of a random forest classifier in determining the most crucial criteria for selecting smart and sustainable materials. The plithogenic-based decision method of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is employed in ranking materials of each kind. The proposed decisioning approach is the combination of a machine learning algorithm and a plithogenic decision approach, which is further facilitated by the intervention of Python programming. The criteria selection accuracy is compared with a support vector machine algorithm to demonstrate the efficacy of this integrated decision approach in ranking the materials used in formulating robust renewable energy systems. Sensitivity analysis is also performed to exhibit the efficacy of this proposed model. This model has few limitations, as it considers a few selected materials under each of the categories
An Effective Hybrid Harmonic Staircase Broadcasting Protocol (HaSB) for Video-on-Demand System
Get PDFVideo on demand (VoD) system is a service that enables the users to choose a video at any time and watch it. VoD systems have different protocols with different strategies for dividing the video segment and sending it by channels. The protocols in VoD have different analyses depending on several metrics that assess the performance, such as server bandwidth, client waiting time, and client buffer requirement. That can help the user to watch the video at any time they are needed, which reduces the waiting time when they select the video and start watching it. This paper proposes a new hybrid broadcasting protocol to link the advantages of harmonic and staircase broadcasting protocols for VOD to reduce the delay time once the viewer starts to stream the video and view it, where the buffer requirement of the client is reduced. HaSB protocols have integrated harmonic broadcasting (HB) protocol and staircase broadcasting protocol (SB) to obtain the strengths of small client waiting time and low client buffer space. Furthermore, HaSB has improved the buffer requirement in HB protocols and improved the waiting time in staircase broadcasting protocols. Finally, the obtained results show that the waiting time is 15 seconds and the buffer requirement is less than 25% with a bandwidth of Mbps. HaSB can produce a suitable result in reducing the waiting time and buffer requirement when compared with other broadcasting protocols
Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy
Get PDFWith 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