Online-Journals.org (International Association of Online Engineering)
Not a member yet
9805 research outputs found
Sort by
Mobile Application Prototype: AP3S to Prevent Stunted Babies
Get PDFStunting in toddlers is a serious concern because it can have various short and long-term adverse effects. Therefore, it is necessary to design a mobile-based system to monitor the health of pregnant women early on so that stunted babies can be prevented. This study aims to develop a prototype of an application for early detection of stunting risk in Androidbased antenatal care services. This application prototype is made in Indonesian and is called: “Aplikasi Pendampingan Percepatan Penurunan Stunting (AP3S)”/Stunting Reduction Acceleration Assistance Application. The methodology applied is the system development life cycle (SDLC), one of the primary methods for developing mobile applications in the health sector. As a result, a prototype application with a design and functionality that facilitates access for pregnant women can connect directly with health workers and monitor and consult further. As for health workers, AP3S can provide a comprehensive health analysis of antenatal care, including nutrition for pregnant women, fetal growth monitoring, and health education. AP3S has the advantage of being able to immediately display the risk of babies born stunted or babies with low birth weight so that preventive interventions can be carried out immediately. In conclusion, after conducting a literature study and prototype development, an efficient, easy-to-use, and safe application prototype was obtained to facilitate patient access to health services to prevent babies born with stunting
Two Success Stories as Result of the Horizon Europe Shift-Hub Project
Get PDFThis 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
RHL-Butterfly: A Scalable IoT-Based Digital Twinning Platform for Embedded Systems and Remote Laboratories
Get PDFThe RHL-Butterfly is a digital twin of a breadboard designed for use with Field Programmable Gate Arrays (FPGAs) and microcontrollers in educational remote engineering laboratories and engineering education, aimed at providing greater flexibility and fostering equity in learning opportunities. This study introduces a novel server-side approach for simulating breadboard circuits using microcontroller backends, enhancing computational efficiency, and offering open-source transparency. The system interfaces the digital twin with a network of physical, remote FPGAs and microcontrollers and introduces a scalable communication protocol system that converts the graphical breadboard layout into a one-dimensional (1D) string representation for network communication. This custom protocol facilitates seamless integration with various digital twinning applications, ensuring a balance between virtualized interfaces and physical hardware and expanding the scalability and accessibility of engineering labs. Survey responses and feedback from students using the system in their course studies have been encouraging. Students particularly appreciate the equitable nature of the system and its similarity to a traditional physical breadboard
Exploring the Role of Course Teams in Helping Graduate Teaching Assistants Navigate Day-to-Day Teaching
Get PDFGraduate 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
Exploring Students’ and Teachers’ Perceptions about Using ChatGPT in Programming Education
Get PDFThis paper aims to study the opinions of teachers and students regarding the opportunities and challenges of using ChatGPT in programming education. The research combines quantitative data from Likert-scale questions with qualitative data from open-ended responses. The findings reveal similarities between students’ and teachers’ views on the advantages of using ChatGPT in programming education and its potential to develop soft skills. A difference appears in assessing the attitudes of both groups toward the disadvantages of integrating ChatGPT in education. Compared to students, teachers express much greater concern about the negative effect of artificial intelligence (AI) on academic integrity and teaching quality. The results showed that both groups positively evaluate ChatGPT as a supplementary tool in education. They believe it should complement traditional teacher-student communication rather than replace it. Based on the research findings, the authors recommend that the integration of ChatGPT into education should be preceded by adopting university AI usage policies and training for the effective use of ChatGPT. Pedagogical guidelines for integrating ChatGPT into programming education are proposed to minimise the effect of students’ overreliance on AI and achieve the learning outcomes defined by Bloom’s Taxonomy
Learning Environment: A Key for Academic Success in Business Administration Students at a Higher Education Institution
Get PDFA study was carried out with the purpose of analyzing the current scientific literature in Latin America and the Caribbean regarding academic performance, highlighting the scarcity of research related to the correlation between the perception of the learning environment and academic performance. The objective of the study was to examine the relationship between the learning environment and academic performance of fourth and fifth semester students of Business Administration in online blended mode in a higher education institution. The instrument used to evaluate the learning environment was the classroom environment scale (CES) in one hundred students, where the classroom learning environment was evaluated in four main categories: academic, institutional, safety and community. For academic performance, data such as end-of-semester grade point average (GPA), age and gender were collected. The results of the study revealed that there is an association between a positive classroom environment and better academic performance. In conclusion, the study determined that a positive and welcoming learning environment is related to better academic performance, while a negative learning environment is associated with lower grades
Mobile AI Tools in Language Learning: EFL Students’ Acceptance of ChatGPT for Writing Brainstorming
Get PDFWriting poses a significant challenge for most students who struggle with brainstorming. Among many mobile AI tools, ChatGPT can help alleviate writing difficulties. Nevertheless, the use of mobile AI tools, especially ChatGPT, for brainstorming in a writing class has not been fully investigated. Thus, this study aims to inspect the acceptance of EFL (English as a foreign language) students regarding using ChatGPT for brainstorming in a writing class. The present study employed quantitative and qualitative approaches with a 5-point Likert scale questionnaire and a semi-structured interview. 257 EFL students at a private Vietnamese university with prior experience using ChatGPT for writing brainstorming participated in the study. The results revealed that the university students in this study responded positively to using ChatGPT to support them in enhancing their writing quality regarding perceptions of ChatGPT, actual practice, the advantages, and disadvantages. Nonetheless, some difficulties were also addressed to assist EFL students in enhancing their writing skills. Several solutions to using ChatGPT in writing were explored in this study to boost students’ writing performance
The Implementation and Empirical Analysis of Adaptive Virtual Mentor: Mobile Technology Empowers Introverts’ Business Communication Skills
Get PDFEffective communication is a key element in professional success, yet individuals with introverted tendencies often face difficulties in developing these skills, which limits their opportunities for growth in the work environment. This research aims to develop Adaptive Virtual Mentor (AVM), a mobile application designed to help introverted individuals improve their business communication skills (BSC). This application utilizes speech recognition technology supported by deep neural networks (DNNs) and recurrent neural networks (RNNs). DNNs are tasked with improving accuracy in recognizing user speech patterns, while RNNs are instrumental in maintaining the context of the conversation and providing timely and relevant feedback in real-time. A quasi-experiment was conducted involving introverted individuals through structured training using this application. The results of the study showed that participants experienced an improvement in communication skills after the training. Potential further developments include refining the adaptive algorithm and adding new features that allow this application to be applied in a variety of other professional contexts. Future research could explore the application of this technology on a wider scale, as well as evaluate its impact on improving productivity and performance in diverse workplaces
Optimizing LiDAR Point Clouds for Mobile Digital Twin Restoration of Cultural Heritage with Trustworthy Validation
Get PDFThe preservation and restoration of cultural heritage have long been significant endeavors throughout human history. With the rapid advancement of LiDAR technology, digital methods have become essential tools for heritage restoration. However, efficiently and accurately processing LiDAR point cloud data—particularly in mobile digital twin restoration—poses numerous challenges. Cross-scale point cloud registration and trustworthiness assessment are among the key technical hurdles. Current research methods often face trade-offs between accuracy and efficiency when handling large-scale point cloud data. Moreover, traditional centralized approaches to trustworthiness evaluation raise concerns regarding privacy protection and security. To address these challenges, this study presents two primary contributions. First, an improved PointNet-based method for cross-scale registration of LiDAR point clouds is proposed, enhancing computational efficiency while maintaining registration accuracy. Second, a novel trustworthiness scoring mechanism is introduced, leveraging federated learning to enhance the reliability of restoration results while safeguarding data privacy. These advancements not only drive forward digital twin restoration technology but also offer safer and more reliable solutions for cultural heritage preservation
Dynamic Prediction and Optimization of Energy Consumption in Mining Equipment Using Mobile Computing Platforms
Get PDFWith the increasing energy consumption in the mining industry, the effective prediction and optimization of energy consumption in mining equipment have become pressing challenges. Traditional energy consumption prediction methods suffer from data processing delays and the fixed nature of monitoring devices, making them inadequate for meeting the real-time and flexible demands of modern mining operations. The advent of mobile computing platforms has introduced new possibilities for the dynamic prediction and optimization of energy consumption in mining equipment. In recent years, energy consumption prediction techniques based on mobile computing platforms have gained significant attention, enabling realtime data acquisition and analysis for a more precise understanding of energy consumption patterns and the implementation of efficient optimization strategies. However, existing studies predominantly focus on conventional models and methodologies, lacking effective mechanisms to capture spatiotemporal dynamics and optimize energy consumption accordingly. In this study, a spatiotemporal gated graph convolutional prediction model was proposed for the dynamic prediction of energy consumption in mining equipment based on a mobile computing platform. Additionally, an energy consumption optimization strategy was explored using the prediction results. This study provides a novel approach to energy consumption optimization in mining equipment, offering both theoretical significance and practical value