Online-Journals.org (International Association of Online Engineering)
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Implementation of a Prototype of an Electronic Device for Measuring Lead Calculation in Blood
Get PDFLead, a vital but dangerous industrial metal, poses serious health risks. Exposure, especially in mining and industrial areas, has led to alarming statistics; in Mexico, 21.8% of children have elevated blood lead levels, while in Cuba, 58% of exposed workers exhibit signs of poisoning. In Huacho, Peru, 71.4% of the population has high levels of lead, attributable to the mining industry. Globally, lead is estimated to contribute to 0.6% of the disease burden and cause approximately 143,000 deaths annually, with more than 600,000 children under the age of five dying from it. Traditional methods are expensive and time-consuming. That is why a low-cost portable device is proposed, developed with technology such as Matlab and Arduino, which uses anodic voltammetry to measure blood lead concentrations. Although requiring improvements, this device has the potential to significantly improve the monitoring and prevention of lead-related diseases, providing accurate and rapid results to protect public health where the reading of the presence of lead in the blood was achieved
Exploring Blended Learning Models Supported by Mobile Interactive Technologies in Higher Education
Get PDFWith the widespread adoption of mobile interactive technologies and the evolving landscape of higher education, blended learning models have increasingly become a focus of educational reform. These models integrate traditional face-to-face teaching with modern online learning approaches, enhancing learning flexibility and interactivity through mobile technologies. Although blended learning has shown significant advantages in enhancing teaching efficiency and meeting individual student needs, research on its impact on student emotional interaction remains relatively limited. Emotional factors play a crucial role in the learning process, affecting student motivation and outcomes. Current research on emotional interactions in blended learning primarily relies on traditional methods, such as surveys, which frequently do not offer real-time and precise emotional data. Therefore, this paper aims to explore the effective selection of emotional sensing nodes and the accurate extraction of emotional features through mobile interactive technology-supported blended learning. The findings of this study aim to enhance the quality of teaching interactions but also to provide theoretical and practical support for optimizing blended learning models
Deep Reinforcement Learning Based Secure Transmission for UAV-Assisted Mobile Edge Computing
Get PDFThe increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility
Achieving Optimal Decision Making in Mobile English Language Teaching Using Information Technology
Get PDFIn the context of globalization, the quality and effectiveness of English education have become increasingly significant. The rapid advancement of information technology has provided a wealth of tools and methods for enhancing English education. However, achieving optimal decision-making in the context of information technology to improve the overall efficiency of English education remains a pressing issue. Network data envelopment analysis (network DEA), an effective performance evaluation tool, systematically analyzes and optimizes complex multi-stage decision processes, thereby providing scientific support for educational decision-making. Although numerous studies have attempted to incorporate information technology into educational decision-making, most methods focus on single-stage processes and lack systematic analysis of the entire decision process. This study constructs and designs a decision process model for mobile English education based on network DEA, systematically analyzing the input-output relationships at each stage and proposing optimization strategies and implementation plans to achieve optimal educational decisions. This study not only addresses the shortcomings of existing methods by providing more comprehensive and scientific decision support but also offers crucial references for educational administrators in formulating and implementing effective teaching strategies in the context of information technology
Reframing Teacher Education Through International Collaboration: Exploring Project-Based Learning in Diverse Contexts
Get PDFThis paper focuses on a multi-method study that investigates students’ attitudes and preferences toward project-based collaboration in diverse international contexts. The courses under scrutiny were developed and taught as part of an international blended learning collaboration in teacher education between Dr. Ambedkar University (AUD) in India and Ludwigsburg University of Education (LUE) in Germany. The primary focus of this paper is to analyze and discuss students’ experiences in international project-based environments, with a particular emphasis on the theme of diversity in teacher education. The discussed study employs a multi-method research design that incorporates qualitative interviews and various written feedback forms to gather data on different aspects of the collaborative processes. The findings of this study have the potential to develop future practices and enhance projectbased learning scenarios within higher education. The insights gained from this research can inform pedagogical approaches, and intercultural exchange, ultimately enriching the internationalization efforts within higher education institutions
Enhancing Communication Between Sonographers and Patients in Diverse Cultural Settings via Digital Human Role-Playing
Get PDFEffective communication skills are essential for sonographers to build trust, to explain examination procedures to the patient in non-technical terms, to alleviate anxiety and gain patient consent and collaboration, and to provide information at a pace suitable for the patient. In order to communicate effectively, the sonographer needs to be able to communicate empathetically, adjusting their communication style to meet the needs of different audiences. This is particularly challenging when working with a diverse and multicultural group of patients where the risk of misinterpretation is higher. Students are provided with the opportunity to practice dialogues with virtual patients that are able to interact as real human beings, communicating concerns, emotions, and moods both at a verbal and non-verbal level. Coaching through digital humans accelerates learning from experience without the risks associated with learning in the field
Machine Learning System for the Effective Diagnosis and Survival Prediction of Breast Cancer Patients
Get PDFBreast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes
Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology
Get PDFMaintaining health and safety is essential for workers’ quality of life, and thus, this has become one of the main priorities for industrial enterprises. Electric welders want required safety precautions to be implemented during work in industries with safety risks, especially muscle injuries. This challenge needs to be addressed by the safety officer, who should suggest a way to decrease the risk for workers. However, traditional assessment based on human evaluation and the need for expertise and accuracy in risk assessment have produced muscle injuries. Thus, using artificial intelligence (AI) technology to mitigate risk assessment is cost-effective and accurate. This study proposed a risk assessment system for muscle injuries (RASMI) with AI technology to assess electric welder postures with rapid entire body assessment (REBA) standards to identify the cause of muscle injuries and to warn electric welders when their pose may be a risk. The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries. Additional results showed that they perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being
The Study of the Effectiveness and Efficiency of Multiple DCNN Models for Breast Cancer Diagnosis Using a Small Mammography Dataset
Get PDFBreast cancer (BC), the most prevalent cancer worldwide, poses a significant threat to women’s health, often resulting in mortality. Early intervention is crucial for reducing mortality rates and improving recovery. Mammography plays a pivotal role in early detection through high-resolution imaging. Various classification techniques, including classical and deep learning (DL) methods, assist in diagnosing BC. Convolutional neural networks (CNN)-based classification with transfer learning enhances efficiency and accuracy, especially with limited datasets. This study evaluates the performance of different pretrained deep CNN architectures in classifying pathological mammography scans from the Mini-MIAS dataset. The results show that Xception, VGG16, VGG19, and MobileNetV2 achieve the highest accuracy (97%), with VGG19 demonstrating the fastest prediction speed (0.53 s)
An Analysis of Stakeholders’ Perceptions of Moroccan Secondary School Quality
Get PDFQuality becomes an integral part of a school when the staff’s thinking and vision align with the school’s culture as a global organization. Stakeholders such as school directors, administrators, educational inspectors, and guidance counsellors play a significant role in ensuring quality integration through effective managerial practices in schools. The research study follows a generally inductive qualitative method. The analysis used semi-structured interviews with stakeholders to explore their perceptions of quality in secondary schools. The 24 interviews were conducted, and their responses were transcribed into text, classified, sorted, and coded in the first stage. This was done with two researchers and experts in consultation. In the second stage, we utilized computer-assisted qualitative data analysis software. As a result, the sub-themes from the research have been grouped into 36 major themes in two phases. We identified perceived categories and themes that mainly revolved around the leadership of school heads and their participative management. This research contributes to an analytical discussion of school administrators’ and directors’ perceptions of quality. A set of generic and innovative multidimensional key performance indicators for school quality is finally proposed