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AI-Driven Automation in Software Testing: Enabling SME Adoption
No full textThe rapid evolution of the software industry has positioned Artificial Intelligence (AI) as a game-changer in software testing, enabling Quality Assurance (QA) teams to deliver higher-quality software with greater speed and efficiency. Despite these advantages, many small and medium-sized enterprises (SMEs) are hesitant to adopt AI into their software testing due to financial limitations, time constraints, and lack of technical skill resources. The study aims to address these challenges by proposing a framework that enables SMEs to implement AI-based automation in software testing aligned with their operational requirements. The research methodology combines a planned survey and a literature review to identify the commonly used automation tools and assess their impact on product quality. The ultimate goal is to develop a cost-effective, practical process innovation framework tailored to support Malaysian SMEs in adopting AI for software testing
Analysis Simulation of Overcurrent Protection System for 3 Phase Induction Motor Using an Arduino and ACS712 Current Sensor
No full textThis research explores a simple yet effective way to protect three-phase induction motors from overcurrent by using an Arduino Uno and an ACS712 current sensor. Overcurrent conditions can cause serious damage to motors, especially if left unnoticed. To address this, the study proposes a smart monitoring system that continuously reads current levels and responds based on their severity. The system categorizes the motor’s status into three levels: normal, warning, and danger. Each level is indicated by an LED and corresponds to specific current ranges. The simulation was carried out using Proteus software, where the Arduino receives real-time current readings from the sensor. If the current crosses a set threshold (around 10% over the safe limit), the system automatically shuts down the power, helping prevent motor failure. Results from the simulation show that the system works as intended—it successfully detects overcurrent conditions and reacts appropriately. This proves that combining an Arduino with the ACS712 sensor is a practical, affordable, and easy-to-use solution for motor protection. The approach is especially useful for learning environments or small industries that need cost-effective safety systems
Cultural Translation Theory and Its Application in Oil-Paper Umbrella Design
No full textThis study explores the transformation of the oil-paper umbrella, an intangible cultural heritage,through cultural translation theory in modern creative design. By analyzing the cultural symbols at material, behavioral, and spiritual levels, products like earrings, necklaces, and pens were designed. The findings provide practical references for the innovative transformation of cultural heritage
Implementation of Multimodal Assessments to Address Public Speaking Anxiety Among College Students
No full textThe ability to deliver oral presentations has been widely recognizable as one of the most essential skills in higher education and the workplace. This skill plays a vital role in securing a job for most fresh graduates. This study examines the implementation of multimodal assessments, incorporating various digital and interactive evaluation methods in higher education to address public speaking anxiety (PSA). Drawing from a case study, the study analyzes the effectiveness of integrating virtual reality (VR) and artificial intelligence (AI)-driven speech analysis in mitigating PSA. The findings highlight that multimodal approaches significantly decrease the anxiety level and build confidence through personalized feedback and track progress over time. A comparative analysis of traditional and multimodal assessment methods highlighting key differences in anxiety reduction, engagement, and overall speaking proficiency. The results demonstrate that multimodal assessments strategically improve students' speaking skills, as indicated by biometric and performance-based metric
The Requirement Analysis of the IoT-Based Vehicular Spatial Allocation System
No full textToday’s crowded cities often have trouble with parking. More cars are driving than there are places to park. Because of this, effective parking management systems are now required. Consequently, we exhibit the benefits of an IoT-based parking management system for efficiently managing parking spaces. To illustrate how it works, we put in IR sensors for sensing if the spot is taken and message to a DC motor that opens the gate. Currently, internet connectivity is handled by a Wi-Fi modem, and the AVR microcontroller runs the entire system. IOT Gecko supports our project by providing online connectivity and designing an IOT management graphical user interface. IR sensors allow the system to spot if parking slots are currently in use. After reading the available parking slot number, the system notifies the cloud server and lets people verify the available slots online. As a result, people can find free parking spots online from any place and not worry about rushing. OpenBay, as a result, solves the parking shortage for cities and gives users a smart parking management system based on IoT
Igbo Apprenticeship Practice: Resilience Vehicle for Venture Creation in Southeastern Nigeria
No full textIn the quest to generate wealth that could salvage the ravaging poverty after the Nigeria-Biafra Civil War, the Igbo-speaking people of southeastern Nigeria birthed apprenticeship practice which has until this day become a norm in the region. It was based on the relevance of this entrepreneurial spirit of the Igbo people and how they have consistently embraced apprenticeship culture to reduce unemployment and create new businesses across the Nigerian space that motivated the researchers to carry out this investigation with 204 mentors (Oga’s) that passed through the apprenticeship practice before establishing their businesses. The purpose of this investigation is to examine the relationship between Igbo apprenticeship practice and venture creation in Southeastern Nigeria. The theories that explained Igbo apprenticeship practice are social learning theory and Igwebuike theory. Descriptive statistics and regression were used to analysed the data. The study found that Igbo apprentice practice has a significant positive relationship with venture creation and job creation in the southeastern region of Nigeri
Understanding the Role of Health Education in Promoting Strengthening Exercises Among the Elderly: A Comprehensive Literature Review
No full textThe purpose of this systematic review was to assess how health education impacts awareness and exercise participation in improving exercise among older adults. Systematic searching was performed in PubMed (2017–2025) with pre-specified keywords, and peer-reviewed quantitative and qualitative papers including adults ≥60 years were considered; 12 studies meeting the methodological quality standards were assessed. There is evidence that health education interventions, particularly when integrated with multi-component or resistance exercise programs, enhance muscle strength, mobility, balance, fall prevention, and prevention of sarcopenia. Even though considerable evidence exists supporting these effects, strengthening exercise participation is low because of misconceptions regarding ageing, fear of injury, and lack of access to structured programs, particularly in rural and low-income areas. The review concludes that physiotherapist-facilitated health education and community-based interventions can increase awareness, exercise compliance, and functional independence of older individuals. These results underscore the importance of policymakers and public health systems incorporating accessible strength-training programs within standard care for the elderly to ensure healthy ageing
Preparation of Organic Scintillators and Applications in Neutron-Gamma Discrimination
No full textNeutron detection holds significant strategic importance in fields such as nuclear safety, medicine, and high-energy physics. However, it is often compromised by gamma-ray interference, making efficient discrimination technology a critical challenge. Organic scintillators, with their advantages of high neutron detection efficiency, rapid response time, and morphological adaptability, have emerged as core materials in neutron-gamma discrimination research. This paper systematically reviews the luminescence mechanisms of organic scintillators and the principles of pulse shape discrimination (PSD). It analyzes the preparation methods, performance characteristics, and research progress of crystalline, liquid, plastic, and loaded scintillators. Case studies highlight the effective enhancement of neutron signal-to-noise ratios and imaging resolution in nuclear power plant monitoring, PET imaging, and high-energy physics experiments using organic scintillators. Future developments in organic-inorganic composite systems and novel perovskite materials are anticipated to expand the broader application of organic scintillators in neutron detection. Among these materials, perovskite-based organic scintillators exhibit the most promising application prospects in future high-precision neutron detection scenarios due to their unique combination of high crystallinity, tunable optical bandgap, and excellent radiation resistanc
Financial Literacy-Based Islamic Education and Deep Learning to Prevent Online Debt in Indonesian Students
No full textOnline lending (pinjol) among Indonesian youth poses ethical and financial risks, particularly regarding riba in debt transactions. According to the Financial Services Authority (OJK), as of February 2025, individuals aged 19–34 held an outstanding balance of IDR 38.18 trillion, while non-performing loans for those under 19 reached IDR 3.6 billion. This study developed a Grade VIII Islamic Religious Education (PAI) textbook, Chapter 9: “Becoming a Trustworthy Person and Avoiding Riba in Trade and Debt Transactions”, integrating financial literacy with a deep learning approach. Using the ADDIE-based Research and Development (R&D) method, the textbook underwent needs analysis, design, expert validation, pilot testing, and evaluation. Expert reviews rated it “excellent” across content (91.4%), instructional design (89.7%), and contextual relevance (90.2%). Implementation with 62 students increased average scores from 65.3 to 84.7 (N-gain = 0.63), improving riba comprehension, critical thinking, and ethical decision-making, while reducing interest in online loans. These improvements were facilitated through the principles of Mindful Learning, which encouraged reflective awareness of the ethical implications of debt; Meaningful Learning, which connected Islamic financial values with real-life cases of digital lending; and Joyful Learning, which engaged students through interactive, problem-based, and collaborative tasks. The results underscore the potential of integrating financial literacy into Islamic education through deep learning pedagogy as a proactive means of fostering ethical financial behavior among adolescents
Optimizing Cryptocurrency Portfolio Rebalancing: A Machine Learning Approach
No full textEver since the introduction of Bitcoin, cryptocurrencies have attracted interest from many due to their potential for appreciation. They have also been stigmatized for their volatility, low correlation with traditional assets, and uncertain regulatory conditions. Nevertheless, many cryptocurrencies still attract the attention of major players in finance. As in the management of a portfolio of other assets, there is a need to regularly rebalance the portfolio to optimize returns across different time horizons. This study evaluates four machine learning models, Logistic Regression, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, for identifying optimal cryptocurrency rebalancing decisions. Using a momentum-based approach with a 30-day forward window, the models are trained to classify assets as hold or rebalance based on future price movement. Our approach is based on feature engineering and hyperparameter tuning. Results show that tree-based models, such as Decision Tree and Gradient Boosting, demonstrate superior classification performance in identifying optimal rebalancing moments. However, the study also highlights the limitations of using historical data exclusively without referring to other external factors such as market sentiment and regulatory changes. Overall, the study makes a contribution to the field of cryptocurrency portfolio management by providing one of the first comparative evaluations of multiple ML architectures for cryptocurrency rebalancing decisions, demonstrating the potential of machine learning to improve portfolio management in highly volatile markets