Special Issue on Innovation in Computing, Engineering Science & Technology 2024-25

This research analyses the challenges faced by ASEAN countries in managing its energy efficiencies and resources due to rapid economic growth, increasing energy demand, and diverse energy infrastructures across member states. This paper explores the energy management policies and strategies within the ASEAN region, focusing on the integration of energy efficiency measures, renewable energy initiatives, and cross-border energy trade. This paper analyse the region’s progress towards its sustainable energy goals, the role of policy frameworks, and the impact of regional collaboration. Key challenges such as energy security, affordability, and environmental sustainability are examined, alongside opportunities for innovation in energy technologies and policy reforms. The findings highlight the importance of a cohesive energy management strategy that balances the diverse needs of ASEAN member states while advancing the region’s transition towards a low-carbon future. This paper provides policy recommendations aimed at enhancing ASEAN’s energy resilience and supporting its sustainable development goals.

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Augmented reality (AR) technology has been increasingly used in developing teaching materials with the aim of sparking more interest in technology (T) and engineering (E) among students in STEM education. In the proposed system, AR is integrated with an educational robot controlled by a KidBright microcontroller board, developed by the Educational Technology research team (EDT) at the National Electronics and Computer Technology Center in Thailand. Moreover, the KidBright program has been implemented over 2,200 Thai schools. To maximize the benefits of the KidBright program, the Assistive System for Collaborative Assembly Task using Augmented Reality (ASCAT-AR) was created with the objective of enabling students to learn and collaborate in assembling robots. Students will work in pairs to assemble robots using the system and learn about mechanics, sensors, and 3D-printed parts. The students were divided into two groups: Group A read the manual and assembled the robot independently, while Group B used the ASCAT-AR system. In addition, AR applications offer smooth graphic rendering at 44-60 frames per second. Evaluation result showed that Group B students had a higher average success rate than average success rate of Group A students. The results showed that users of the ASCAT-AR system were more motivated in learning and obtained more knowledge about robot technology and programming.

Augmented reality (AR) technology has been increasingly used in developing teaching materials with the aim of sparking more interest in technology (T) and engineering (E) among students in STEM education. In the proposed system, AR is integrated with an educational robot controlled by a KidBright microcontroller board, developed by the Educational Technology research team (EDT) at the National Electronics and Computer Technology Center in Thailand. Moreover, the KidBright program has been implemented over 2,200 Thai schools. To maximize the benefits of the KidBright program, the Assistive System for Collaborative Assembly Task using Augmented Reality (ASCAT-AR) was created with the objective of enabling students to learn and collaborate in assembling robots. Students will work in pairs to assemble robots using the system and learn about mechanics, sensors, and 3D-printed parts. The students were divided into two groups: Group A read the manual and assembled the robot independently, while Group B used the ASCAT-AR system. In addition, AR applications offer smooth graphic rendering at 44-60 frames per second. Evaluation result showed that Group B students had a higher average success rate than average success rate of Group A students. The results showed that users of the ASCAT-AR system were more motivated in learning and obtained more knowledge about robot technology and programming.

The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as a regression task. The results indicate that LightGBM and XGBoost surpass other models, and their effectiveness is further enhanced by employing feature selection and synthetic data generation methods. The optimized LightGBM model exhibited superior performance, achieving a MAPE score of 10.78%, which highlights the effectiveness of gradient boosting mechanisms in handling heterogeneous data sets typical in custom manufacturing. Additionally, we introduce a methodology that enables domain experts to observe and critique the results through explainable AI visualizations.

As society ages, the imbalance between family caregivers and elderly individuals increases, leading to inadequate support for seniors in many regions. This situation has ignited interest in automatic health monitoring systems, particularly in fall detection, due to the significant health risks that falls pose to older adults. This research presents a vision-based fall detection system that employs computer vision and deep learning to improve elderly care. Traditional systems often struggle to accurately detect falls from various camera angles, as they typically rely on static assessments of body posture. To tackle this challenge, we implement a feature fusion strategy within a deep learning framework to enhance detection accuracy across diverse perspectives. The process begins by generating a Human Silhouette Image (HSI) through background subtraction. By combining silhouette images from two consecutive frames, we create a Silhouette History Image (SHI), which captures the shape features of the individual. Simultaneously, Dense Optical Flow (DOF) extracts motion features from the same frames, allowing us to merge these with the SHI for a comprehensive input image. This fused representation is then processed using a pre-trained Convolutional Neural Network (CNN) to extract deep features. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is subsequently trained on these features to recognize patterns indicative of fall events. Our approach’s effectiveness is validated through experiments on the UP-fall detection dataset, which includes 1,122 action videos and achieves an impressive 99% accuracy in fall detection.

This study proposes the Value Karuta (VK), an application of the traditional Japanese card game karuta. Its goal is to contribute to the understanding of value, which is the first principle of lean management. After stating the problems of lean management and the specifications of VK, this paper confirms the validity of the proposal by discussing two surveys. The first survey explored the utility factors of the cards themselves; it was conducted with a group of students and businesspeople in Japan. The second survey observed the actual situation in the game and was conducted in a group of academics at two UK universities. Both surveys used qualitative methods, such as observation and discussion, and quantitative questionnaires. The results confirm the role of VK as a fundamental tool addressing the need to understand customer value in lean management.

Deep Neural Networks (DNNs) prove to be susceptible to synthetically generated samples, so-called adversarial examples. Such adversarial examples aim at generating misclassifications by specifically optimizing input data for a matching perturbation. With the increasing use of deep learning on embedded devices and the resulting use of quantization techniques to compress deep neural networks, it is critical to investigate the adversarial vulnerability of quantized neural networks.In this paper, we perform an in-depth study of the adversarial robustness of quantized networks against direct attacks, where adversarial examples are both generated and applied on the same network. Our experiments show that quantization makes models resilient to the generation of adversarial examples, even for attacks that demonstrate a high success rate, indicating that it offers some degree of robustness against these attacks. Additionally, we open-source Adversarial Neural Network Toolkit (ANNT) to support the replication of our results.

Extemporaneous omeprazole-based preparations are commonly used in hospitals; however, there are no validated studies about physicochemical stability. This study aimed to determine if temperature, luminosity, and the type of diluent affect the stability of omeprazole in the extemporaneous preparation. For stability, the methodology validated previously by our group was used. The 2k experimental design included Temperature (25ºC and 35ºC) and Luminosity (covered by light and 400 Lx) variables. Diluents were evaluated at five levels: 1) Citric acid + polyethylene glycol solution, 2) Polyethylene glycol solution, 3) Physiological saline solution, 4)Citric acid + polyethylene glycol solution + physiological saline solution, and 5)Polyethylene glycol solution + physiological saline solution. Minitab 18 software was used fordata analysis, and the degradation kinetics were determined by linear regression. The optimalcondition for physicochemical stability was a temperature of 25ºC covered by light (OM1-3h49’, OM2-3h 2’, SSF-5h 8’, OM1SSF-2h 27’, OM2SSF-6h 23’). The diluent based onphysiological saline solution provided more than five hours of shelf-life, and more than sixhours, the diluent based on OM2 + SSF. However, the best option is physiological salinesolution, considering the accessibility of the diluent. In conclusion, environmental conditionsshould be considered in extemporaneous omeprazole-based preparation since they are affectedby temperature, luminosity, and type of diluent. Assessing shelf-life prior to administration isnecessary to provide a safe and effective drug, avoiding the occurrence of side effects inpatients.

Despite the development of the Internet and wired networks such as fiber optics, mobile networks remain the most used thanks to the mobility they offer to the user. However, data protection in these networks is more complex because of the radio channels they use for transmission. Hence,there is a need to find more sophisticated data protection means to face any attack. But, this is not an easy task, especially with the emergence of AI-based attacks. In this context, we proposed in this work a solution that can significantly improve data protection in a new-generation mobile network. Therefore, the main objective of this study is to improve and implement an enhanced version of the standard ZUC algorithm designed by the Data Assurance and Communication Security Research Center of the Chinese Academy of Sciences and standardized by the 3GPP (3rd Generation Partnership Project) organization to ensure the LTE (Long Term Evolution of radio networks) security. The proposed design is principally based on replacing the static Sboxes of the original algorithm (S 0 and S 1) with a chaos-based dynamic S-boxes thus allowing to generate a different key stream for any change on the secret key and with the best randomness and robustness properties. The two new dynamic S-boxes are initialized with 256 initialization values each (x”00”), then filled in parallel using two chaotic maps that use the ZUC algorithm registers, the CK (Cipher Key), and the IV (Initial Vector) to form two different initial values for each chaotic map. To reach the hardware performance, we implemented the system on a Xilinx XC7Z020 PYNQ-Z2 FPGA platform. The designed architecture occupies low logic resources (1135 Slice LUTs, 762 Slice Registers, and 8 DSP48E1) on the used FPGA device and can reach a throughput of 2515.84 Mbps with a running frequency of 78.62 Mhz by consuming only 0.188 W. To evaluate the resistance of the proposed cryptosystem, we used many security tests (keystream distribution, keystream randomness, key sensitivity, plaintext sensitivity, keyspace, and NIST statistical tests). The experimental results and comparison with other S-boxes based algorithms prove on one hand that using the dynamic S-box technique has enforced considerable data protection against cryptanalysis attacks, and on another that the hardware metrics (used logic resources, achieved throughput, and efficiency) are suitable for real-time applications such as mobile security transmission.

True Random Number Generators (TRNG) find applications in various fields, especially hardware security. We suggest a TRNG that exploits the intrinsic static stochasticity of Resistive Switching Random Access Memories (ReRAMs) to generate random bits. Other suggested designs use stochasticity in the switching mechanism, which requires high precision over input voltage and time. In the proposed design, the random bits are produced by comparing the resistance of two ReRAMs in their high resistance states. ReRAM crossbar architectures are being highly researched, and our design is completely compatible with a ReRAM crossbar. The design was verified by simulations and testing the output stream using the NIST randomness test suite. The effect of device-to-device variability was tested on the randomness of the generated output bit stream.