Sripatum Review of Science and Technology

Causal Factors Influence the Cybersecurity Readiness Capability in the Royal Thai Air Force

2025-02-11T11:10:48+07:00

Royal Thai Air Force’s mission is to prepare and deploy air power to maintain national security. However, maintaining security must be conducted both physically and digitally. In particular, the digital aspect focuses on critical digital infrastructure. At the same time, cyber risks and threats must also be taken into consideration. This research aims to study and analyze the causal factors that influence the cybersecurity readiness capability in the Royal Thai Air Force. The researchers employ structural equation modeling, using a sample of 810 personnel working in digital technology and cybersecurity for the Royal Thai Air Force. The research findings indicate that the Royal Thai Air Force has the highest level of readiness for cybersecurity. Additionally, the factors of personnel, processes, and technology readiness capability positively influence the cybersecurity readiness capability in the Royal Thai Air Force. Therefore, developing cybersecurity capabilities for the Royal Thai Air Force should focus on personnel, processes, and technology simultaneously. Particularly, the aspects of personnel and processes are of great importance, as it will significantly enhance the sustainable strength of the Royal Thai Air Force’s cybersecurity. Additionally, we have proposed a cybersecurity framework aimed at enhancing the readiness and strengthening the effectiveness of the Royal Thai Air Force in preventing cyber threats.

Comparing The Carbon Dioxide Emissions from Residential Building Structures with Reinforced Concrete, Wood, and Steel Profiles: A Case Study of The D.D. Rakfa 4 House of The Ministry of Energy

2025-04-25T09:09:28+07:00

The accumulation of greenhouse gases is the cause of current climate change, with the construction and building sector accounting for 37% of total greenhouse gas emissions. Reducing greenhouse gas emissions from the construction sector can mitigate the impact of climate change. Realizing the importance of this, the researchers conducted a study comparing the carbon dioxide (CO₂) emissions of residential structures made of three different construction materials: reinforced concrete, wood, and steel. They used the D.D. Rak Fah 4 house structure from the Ministry of Energy. The study's objective was to compare the carbon dioxide (CO₂) emissions of various construction materials, aiming to design residential houses that can reduce CO₂ emissions as much as possible. The results of the research found that wooden-structured houses had the lowest carbon dioxide (CO₂) emissions at 583.61 kgCO₂eq/m², followed by steel-structured houses at 620.82 kgCO₂eq/m², and reinforced concrete-structured houses had the highest at 680.80 kgCO₂eq/m² when comparing reinforced concrete structures with wood and steel structures. The study revealed that it could decrease carbon dioxide (CO₂) emissions by 14.28% and 6%, respectively.

Semi-Supervised Left Atrium Segmentation in 3D Cardiac MRI Using Confidence-Guided Pseudo-Labeling

2025-09-05T11:48:14+07:00

This study proposes a semi-supervised learning framework for left atrium (LA) segmentation from three-dimensional cardiac Magnetic Resonance Imaging (MRI) using pseudo-labeling. The objective is to improve segmentation performance under limited labeled-data conditions. The proposed method integrates a 3D U-Net architecture with an iterative training pipeline and dynamic confidence-based pseudo-label refinement. Using the Medical Segmentation Decathlon dataset, experiments demonstrate that the semi-supervised model achieves a mean Dice Coefficient (DSC) of 0.9066 ± 0.0043 and a mean Average Hausdorff Distance (AHD) of 2.2409 ± 0.3661, surpassing the fully supervised baseline (DSC: 0.8519 ± 0.0395; AHD: 4.7696 ± 1.3128). Qualitative evaluation further confirms reduced false positives and enhanced anatomical precision. The results indicate that the proposed approach effectively leverages unlabeled data to achieve high segmentation accuracy with minimal manual annotation, providing a practical solution for clinical management of atrial fibrillation (AF).

Application of the Fuzzy Analysis Hierarchy Process to Develop AUN-QA

2025-09-10T11:34:11+07:00

This study aimed to apply the Fuzzy Analytic Hierarchy Process (FAHP) to determine the weights of the AUN-QA criteria, thereby enhancing accuracy and reducing ambiguity in internal quality assessment scoring. The resulting FAHP-based weights were compared with the original faculty assessment scores to calculate the Absolute Percentage Error (APE). The study involved 17 quality assurance experts from Sripatum University, who conducted pairwise comparisons and consistency ratio analyses to derive the weights. The findings indicated that “Expected Learning Outcomes” and “Teaching and Learning Approach” received the highest weights, underscoring their crucial role in academic quality management and alignment with institutional missions. The APE values for the Faculty of Engineering and the Faculty of Information Technology were 1.563% and 2.344%, respectively, both below the 3% threshold, which confirms the model’s accuracy and robustness. Overall, the results demonstrate that the FAHP approach strengthens the credibility of internal quality assessment systems and provides a strategic decision-support tool for guiding curriculum development, faculty capacity building, and continuous quality improvement in higher education.

An Experiential Learning Innovation for Analyzing the Impact of Tilt Angle and Orientation of Off-Grid Photovoltaic Panels on Maximum Power Generation

2025-12-23T16:42:50+07:00

This research aimed to design and develop an experiential learning innovation to analyze the relationship between the tilt angle and orientation of photovoltaic (PV) panels and their effects on maximum power generation in an off-grid solar energy system. The proposed learning system was intended to enhance students’ conceptual understanding of photovoltaic theory through hands-on experimentation and direct observation.The experimental system was installed at Rajamangala University of Technology Rattanakosin, Salaya Campus, Nakhon Pathom Province, Thailand (latitude 13.7958°N, longitude 100.3228°E). A single 250 W photovoltaic panel was connected to a simulated resistive load of 4 Ω. The research methodology consisted of two main phases. In the first phase, the PV panel orientation was adjusted toward the north, south, east, and west, while the tilt angle was varied at 0°, 15°, 30°, 45°, and 60°. Solar irradiance and electrical power output were recorded during the experimental period conducted on 23–24 July 2025, from 8:00 a.m. to 4:00 p.m. The experimental results indicated that a south-facing photovoltaic panel with a tilt angle of 15° produced the maximum power output of 211.04 W. This was followed by tilt angles of 0°, 30°, 45°, and 60°, which yielded maximum power outputs of 194.60 W, 153.47 W, 149.84 W, and 102.65 W, respectively. These findings demonstrate that the appropriate selection of panel tilt angle and orientation has a direct impact on enhancing the power generation efficiency of off-grid photovoltaic systems. In the second phase, student satisfaction with the experiential learning activity was evaluated using purposive sampling, involving 25 students who participated in the learning activities. A Likert-scale questionnaire was employed for data collection. The evaluation results revealed a high level of satisfaction, with a mean score of 4.52 from 5, and a standard deviation of 0.50. This outcome reflects that the developed learning innovation effectively promotes students’ understanding and practical skills in solar energy systems.

Wind Speed Forecasting using Machine Learning, Deep Learning, and Explainable AI: A Case Study of Phuket, Thailand

2025-12-16T09:54:42+07:00

This study aims to forecast hourly wind speed in Phuket Province to support wind power generation planning by applying and comparing machine learning and deep learning models in conjunction with explainable artificial intelligence (XAI) techniques. The dataset consists of historical hourly meteorological data covering a 12-month period from January to December 2024. The data were divided into an eight-month training set and a four-month testing set. Experimental results indicate that the machine learning model, particularly linear regression, outperformed deep learning models, achieving a mean absolute error (MAE) of 1.5679, a root mean square error (RMSE) of 2.0739, and a coefficient of determination (R²) of 0.8113. In contrast, the deep learning models yielded R² values ranging from 0.2581 to 0.5760. XAI-based analysis reveals that short-term lagged wind speed variables and temporal features are the most influential factors affecting wind speed, reflecting the daily wind patterns characteristic of coastal areas. The findings confirm that the linear regression model is well suited for hourly wind speed forecasting in terms of both predictive accuracy and interpretability. The resulting wind speed forecasts can be effectively applied to wind energy management, storage planning, and enhancing the operational stability of wind power generation systems.

Instructions for preparing the manuscript

2025-12-29T10:52:09+07:00

Editorial

2025-12-29T10:47:13+07:00