Sripatum Review of Science and Technology

Performance Testing of Herbal Compress Steamer using Automatic Control System

Parichart Keansom, Saksri Keansom, Promphak Boonraksa — Tue, 31 Dec 2024 00:00:00 +0700

This research aims to design and construct an herbal compress steamer with temperature and duration control. The research has begun to design a temperature control system divided into two control modes. Control mode 1 (steaming mode) heats the compress with steam for 20 minutes to simulate the drug. Control mode 2 (warming mode) warms up the compress with a starting temperature of 39 degrees Celsius. The summary of the herb dryer efficiency test is divided into two topics: 1. Setting the time by collecting the command accuracy in the first control mode, steam mode, found that the system can cut off and alert when the set time is 20 minutes(tested ten times), it was found that the error was 10% and 2. The optimal temperature setting for using the herbal compress ball was 42 degrees Celsius, and then the system cut off the temperature and prompted them to use the herbal compress ball. A total of 10 times tests found that the system has an error of 0 %. The test results concluded that the herbal compress steamer can control the temperature and duration of steaming the herbal compress. The operation of the machine meets the objectives of working in both modes.

Development of Carbon Powder from Burnt Coconut Husk for Latent Fingerprint Detection

Supicha Saengkaewsuk, Parinya Seelanan — Tue, 31 Dec 2024 00:00:00 +0700

Latent fingerprint detection is an essential process for evidence collection linked to the incident in forensic science. The carbon powder used in fingerprint detection is generally an imported product. In this research, the study of powder from burnt coconut husk for latent fingerprint detection was developed. The coconut husk used in this study was burnt into powder & analyzed by the scanning electron microscope with energy dispersive X-ray spectroscopy. Latent fingerprint detection on non-porous & semi-porous surfaces by the developed coconut husk powder was performed. The results showed that the powder from burnt coconut husk had an average size of 20.3 ± 2.5 micrometers under angular sheet with non-symmetric shape. The powders were applied to detect latent fingerprints imprinted on 4 types of surfaces & analyzed by the Mini Automated Fingerprint Identification System (Mini AFIS). It was found that the burnt coconut husk powder provided up to 60 points of minutiae whereas latent fingerprint quality was qualified to the criteria for fingerprint examination on all sample surfaces. Statistical analysis demonstrated that the minutiae & latent fingerprint quality collected from burnt coconut husk powder were not different from using imported powder at the significance level of 0.05. In conclusion, the developed powder from burnt coconut husk can be used as an alternative tool for latent fingerprint detection in forensic examination with advantages of easy preparation, low cost, & environmentally friendly material.

Development of Information System for Forecasting the Quality of Curriculum Administration in Business Computer Program, Management Science Faculty, Nakhon Pathom Rajabhat University

Pimpravee Rod-una — Tue, 31 Dec 2024 00:00:00 +0700

The objectives of this research were 1) to analyze factors affecting curriculum administration in Business Computer Program, Management Science Faculty, Nakhon Pathom Rajabhat University, 2) to analyze the structural equation model of the quality of curriculum administration affecting student quality and satisfaction of graduate users, and 3) to propose Information System for forecasting the quality of curriculum administration. The research population was 55 administrators and academic staff at Nakhon Pathom Rajabhat University related to the administration of the business computer curriculum, Faculty of Management Science, Nakhon Pathom Rajabhat University. Data collection was implemented by the researcher. Data were analyzed using Structural Equation Modeling (SEM) using the SmartPLS program. The research findings revealed that (1) factors of the curriculum quality at the higher education level arranged in order of importance from most to least: policies, things to support learning, student, family, teaching workload, teaching media, and teaching methods, respectively, (2) the quality of the curriculum has a direct effect on the quality of the students, the quality of students has a direct effect on the satisfaction of graduate users, and the quality of the curriculum has an indirect effect on the satisfaction of graduate users through student quality, and (3) Information System for forecasting the quality of curriculum administration of the Business Computer Program, Faculty of Management Science, Nakhon Pathom Rajabhat University has 4 main components: 1) curriculum data management, 2) curriculum quality forecasting, 3) display and reporting, and 4) data maintenance and updating. Also, there should be a Dashboard that can report forecast results.

The Model to Predict Depression Risk using Deep Learning Techniques from Basic Satisfaction in Subsistence A Case Study of Peoples in Bangkok

Methasith Mimual, Pin Chatkaewmanee — Tue, 31 Dec 2024 00:00:00 +0700

This research aims to predict the risk of depression based on external factors using deep learning models. It has three objectives: (1) to study, develop, and create a model to predict the risk of depression, focusing on a case study of individuals in Bangkok using deep learning techniques; (2) to test the effectiveness of the predictive model for depression risk in the case of people in Bangkok using deep learning techniques; and (3) to compare and evaluate the prediction model's results against a questionnaire designed by the researcher, which focuses on external factors that contribute to depression risk, and compare it with the depression assessment questionnaire provided by the Department of Mental Health, Ministry of Public Health. The case study involved 400 online survey samples from Bangkok residents. Both internal and external factor models were created, with score criteria adjusted according to international standards, and the results were compared. It was found that the model based on external factors achieved 99.92% accuracy, while the model based on internal factors achieved 99.93% accuracy, with only a 0.01% difference between the two. Both models had a mean squared error (MSE) of less than 0.0295 and a mean absolute error (MAE) of less than 0.1279. In conclusion, both models are consistent, and the research can be further developed to improve the accuracy of depression risk prediction

Classification of Cactus Species Using Deep Learning Model

Chakkarin Santirattanaphakdi, Nichapat Tulathan — Tue, 31 Dec 2024 00:00:00 +0700

This research aims to develop a deep learning model for cactus species classification and evaluate its accuracy. Three popular deep learning models—ResNet34, VGG16 and MobileNetV3—are trained on a dataset of 4,512 images representing 10 cactus species that are popular among consumers, based on data from cactus shops in Mueang Nakhon Ratchasima district, Nakhon Ratchasima province. The dataset is augmented through various image transformations to increase its size and diversity, allowing the model to learn better and avoid overfitting. The training process includes hyperparameter tuning and the application of the one-cycle policy to adjust the learning rate and momentum for optimal weight adjustment using backpropagation. The evaluation results on the validation dataset show that MobileNetV3 achieves the highest accuracy of 91.36%, with precision, recall and f-measure values of 91.46%, 91.36%, and 91.20%, respectively. When tested on an unseen dataset of 400 images to assess the model's performance in real-world scenarios, the expert-verified cactus species classification accuracy reaches 81.50%

Development of a Digital Platform and Mobile Application to Promote Online Commerce of Lava Durian in Sisaket Province

Phisan Sookkkhee, Sirikanlaya Sookkhee — Tue, 31 Dec 2024 00:00:00 +0700

This research aimed to develop a digital platform and mobile application, evaluate their efficiency, and assess user satisfaction in promoting online trade of Lava durian in Sisaket Province. The study employed a systematic development process, beginning with a user requirement analysis through field data collection from GI-registered durian farmers in three districts: Khun Han, Kantharalak, and Si Rattana. Following software engineering principles, the system was analyzed and designed to develop a cross-platform solution accessible via both website and mobile devices. The platform features notable security measures compliant with OWASP standards, GI registration-based vendor authentication, real-time inventory management, and a transparent delivery tracking system. The efficiency evaluation conducted by nine experts revealed the highest overall rating, with program and design aspects scoring highest, followed by visual elements and structure, and accuracy and content. User satisfaction assessment among 23 users also showed the highest overall rating. During the first month of implementation, 91 farmers registered on the platform, generating 250 transactions with a total value exceeding 250,000 baht and achieving a 98.5% transaction success rate. These results demonstrate that this platform serves as an effective online trading channel for Sisaket's Lava durian.

Algorithmic Assessment of Water Flow Potential for Sustainable Run-of-River Hydropower Plant Development

Kiattisak Sakulpan, Thanaphat Promwatthanaphakdi — Tue, 31 Dec 2024 00:00:00 +0700

Small-scale run-of-river hydropower plants typically exhibit a low Plant Capacity Factor (PCF) of only 14%, primarily due to variations in actual water flow rates. Meanwhile, the Ministry of Energy's renewable energy development plan emphasizes increasing electricity generation from small hydropower plants in areas with potential water resources nationwide. Therefore, developing techniques to assess hydropower potential and determine optimal installed capacity is critical. This study presents an algorithm designed to use actual flow rate data and probability distribution functions to enhance computational accuracy and address missing data issues. Additionally, the algorithm incorporates turbine operation constraints, such as the minimum flow rate required for operation, to improve reliability. The results demonstrate that the proposed algorithm effectively assesses hydropower potential in terms of both optimal installed capacity and annual electricity generation. Moreover, the study highlights the relationship between water flow rates and the continuous operation of hydropower turbines, supporting year-round efficiency. The algorithm is adaptable for real-world application in run-of-river hydropower plants, enhancing water resource utilization and aligning with national renewable energy development goals.

Instructions for preparing the manuscript

Wanayuth Sanngoen — Tue, 31 Dec 2024 00:00:00 +0700

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Editorial

Wanayuth Sanngoen — Tue, 31 Dec 2024 00:00:00 +0700

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