Science & Technology Asia

Optimizing Combined Holt–Winters and Decomposition Forecasting Models with the Whale Optimization Algorithm for Monthly Maximum of Daily 24-hour Average PM2.5 Concentrations in the Bangkok Metropolitan Region

Pradthana Minsan — 2026-03-31

This study proposes a novel hybrid forecasting model, Combined Holt–Winters and Decomposition optimized by the Whale Optimization Algorithm (CHD–WOA), to predict the monthly maximum of daily 24-hour average PM2.5 concentrations in the Bangkok Metropolitan Region. The model integrates decomposition and Holt–Winters exponential smoothing forecasts through weighted averaging, with 18 parameters simultaneously optimized using the Whale Optimization Algorithm. Ten methods are compared, including WOA-optimized variants, classical combination models, and benchmark approaches such as Box–Jenkins and Long Short Term Memory (LSTM). Performance is evaluated using RMSE for in-sample fit and MAPE for out-of-sample accuracy. The proposed CHD–WOA model achieves the lowest MAPE at four of eight stations and consistently outperforms benchmarks. Forecasts for 2025 accurately capture seasonal pollution peaks during the dry season, demonstrating the robustness and effectiveness of combining classical statistical techniques with metaheuristic optimization for complex urban air quality forecasting.

Color from Nature: Preserving and Promoting Thai Cultural Heritage through Natural Dyeing with Local Plants in Mae Tha District, Lampang Province

Thanika Hutakamol — 2026-03-31

This study focused on improving the traditional natural dyeing practices in the Mae Tha community in Thailand. Initial assessments revealed that yarns dyed using traditional techniques with local plant extracts, e.g., lac and sappan wood, often demonstrated inconsistent color quality and low color fastness. Potassium aluminum sulfate (alum) and tin (II) chloride were applied as mordants to improve both color uptake and color durability. The dyed fabrics appeared reddish in all cases. We improved the dyeing practices by carefully controlling the dye extraction processes and adjusting dyeing techniques, as well as using standardized measurement tools to ensure color consistency. Results from color measurements (tested by a modern scientific instrument) confirmed that the dyeing process increased color uniformity and fastness to washing and artificial perspiration. This study demonstrates that traditional practices can be successfully integrated with scientific techniques, thereby preserving cultural heritage while promoting sustainable and eco-friendly methods.

Development Coding to Support a Smart Greenhouse System for Household Vegetable Gardening with Convolutional Neural Network

Panyaporn Prangjarote — 2026-03-31

This study presents the outcomes of the “Coding Coaching and Coding Learning” project, which aimed to enhance technological literacy and programming proficiency among secondary school students and teachers. A smart greenhouse system prototype was developed to facilitate household vegetable cultivation, integrating Internet of Things (IoT) technologies through a project-based learning (PBL) approach. Participants received training in microcontroller programming, sensor integration, and automation system design using the Arduino ESP32 platform and the Blynk application for remote monitoring and control. The prototype enabled real -time environmental management of temperature, humidity, light, and soil moisture. A 35-day cultivation trial involving lettuce, spring onions, and holy basil demonstrated significant improvements in IoT and coding knowledge (scores increased from 2.1 to 4.3, p < 0.05). The greenhouse maintained optimal conditions, produced an average of 750 grams per cycle, and saved approximately 30% water compared to traditional irrigation. User satisfaction scores averaged 4.8/5. Additionally, an AI-based CNN model detected leaf abnormalities achieved an accuracy of 87.5% on 500 images, highlighting the potential for small-scale smart farming systems in education and practical application.

Parameter Tuning for Modified Ebola Optimization Search Algorithm in Vehicle Routing Problem with Time Constraints

Sirichai Yodwangjai — 2026-03-31

Vehicle Routing Problem with Time Windows (VRPTW) is a challenging problem because it involves finding the best routes for a fleet of vehicles to serve customers within time windows. VRPTW is an NP-hard Combinatorial optimization problem. In this paper, we propose an Ebola Optimization Search Algorithm (EOSA) framework to solve the VRPTW. An improved Ebola Optimization Search Algorithm modified with a control parameter is designed to solve the problem. Also, modified in EOSA is the impact of both the exploitation and exploration stages. However, the contact rate is a parameter that influences solution quality. The Taguchi method is used for tuning the parameters of the number of solutions and the contact rate. The experiment results showed the effectiveness of the proposed method, which was compared to the best-known solution.

Development and Performance Evaluation of a Mixed DEWMA–MA Control Chart for Detecting Shifts in Standard Deviation

Suganya Phantu — 2026-03-31

This study introduces a Mixed Double Exponentially Weighted Moving Average–Moving Average (DEWMA–MAS) control chart for monitoring process variability, with a focus on detecting shifts in standard deviation. By combining the long-term memory of the
DEWMAS chart with the short-term smoothing of the MAS chart, the proposed method improves sensitivity to small and moderate variability changes. Simulation results, evaluated using ARL, SRL, and MRL metrics, show that larger moving average windows enhance detection of moderate shifts, while smaller 𝜆 values are more effective for small shifts. Compared with conventional S, MAS, and DEWMAS charts, the DEWMA–MAS consistently demonstrates superior performance, particularly for moderate shifts, and remains competitive for large shifts. A soft-drink filling case study further validates its practical advantages, confirming the DEWMA–MAS chart as a more effective and reliable tool for modern industrial variability monitoring.

Simulation-Based Study of Fire Suppression Guidelines at Chatuchak Weekend Market Using the GAMA Platform

Chadchanon Kongwan — 2026-03-31

We developed an integer linear programming model in conjunction with simulation programming on the GAMA platform to plan fire suppression strategies, with a focus on resource allocation, specifically the number of firefighters and fire trucks, in order to minimize potential damage. This study uses the Chatuchak weekend market, located in Chatuchak District, Bangkok, Thailand, as a case study. The results from the model indicate that Zones 25 (Wood Carving, Spa, & Incense) and 28 (Second-hand Clothing, & Shoes) are the most difficult area for firefighters to access. Based on simulations conducted with GAMA, it was determined for example that if a fire starts in Zone 18 (Clothing, Camping Gear, & Leather Goods), two fire trucks, each carrying five firefighters, should be deployed. This approach results in the lowest median damage, affecting 15 shop units. The incident was resolved within 4 hours and 12 minutes on average. Conversely, if a fire starts in Zone 15 (Silverware, & Home Decoration), the optimal response involves deploying seven firefighters equipped with portable extinguishers on foot to be able to extinguish the incident within 1 hour and 47 minutes on average, and minimize damage to a median of 4.5 shop units. For fires in other zones, the simulation can similarly assign resource allocation and response strategies.

Enhancing Heat Transfer in Turbine Blade Cooling Channels Using Perforated V-Shaped Ribs: A CFD Study

Kumpanat Chaiphet — 2026-03-31

This study numerically investigates the thermal and hydraulic performance of square cooling channels fitted with V-shaped ribs, emphasizing the effect of rib perforations. Three configurations were examined: solid V-ribs (VR), circular-perforated V-ribs (CPV), and semi-circular-perforated V-ribs (SCPV), each incorporating one to three holes. Simulations were conducted for Reynolds numbers between 5,000 and 15,000 using a three-dimensional, steady-state, incompressible flow model with the RNG-𝑘𝜀 turbulence model. Performance was evaluated through local Nusselt number distributions, Nu/Nu0, friction factor ratio ( 𝑓 / 𝑓0), and thermal enhancement factor (TEF). Results indicate that rib perforations reduce recirculation zones and improve fluid mixing, producing more uniform wall heat transfer. Compared with solid ribs, perforated designs consistently achieve higher Nusselt numbers and TEF values, with semi-circular perforations showing slightly superior performance to circular ones. Additionally, perforations help limit frictional penalties typically associated with rib turbulators, especially when multiple holes are used. Overall, rib perforation emerges as an effective approach for enhancing heat transfer while controlling flow resistance in turbine blade internal cooling.

A Circularly Polarized 4×4 Microstrip Array Antenna Using Diagonal-Slot and Truncated-Patch Techniques for 2.4 Ghz WLAN Applications

Slamet Purwo Santosa — 2026-03-31

The use of the latest wireless telecommunications technology emphasizes the importance of antennas in improving the performance of communication systems. One recommended option is a microstrip antenna because it is lightweight, easy to integrate with the circuit, and effective in receiving circular polarization signals. This study focuses on designing, simulating, and manufacturing microstrip antennas with diagonal slots and a truncated square patch array 4×4 at a frequency of 2.4 GHz. The goal is to create microstrip antennas that are optimal for signal reception. The antennas use arrays to increase gain, while diagonal slots and truncated patches achieve circular polarization. The single-enumeration technique was used with the feed line method and simulated with CST Microwave Studio Suite 2019. The simulation results show the characteristic parameters of the 4×4 array microstrip antenna, such as working frequency 2.4 GHz, return loss -27.90 dB, VSWR 1.0984, bandwidth 55.7 MHz, gain 6.93 dB, and axial ratio 2.5 dB.

Investigation on Build Orientation for High Quality and High Tensile Strength Additive Manufacturing Parts

Sunil Kumar Tiwari — 2026-03-31

Polyamide-12 (PA 2200) from EOS GmbH is widely used in selective laser sintering (SLS) processes, but its powder quality degrades with repeated use, leading to powder loss. To maintain part quality, 50 to 70 percent of the used powder is regenerated, with only 8 to 12 percent of fresh powder contributing to usable parts. Aged powder must be removed to ensure desired mechanical properties. This study examines how build orientation affects the tensile properties and surface quality of parts made with EOSINT P395 and PA2200 powder, using Taguchi’s design of experiments. Results show that specimens printed in the z-axis orientation had the lowest tensile strength, while those in the x-axis orientation exhibited the highest values (43.47 MPa to 46.15 MPa). Additionally, combining new and aged powder, along with post-heating treatment, improved the surface quality of parts, particularly when reclaimed powders were reused.

Hybrid and Ensemble Learning Approaches for Accurate Breast Cancer Detection and Classification

Sridhar Siripurapu — 2026-03-31

Breast cancer is a leading cause of mortality among women worldwide, underscoring the need for accurate differentiation between malignant and benign tumors to support early diagnosis and timely treatment. Malignant tumors are invasive and often require aggressive therapy, while benign tumors are non-cancerous and localized. Advances in Machine Learning (ML) and Deep Learning (DL) have significantly enhanced diagnostic performance in healthcare. This study explores hybrid and ensemble learning approaches -including Bagging, Boosting (AdaBoost, Gradient Boosting, and XGBoost), and Stacking—combined with traditional ML classifiers such as Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest, alongside DL models including Convolutional Neural Networks and Long Short-Term Memory networks for breast cancer detection. Experiments were conducted on the Wisconsin Diagnostic Breast Cancer dataset. The workflow incorporated preprocessing, feature selection, and SMOTE-based class imbalance handling applied to training folds only. Model robustness was ensured through 10-fold stratified cross-validation with GridSearchCV hyperparameter tuning. Results show that ensemble and hybrid models outperform individual classifiers, with SVM-KNN and boosting methods demonstrating particularly strong and clinically relevant performance.

Data Mining Model Approach for Employment Prediction for University Graduates

Tewa Promnuchanont — 2026-03-31

Graduate employability prediction has become increasingly important as universities seek to understand the factors influencing employment outcomes and to improve academic planning. However, prior studies in this area often rely on limited datasets, evaluate only a narrow range of models, and lack systematic feature assessment, which restricts the robustness and generalizability of their findings. This study addresses these limitations by developing a comprehensive multi-model data-mining framework to predict the employability of graduates from Rajamangala University of Technology Lanna (RMUTL). A dataset of 4,352 graduate records from the 2023 academic year was analyzed. Three filter-based featureselection techniques—chi-square, information gain, and correlation-based evaluation—were applied to identify the most influential predictors. Five machine-learning algorithms (Decision Tree, Random Forest, Gradient Boosted Trees, Naïve Bayes, and K-Nearest Neighbors) were trained and evaluated using accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest achieved the highest accuracy (83.72%), while Gradient Boosted Trees yielded the highest AUC (0.813), indicating superior class-separation performance. Key predictive factors identified across models included curriculum, education level, department, faculty, campus, gender, and GPA level. This study provides a structured comparative modeling framework and identifies institution-specific predictors that influence graduate employability. The findings offer practical implications for curriculum enhancement, evidence-based academic planning, and career-guidance development aimed at improving employment outcomes for RMUTL graduates.

Technical and Financial Feasibility Analysis of Hydrogen Production from Palm Oil Mill Effluent Biogas for Fuel Cells in Supporting Energy System Stabilization from Intermittent Power Supply Fluctuations

S.P. Ahmad — 2026-03-31

Hydrogen production from Palm Oil Mill Effluent (POME) biogas via Dry Methane Reforming (DMR), integrated with a Proton Exchange Membrane Fuel Cell (PEMFC), presents a technically and economically viable solution to enhance voltage stability in Indonesia’s 20 kV distribution networks. Process simulation using Aspen HYSYS yielded 1,085 kg/h of hydrogen, stored in three spherical tanks (16.2 m diameter each), and used to power a 2.115 MW PEMFC system with 33.76% electrical efficiency. Grid analysis using PSS^® Sincal demonstrated improved voltage levels from 12.51–19.67 kV to 19.5–22.16 kV, eliminating undervoltage and reducing reliance on the 150 kV grid. Economically, the system achieves a revenue-to-cost ratio of 2.42, supported by annual hydrogen revenue of USD 19.05 million and diesel backup cost savings of USD 7.88 million. Financial metrics confirm project viability with a Net Present Value (NPV) of USD 49.58 million, an Internal Rate of Return (IRR) of 53.61%, a payback period of 2.7 years, and a Benefit-Cost Ratio (B/C) of 2.8. The integrated system supports Indonesia’s national targets for 23% renewable energy by 2025 and a 29% GHG emission reduction by 2030.

A 3D Virtual Object Repository Model for the Preservation of Lan Xang Buddhist Art

Yuttasak Thongsan — 2026-03-31

Lan Xang Buddhist Art is a vital cultural heritage of Northeastern Thailand, rich in historical and religious significance. This study aimed to (1) develop a 3D virtual object repository model for preserving Lan Xang Buddhist Art and (2) evaluate a prototype system based on this model. Using a research and development (RD) approach grounded in systems theory, data were synthesized from: (1) a comparative analysis of ten international 3D repository systems, (2) expert interviews (n = 15) in relevant fields, and (3) a user needs survey (n = 32). A prototype was developed using the SDLC framework and evaluated by five experts and 50 users. The proposed model consists of three core components: (1) education and research, (2) access and usability, and (3) preservation and dissemination. The
system supports portable devices and secure access, allowing high-resolution 3D storage of Lan Xang artifacts. Evaluation results showed high system performance (M = 4.66, S.D. = 0.62) and very high user satisfaction (M = 4.63, S.D. = 0.55). This model facilitates the digital preservation of art object forms, patterns, and cultural contexts, enhancing public and academic accessibility. It provides a scalable framework for cultural heritage management in the digital age.

OIDS-CS: An Efficient Optimal Intrusion Detection System for Cyber Security using Hybrid Artificial Intelligence

Yogomaya Mohapatra — 2026-03-31

Cybersecurity systems face significant challenges in intrusion detection due to high false alarm rates and the inability to accurately detect evolving attack patterns in large-scale network traffic. To address this problem, this paper proposes an Optimal Intrusion Detection System for Cybersecurity (OIDS-CS) based on a hybrid artificial intelligence framework. The proposed OIDS-CS framework for DDoS detection consists of three main stages: preprocessing, feature selection, and intrusion detection and classification. In the preprocessing stage, the network traffic data are cleaned to remove noise and redundancy, improving the quality of the input for subsequent analysis. In the feature selection stage, the extracted features are optimized using the Improved Buzzard Optimization (IBO) algorithm, which minimizes correlation among features and ensures that only the most significant and discriminative features are retained for DDoS detection. Finally, the Residual Artificial Neural Network (RANN) is employed for intrusion detection and classification. The optimized features are used as input to the RANN, which predicts whether a DDoS attack is present or not. The outputs are classified into two categories: DDoS present or DDoS not present. This structured approach not only reduces computational complexity but also improves detection accuracy and robustness against evolving DDoS attack patterns.

Influence of Robot-assisted 3D-Printing Process Parameters on the Mechanical Properties, Structural Robustness, and Surface Finish of Clay and Sintered Clay

Nopporn Bukwan — 2026-03-31

This research investigates optimization of process parameters in robot-assisted threedimensional (3D) printing of clay and sintered clay, focusing on nozzle diameter, print speed, layer height, and clay printability to improve mechanical strength, structural stability, and surface quality for sustainable architectural applications. Three nozzle sizes (3.0, 4.0, and 5.0 mm) were tested with print speeds from 100-500 mm/s and layer heights of LH60-160, while clay composites with controlled water-to-clay ratios were prepared to evaluate their effects on extrusion and structural performance. Results show that a smaller nozzle diameter of 3.0 mm combined with slower print speeds of 150-250 mm/s produced the strongest specimens, achieving compressive strengths up to 8.5 MPa and flexural strengths of 4.2 MPa while also generating smoother surfaces and more uniform layer deposition. Larger nozzles and higher speeds increased defects such as voids and cracks, reducing mechanical reliability. Lower layer heights (LH60) improved interlayer bonding and reduced porosity, whereas higher layers weakened adhesion. Optimal material performance occurred at 23.2% moisture content, balancing workability and structural stability. Excess moisture caused sagging while insufficient moisture increased extrusion resistance and cracking.

Numerical And Experimental Study On Shear Strength Of High Strength Reinforced Concrete

Cherukupally Rajesh — 2026-03-31

Shear strength of concrete is a vital consideration in the analysis and design of structures, as its failure occurs abruptly and can result in catastrophic consequences. In the current study, two different grades of reinforced concrete beams were examined, each with three shear span-to-depth ratios (a/d) of 1, 2, and 3. The beams were both cast and experimentally tested. Additionally, numerical simulations of the beams were conducted using the ATENA software to model and analyze their performance. In ATENA, the material for concrete was assigned using the cementitious2 and cementitious2-user models. The cementitious2 model includes the Euro-Code and Model-Code as default models, both of which were utilized in the study to determine the shear strength of reinforced concrete beams. For the Cementitious2user material model, the compressive stress-strain model can be user-defined. In this study, the GRK stress-strain model and Mander’s stress-strain model were used as input to calculate the shear strength of the reinforced concrete beams. The results showed that the shear strength of the test specimens increased with the concrete strength for all a/d ratios. However, as the a/d ratio increased from 1 to 3, the shear strength decreased by approximately 60%. The shear strength values obtained using ATENA with various models were compared with the experimental results, and it was found that the shear strength behavior from the numerical simulations closely matched the experimental results.

A Comparative Framework of VAR and LSTM Networks for Soil Moisture Prediction Using Atmospheric Data

Tawsif Mahmud — 2026-03-31

Accurate prediction of soil moisture is vital for optimizing water resource management and agricultural productivity, yet it remains a challenging problem due to the complex, nonlinear relationships between soil dynamics and atmospheric conditions. To address this, this study proposes a comprehensive forecasting framework comparing conventional statistical methods with advanced deep learning techniques. The key contribution of this work is a robust methodological pipeline that integrates a comprehensive set of ten meteorological variables, including temperature, relative humidity, wind speed, radiation, and vapor pressure deficit, following rigorous time-series preprocessing and stationarity checks. Specifically, we formulate and compare a Vector Auto-regression (VAR) model against a Long Short-Term Memory (LSTM) network for one-day ahead forecasting of soil moisture during cropping seasons. To evaluate the efficacy of the proposed techniques, we utilized historical data spanning from 1972 to 2024. The evaluation results demonstrate that the LSTM network significantly outperforms the conventional VAR model, achieving exceptional accuracy with an R2 exceeding 0.99 and notably lower RMSE values. These findings validate the reliability of the proposed LSTM framework and establish its superiority over traditional statistical approaches for precise soil moisture estimation.

Groundwater Potential Zonation Through Integration of Remote Sensing, Geographic Information Systems and AHP Techniques in FCT Abuja

Farouq Ado Umar — 2026-03-31

Groundwater is a vital freshwater resource supporting domestic, agricultural, and industrial demands, especially in semi-arid regions with limited surface water. This study maps groundwater potential zones in the Federal Capital Territory (FCT), Abuja, Nigeria, using an integrated approach combining Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). These methods enable wide spatial coverage and objective analysis where conventional data are scarce. The study uses thematic datasets—slope, drainage density, lineament density, rainfall, topographic wetness index (TWI), land use/land cover (LULC), geology, and soil type—derived from satellite imagery, government sources, and field surveys (2015–2024). Parameters were weighted with AHP, processed in ArcGIS 10.7.1, and overlaid to produce a composite groundwater potential map. Results show steep slopes dominate over 80% of the area, limiting recharge, while only 2.29% favors infiltration. Moderate to low drainage density supports balanced recharge, while higher densities restrict it. Moderate lineament density aids recharge, though highdensity zones risk contamination. Most areas have low TWI, with small recharge hotspots. Vegetation dominates land cover, supporting infiltration, while urbanization hinders it. Overall, the model identifies high, moderate, and low potential zones, supporting sustainable groundwater management.

Comparative Analysis of V-Shaped Rib Configurations for Enhanced Cooling Performance in Gas Turbine Blades

Kumpanat Chaiphet — 2026-03-31

This study conducts a numerical analysis of six different V-shaped rib configurations installed in a square internal cooling passage (aspect ratio, AR=1:1), representing a typical gas turbine blade cooling channel. Using the RNG 𝑘 −𝜀 turbulence model, simulations were
conducted for Re ranging from 5,000 - 30,000 to evaluate heat transfer and flow characteristics through three primary metrics: the Nusselt number (Nu), friction factor ( 𝑓 ), and thermal enhancement factor (TEF). As Re increases, secondary vortices intensify, enhancing mixing and heat transfer. Although continuous V-shaped ribs (Model 1) provide the greatest heat transfer rate, they generate considerable flow resistance. Alternating continuous ribs (Model 2) achieve the highest TEF, particularly at lower Reynolds numbers, by balancing heat transfer improvement with moderate pressure losses. Discontinuous and alternating rib designs (Models 3–6) effectively decrease the pressure drop while sustaining acceptable heat transfer levels. These findings emphasize the importance of rib geometry in controlling turbulence, pressure loss, and thermal performance, offering valuable guidelines for optimizing internal cooling designs in turbine blades.

Development of an Innovative Smart Walking Aid with Electric Motor to Enhance Safety and Satisfaction Among Older Adults

Punpaphatporn Bunprom — 2026-03-31

Thailand’s rapidly aging population faces increasing mobility challenges, with falls representing a major cause of injury, dependence, and reduced quality of life. This study reports the development and pilot evaluation of a smart walking aid with an electric motor designed to improve walking safety and confidence among older adults. A quasi-experimental, mixed-methods design was employed, involving ten older adults with mobility limitations and their ten primary caregivers. Walking confidence was assessed before and after use of the device using a paired-samples t-test, and user experiences were explored through semistructured in-depth interviews. Quantitative findings revealed a statistically significant improvement in walking confidence, with mean scores increasing from 2.65 (SD = 0.50) to 4.18 (SD = 0.42), t(9) = 7.15, 𝑝 < 0.001, Cohen’s d = 2.25. Qualitative feedback reinforced these results, highlighting enhanced feelings of safety, reduced physical exertion, and decreased caregiver burden. Participants recommended practical refinements such as simplified height adjustment, wireless charging, larger control buttons, and safety features including voice alerts and floor-level illumination. These findings emphasize the importance of user-centered design in assistive technology and demonstrate the potential of smart walking aids to promote mobility, independence, and caregiver relief, aligning with Thailand’s public health strategy and the Sustainable Development Goals (SDG 3 and 9).