Journal of Science and Technology Kasetsart University

Acute Effects of Evening High-Intensity Exercise before Bedtime on Sleep Quality and Cognitive Performance

2026-08-26T09:56:26+07:00

The purpose of this study was to investigate and compare the acute effects of evening high-intensity exercise before bedtime on sleep quality and cognitive performance. The participants were 11 male undergraduate students from the Faculty of Sport and Health Science, Kasetsart University, Kamphaeng Saen Campus, aged 20–27 years. A randomized crossover experimental design was employed consisting of four experimental conditions (1) no exercise with bedtime at the scheduled time (12.00 a.m. – 08.00 a.m.) (2) exercise completed 4 hours before bedtime (3) exercise completed 3 hours before bedtime (4) exercise completed 2 hours before bedtime. The exercise protocol consisted of continuous treadmill running for
30 minutes at an intensity of 80–85% of heart rate reserve. Physiological variables, sleep quality variables and cognitive performance were measured. The data were analyzed using mean and standard deviation and one-way repeated measures analysis of variance. The level of statistical significance set at 0.05.

The findings indicated that completing high-intensity exercise 2 hours before bedtime resulted in reduced sleep quality, as evidenced by a higher mean sleeping heart rate, higher pre-sleep body temperature, reduced sleep efficiency, and longer sleep onset latency compared with the other conditions. However, it does not affect cognitive performance. These effects may be attributed to residual physiological and neural activation induced by high-intensity exercise. In conclusion, high-intensity exercise should be avoided within 2 hours before bedtime to prevent negative effects on sleep quality.

Freshwater Fish Diversity in Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, Thailand

2026-08-26T09:56:32+07:00

This study aimed to survey and identify freshwater fish diversity within Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom Province, using conventional taxonomic methods, and to establish reference specimens for future academic use. Fish sampling was conducted at eight freshwater sites representing different habitat types during two seasons: the rainy season (August 2024) and the dry season (March 2025). Water quality parameters, including dissolved oxygen, pH, temperature, and water transparency, were measured concurrently to assess physical factors associated with fish assemblage composition and distribution. A total of 3,954 fish specimens were collected and classified into 9 orders, 1 subseries, 18 families, and 50 species. The orders Cypriniformes and Anabantiformes exhibited the highest species richness, accounting for 44% and 22% of the total species, respectively. The most abundant species, based on the number of individuals, were Gobiopterus sp., Oreochromis niloticus, and Trichopsis vittata. Four non-native species were recorded, including Labeo rohita, Pterygoplichthys disjunctivus, Oreochromis niloticus, and Gambusia affinis. Water quality varied among seasons and site characteristics and was associated with differences in fish assemblage composition. Sites with low dissolved oxygen levels were dominated by species tolerant of degraded environmental conditions. The results provide baseline information for assessing freshwater fish diversity and serve as a preliminary guideline for conservation and water resource management within academic institutions.

Characterization and Antifungal Activity Evaluation of Clove Oil-Based Microemulsions Containing Fluconazole

2026-08-26T09:56:15+07:00

Topical drug delivery represents an effective approach for the management of cutaneous fungal infections, as it minimizes the risk of systemic adverse effects. Nevertheless, successful formulation design requires adequate enhancement of drug permeation through the skin barrier. The present study aimed to develop clove oil-based microemulsions incorporating 2% w/w fluconazole. Four formulations were selected from the microemulsion region identified through the construction of a pseudoternary phase diagram comprising clove oil, 1:1 mixture of water and propylene glycol, and Tween 80. The properties of the formulations, along with their antifungal activity against four Candida species including C. albicans, C. krusei, C. lusitaniae and C. tropicalis were evaluated. The experimental results indicated that all prepared formulations were oil-in-water microemulsions. They appeared as clear yellow liquids. Their pH and viscosity values were within an acceptable range for topical application. All clove oil-based microemulsions demonstrated antifungal activity against the tested strains, which can be attributed to eugenol, the principal constituent of clove oil. The inhibition zone diameter ranged from 23.0-30.3 millimeters for fluconazole-loaded microemulsions, which was significantly greater than that of the corresponding blank formulations. This research revealed that the developed clove oil-based microemulsions containing fluconazole show promise as topical antifungal formulations and warrant further investigation. The most interesting formulation for further study was FLZ-ME-1 which was composed of fluconazole, clove oil, 1:1 mixture of water and propylene glycol, and Tween 80 at 2.0%, 9.8%, 49% and 39.2% w/w, respectively.

Efficiency of Salad Vegetable Packaging Produced from Oyster Mushroom Bio-Film

2026-08-26T09:56:17+07:00

This research evaluated and compared the properties of bio-film derived from oyster mushroom (Pleurotus ostreatus) waste with commercial bio-plastics. The study focused on evaluating its mechanical strength, its application in preserving the freshness of salad vegetables, as well as its eco-friendliness. The methodology involved developing bio-films from oyster mushroom processing waste and conducting performance testing in real-world applications. The results indicated that the oyster mushroom-based bio-film exhibited a tensile strength of 28.45±1.20 MPa and maintained the freshness of salad vegetables comparably to commercial bioplastics. It restricted the average weight loss to 3.2% while maintaining high crispness scores throughout a 7-day storage period, performing comparably to synthetic commercial bioplastics. Furthermore, the bio-film achieved complete biodegradation within an average of 45 days under natural landfill conditions and reduced greenhouse gas emission (carbon footprint) by 30–43%. These findings demonstrate the preliminary feasibility of utilizing local bio-resources to develop eco-friendly packaging that aligns with the Circular Economy and sustainable zero-waste management goals.

Theoretical Perspectives on Digital Transformation and Practical Guidelines for Organizations and Policymakers in Advancing Sustainable Development within Regional Economic Contexts

2026-08-26T09:56:41+07:00

This study explores how the adoption of Artificial Intelligence (AI) and Enterprise Resource Planning (ERP) systems influences Environment, Society, and Governance (ESG) development within a regional organization in Ubon Ratchathani Province, Thailand. Using a qualitative case study approach, data were collected through in-depth interviews, direct observation, and document analysis, and analyzed using thematic and matrix coding in NVivo. The results revealed that AI and ERP systems enhanced operational transparency, accountability, and efficiency through data centralization and process automation. Notably, digital adoption improved environmental monitoring, employee collaboration, and governance compliance, aligning technological transformation with sustainability outcomes. Leadership commitment and employee empowerment were identified as key mediators driving positive organizational culture and acceptance of digital systems. This study contributes to the theoretical understanding of digital transformation by demonstrating how AI and ERP systems can advance ESG principles in regional enterprises. The findings offer practical implications for policymakers and managers seeking to promote sustainable digital transformation in developing economies.

Probabilistic Risk Assessment of In-Season Rice Production in Northeastern Thailand under Climate Uncertainty: An Application of Monte Carlo Simulation

2026-08-26T09:56:40+07:00

Rain-fed rice farming in Northeastern Thailand is highly vulnerable to climate variability. Previous deterministic assessments often overlook probabilistic risks and extreme events. This study employs Monte Carlo simulation to assess the risk of production loss. A stochastic model, using empirical elasticities and calibrated against statistics from the Office of Agricultural Economics, was used to simulate 10,000 climatic scenarios under the SSP2-4.5 pathway. The results indicate a mean production of 10.45 million tons. Crucially, the Value at Risk (VaR) at the 95% confidence level is 10.22 million tons, revealing a potential downside of 0.23 million tons during extreme years. Additionally, the probability of production falling below the baseline mean stands at 50.40%. This probabilistic approach provides a robust tool for managing climate-induced agricultural risks compared to traditional mean-based projections.

Effects of Superplasticizers on Flow and Strength of Mortars Using Low-Carbon High Early Strength and Type III Cements

2026-08-26T09:56:24+07:00

This research aimed to investigate the effects of polycarboxylate ether (PCE)-based and sulfonated naphthalene formaldehyde (SNF)-based superplasticizers on the flow spread and compressive strength of mortar mixtures incorporating one brand of low-carbon high-early-strength cement and two brands of Type III Portland cement. Two brands of each type of superplasticizer were used at dosage ranges of 0–1.6% and 0–2.1% by weight of cement for PCE and SNF, respectively. Mortar mixtures were prepared in accordance with ASTM C109 using a water-to-cement ratio of 0.485, a cement-to-sand ratio of 1:2.75, and sand with a fineness modulus of 2.80. Compressive strength specimens consisted of 5×5×5 cm mortar cubes tested at the ages of 18 hours, 3 days, and 7 days.The experimental results showed that mortar mixtures containing PCE-based superplasticizers began to exhibit segregation at approximately 0.8%, with clear segregation observed at approximately 1.2%. In contrast, mortar mixtures containing SNF-based superplasticizers began to exhibit segregation at approximately 1.4%, with more pronounced segregation occurring at approximately 1.7%. Consequently, the flow spread increased until reaching its maximum value before decreasing when the superplasticizer dosage exceeded the optimum adsorption level, resulting in instability within the mortar system. At high superplasticizer dosages, the compressive strength tended to fluctuate and become unstable due to segregation and bleeding, which caused a non-uniform internal structure and increased porosity within the mortar. However, within the appropriate dosage range, the compressive strength tended to increase due to improved cement particle dispersion and the development of a denser internal structure.Furthermore, mortars containing low-carbon high-early-strength cement combined with PCE-based superplasticizers exhibited relatively high and consistent flow spread values. In addition, mortars containing low-carbon high-early-strength cement showed higher flow spread values than those containing Type III Portland cement when the same dosage of SNF-based superplasticizer was used.

Development of an Event-Driven Portfolio Management Algorithm for Funding Rate Arbitrage Strategies to Enhance Sharpe Ratio in Cryptocurrency Markets

2026-08-26T09:56:23+07:00

Using the Funding Rate Arbitrage strategies of cryptocurrency Perpetual Futures markets brings many of its limitations to light. When faced with market volatility, a static equal-weight solution is unable to adjust immediately. On the other hand, Machine Learning approaches rely on forecasting Funding Rate values, which are characterized by mean-reversion, and this makes it difficult to predict profitably. This research thus develops an Event-Driven Portfolio Management Algorithm using Mean-Variance Optimization and compares it with three baseline strategies: Static Equal-Weight, Ridge Regression, and XGBoost with Walk-Forward Validation. Backtesting was done on actual data from Binance Futures for eight digital assets over a four-year period. Test results show that our method achieves an annualized return of 10.97% with a Sharpe Ratio of 30.47, outpacing Static by 22.4%, while both Machine Learning baselines fail to beat Static (Cohen's d less than 0.2). Our method never lost in any single month (43 out of 43 months) with Max Drawdown of only 0.18%. Statistical significance is confirmed through 8 methods, with Monte Carlo Bootstrap P (>0) equal to 99.63%. Sensitivity analysis confirms robustness and the Breakeven Fee ranges from 5.4 to 7.3 bps at least twice the actual transaction fee.

A Study on Flood Forecasting in the Upper Bang Pakong River Basin Using Forecasted Rainfall from Numerical Weather Prediction Model

2026-08-26T09:56:33+07:00

Due to the sparse distribution of rainfall gauging stations in the Upper Bang Pakong River Basin, this study aims to evaluate the efficiency of Numerical Weather Prediction (NWP) data for flood analysis at the Kgt.3 gauging station using the MIKE11 mathematical model. The model was calibrated to determine optimal parameters, yielding a Coefficient of Determination (R²) of 0.88–0.98 and a Root Mean Square Error (RMSE) of 0.31–0.44 m, indicating high reliability in hydraulic simulation. In the flood forecasting phase, a comparative analysis was conducted between scenarios with and without NWP rainfall data. The results revealed a variance of 30%–40% between the two approaches. From a risk management perspective, this degree of variance is considered acceptable and sufficiently reliable for serving as a decision-support baseline and early warning system. Consequently, integrating NWP rainfall data into flood forecasting enhances water resource management efficiency and provides a robust framework for developing flood prediction models in data-scarce basins in the future.

Application of Physics-Informed Neural Networks (PINNs) for Simulating Water Movement in Unsaturated Soils based on Richards’ Equation

2026-08-26T09:56:13+07:00

Understanding unsaturated soil water flow is essential for precision agriculture and water resources management. Traditional numerical methods for solving the Richards’ equation often encounter instability at the wetting front and computational burdens due to grid discretization. This study evaluates the performance of Physics-Informed Neural Networks (PINNs) in simulating one-dimensional soil-water flow under irrigation conditions, using HYDRUS-1D data for loamy soil as a reference. The PINN architecture features four hidden layers with 64 neurons each, incorporating a sinusoidal activation function and a soft-clipping technique to enhance automatic differentiation efficiency and ensure mathematical stability near saturation limits. Results indicate exceptionally high performance, with a correlation coefficient (r) exceeding 0.99 and a Root Mean Square Difference below 0.004 for both training and testing datasets. The model accurately captured the highly non-linear movement of steep wetting fronts and daily irrigation-induced dynamics without numerical oscillations. Despite minor initial oscillations at t = 0, PINNs demonstrated a robust ability to learn physical laws and mass conservation. This research confirms the potential of PINNs as reliable surrogate models, offering a mesh-free alternative for analyzing soil water dynamics.

Genome-Wide Association Analysis of Hundred-Seed Weight in Soybean Using SNP Markers and The Development of Molecular Markers for Breeding

2026-08-26T09:56:21+07:00

Soybean (Glycine max (L.) Merr.) is an important economic crop for food and feed industries. Hundred-seed weight (HSW) is a key trait influencing seed yield and quality. This study aimed to investigate the genome-wide associations of HSW and to develop DNA markers for soybean breeding. A panel of 101 soybean accessions was grown at the Chiang Mai Field Crops Research Center during the 2025 dry season, and HSW was measured. Genotyping was performed using low-pass whole genome sequencing, generating 1,076,102 SNPs for genome-wide association study (GWAS) using four statistical models: MLM, FarmCPU, SUPER, and MLMM. Nineteen missense SNPs significantly associated with HSW were identified across multiple chromosomes. Among them, SNPs within Glyma.03G094400 and Glyma.07G270000 were consistently detected by multiple models and selected for marker development. The SNP in Glyma.07G270000 was developed into a tetra-primer ARMS-PCR marker successfully, and the testing showed that it correctly classified genotypes in agreement with genomic data. Evaluation of the marker’s performance demonstrated an overall accuracy of 71.95% and a sensitivity of 98.1% to identify large-seeded accessions. These results indicate that the developed SNP marker is useful as a preliminary marker for enrichment of large-seeded genotypes to support breeding of soybean varieties with larger seeds and higher yield in the future.

Comparative Toxicity of Synthetic Insecticides and Bacillus thuringiensis var. kurstaki and Bacillus thuringiensis var. aizawai against the Fall Armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae)

2026-08-26T09:56:19+07:00

The fall armyworm, Spodoptera frugiperda (J.E. Smith) is an important pest of corn. It was first reported to have spread into Thailand in late 2018, with significant outbreaks in the provinces of Mae Hong Son, Tak, Kanchanaburi, and Nakhon Sawan. The fall armyworm starts damaging corn from 7 days after planting until the corn reaches the ear stage by feeding on the leaves, shoots, and ears, causing severe damage. There are several methods of controlling S. frugiperda larvae with the most common being the use of insecticides. The objective of this study was to determine the toxicity levels of insecticides and biopesticides (Bacillus thuringiensis) to fall armyworm. The experiment was conducted using the leaf-dipping method, where corn leaves were dipped into insecticides and then fed to the larvae. The results showed that after 24 hours, the insecticide Lambda-cyhalothrin had the lowest toxicity, with an LC₅₀ value as high as 49.344 ppm, while the insecticide Emamectin benzoate had the highest toxicity, with an LC₅₀ value as low as 2.578 ppm. The bioinsecticides Bt. var. kurstaki and Bt. var. aizawai were found to be less toxic than Emamectin benzoate, with LC₅₀values of 2.521×10¹¹ and 3.011´10¹¹ CFU/ml, respectively. These findings can be used as a guideline for selecting effective insecticides available in the market for controlling fall armyworm. Additionally, to prevent resistance development in pests, it is recommended to implement an integrated resistance. management (IRM) strategy, which is part of integrated pest management (IPM), when making decisions on insecticide use.

Identification of QTL and SSR Markers Associated with Cowpea Weevil (Callosobruchus maculatus) Resistance in Black Gram (Vigna mungo (L.) Hepper)

2026-08-26T09:56:28+07:00

Black gram [Vigna mungo (L.) Hepper] is an important crop in Asia. It is utilized in various ways, including direct consumption and processing in various industries, which increases the demand for higher yields of black gram. However, diseases and insect pests significantly impact the quantity and quality of black gram production. One of the major issues is the infestation of the cowpea weevil (Callosobruchus maculatus). Loss of seed yield in black gram during storage due to bruchid especially C. maculatus is the most significant stored insect pest of tropical legume crops and a serious problem for farmers and traders. Therefore, it is necessary to develop black gram varieties resistant to cowpea weevil, very little is known about the genetics of bruchid resistance in black gram. Previously, genetic mapping and identification of a quantitative trait locus (QTL) controlling seed resistance to bruchids in wild black gram were conducted. It was found that the resistance is controlled by 2 major QTLs, namely qVmunBr6.1 and qVmunBr6.2. This study aimed to develop and identify DNA markers associated with the gene controlling resistance to cowpea weevil in black gram. An F₂ population of 198 individuals, derived from a cross between the cultivated black gram Chai Nat 80 (CN 80; susceptible) and the wild black gram TVNu 1076 (TVNu 1076; resistant), was used. The population was subjected to genetic analysis using newly developed simple sequence repeat (SSR) markers. Marker–trait associations were evaluated by correlating marker genotypes with the percentage of seed damage caused by cowpea weevil through single marker analysis (SMA). Four markers, namely; CMSSR09, CMSSR11, CMSSR19, and CMSSR40, showed significant associations with resistance. Furthermore, one major QTL, designated qVmBr6.1.1, was detected between markers CMSSR11 and CMSSR19, accounting for 29.19% of the phenotypic variation in percentage of seed damage. These results provide a valuable basis for fine mapping and identification of genes conferring resistance to cowpea weevil in black gram.

Development of an Alginate–PVA Hydrogel Containing Zinc Oxide Nanoparticles for the Control of Pythium deliense–Induced Damping-Off and Nitrogen Release in Seeda Tomato (Solanum lycopersicum ‘Seeda’) Cultivation

2026-08-26T09:56:43+07:00

This study aimed to develop and evaluate alginate–polyvinyl alcohol (PVA) hydrogels incorporating zinc oxide nanoparticles (ZnO-NPs) and urea as multifunctional materials for controlling Pythium deliense, the causal agent of damping-off disease in Seeda tomato, while simultaneously improving nitrogen release efficiency in soil. The developed hydrogels exhibited concentration-dependent antifungal activity against P. deliense. Complete inhibition of fungal growth was achieved at a ZnO-NP concentration of 1.00% (w/v). Among the tested formulations, the A2.5P20 hydrogel demonstrated the highest antifungal efficacy (90.15%), which was significantly greater than that of the commercial fungicide Captan. The nitrogen release behavior of the A2.5P20 hydrogel was well-fitted to the Higuchi and Korsmeyer–Peppas models, indicating a hybrid diffusion- and swelling-controlled release mechanism governed by the polymer matrix. In contrast, the A2.5P40 formulation exhibited a high swelling ratio of 292% by the fifth cycle and underwent complete biodegradation within 54 days. In planta bioassays further confirmed that A2.5P20 significantly promoted the growth of Seeda tomato seedlings compared to the control and ZnO-NPs-free hydrogel treatments. Overall, the developed hydrogel shows considerable potential as a functional agricultural material for integrated disease management, reducing nitrogen loss, and improving nutrient use efficiency in sustainable agricultural systems.

Effect of Storage Time on Egg Quality of Quail Eggs Kept in Refrigerator Temperatures

2026-08-26T09:56:30+07:00

This study aimed to evaluate the changes in quality of Japanese quail eggs during storage. Eggs were stored for 0, 7, 14, 21, 28, 35, and 42 days under refrigerated conditions at an average temperature of 5 °C and relative humidity of 77%. External quality parameters, including shape index, shell weight, shell ratio, egg weight and eggshell thickness, were analysed. Internal quality analysed albumen weight, albumen ratio, albumen index, albumen height, yolk weight, yolk ratio, yolk index and Haugh unit. The results showed that storage duration had no significant effects on shape index, shell weight, shell ratio and egg weight (P>0.05). However, eggshell thickness was significant (P<0.05). Regarding internal quality, it was found that increasing the storage duration affected egg white quality (albumen weight, albumen ratio, albumen index, and albumen height, and yolk index. Haugh unit decreased significantly (P<0.05), In conclusion, storing quail eggs at a temperature of 5°C and a relative humidity of 77% can delay quality deterioration; eggs refrigerated for 42 days still met the Grade A quality criteria according to the ACFS 6704-2549 standard.