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Three-dimensional numerical modeling for assessing airborne infection risk in hospital waiting rooms with various ventilation approaches
(2026-06-01)
Suebyat, Kewalee
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Pochai, Nopparat
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Sooknum, Jenjira
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Oyjinda, Pravitra
Airborne infectious diseases, such as COVID-19, TB, MERS, and SARS, constitute a profound threat to public health and quality of life. These pathogens are transmitted primarily via atmospheric particles, especially within clinical environments, where they often circulate. Effective ventilation controls to mitigate pathogens and air pollution are thus essential for reducing hospital-based transmission of airborne infections. The purpose of this research is to assess the risk of airborne infectious diseases within a hospital in Thailand using a mathematical model. Specifically, the finite difference technique is employed to estimate carbon dioxide (CO<inf>2</inf>) concentration as a proxy for indoor air quality to indicate and assess the risk of airborne infectious diseases. The hospital layout is categorized into waiting areas and circulation areas with disparate occupant densities. Three simulation scenarios are conducted, accounting for variations in ventilation rates and architectural structure of hospitals. The results of this research demonstrate that CO<inf>2</inf> concentration can be effectively quantified as a proxy for indoor air quality within hospital environments. These calculated CO<inf>2</inf> levels are subsequently used to model the risk of airborne infection at a hospital, providing a robust framework for assessing this risk. Crucially, by integrating ventilation dynamics that reflect the physical constraints and structure of the hospital, this research enables precise evaluation of infection risks. The findings indicate that ventilation control can reduce the incidence of airborne infection, with significant practical utility in real-world clinical settings.
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Numerical Simulation of Phycoremediation for Nutrient Removal Using the Extended Monod Model with Saulyev Technique
(2026-01-01)
Vongkok, Areerat
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Chumsri, Anantanit
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Pochai, Nopparat
Nowadays, as water pollution is increasing from agricultural sectors due to nitrogen and phosphorus, phycoremediation is used to remove nutrients with microalgae. In this paper, a mathematical model is developed to use the extended Monod model to analyze the growth of microalgae that have adsorbed nutrients by considering various flow velocities and levels of nitrogen as effects of biomass concentration. This model has been calculated with the numerical finite difference method using the Saulyev technique. Numerical simulations show results for various scenarios with flow velocities and levels of nitrogen that affect the growth of microalgae.
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A Biomathematical Clustering Framework for Classifying Neuromechanical Performance Profiles in Amateur Boxers
(2026-01-01)
Punthipayanon, Sirichet
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Chottidao, Monchai
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Manilam, Surasak
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Thongtha, Kaboon
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Pochai, Nopparat
Boxing effectiveness is strongly influenced by neuromuscular power generation and the efficient transmission of force through the lower extremities; however, traditional evaluation methods tend to assess these variables in isolation across athletes. A biomathematical clustering strategy provides a structured approach to extracting coherent performance patterns from complex, multidimensional biomechanical datasets. The present investigation proposes a biomathematical clustering model to categorise amateur boxers and quantify between-cluster variation, thereby facilitating tailored training interventions. A cohort of 30 amateur competitors underwent a series of standardised biomechanical tests, including Muscle Power (MP), reaction time (RT), rear-leg ground reaction force (GRF) relative to body mass, and maximal cross-punch (MCP) force output. Before analysis, all variables were rescaled through min–max normalisation. Unsupervised classification was executed via K-means clustering. The quality of clustering was assessed using indices of compactness and separation, specifically the Dunn Index and Davies–Bouldin Index. In contrast, the appropriate cluster count was determined using within-cluster sum of squares (WCSS) interpreted via the elbow method. Statistical procedures, including post hoc testing and effect size computation, were applied to evaluate intergroup differences. Additionally, principal component analysis (PCA) was utilised to project the data into a reduced-dimensional space for clearer visual interpretation of cluster distinctiveness. All computational procedures were implemented in Python. The analysis supported a three-cluster configuration. Cluster 3 (40%) exhibited superior performance characteristics, including elevated MP (7,700 ± 3,500 W), reduced RT (0.18 ± 0.03 s), and greater rear-leg GRF (1.55 ± 0.18 BW) relative to Cluster 2 (p ≤ 0.002; d = 1.35–2.45). In contrast, Cluster 2 (46.7%) was characterised by diminished MP (4,000 ± 1,400 W) and prolonged RT (0.26 ± 0.07 s), whereas Cluster 1 (13.3%) showed moderate values across all measured variables. The biomathematical clustering framework successfully distinguishes discrete neuromechanical profiles among amateur boxers, thereby enabling cluster-specific training strategies and enhancing individual performance optimisation.
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Health Behavior and Emotional Responses of Thai National Team Athletes during the COVID-19 Pandemic: A Comparative Study of Individual and Team Sports
(2025-08-01)
Pluemsamran, Theeratheeta
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Pariyavuth, Pariya
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Pochai, Nopparat
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Panurushthanon, Phichayavee
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Punthipayanon, Sirichet
Objectives: COVID-19 significantly impacted athletes’ health behavior and emotional well-being. Disruptions to training routines, competition schedules, and social structures raised concerns about psychological resilience, especially for elite athletes. In this study, we (1) adapted and validated the Emotional State Questionnaire (EST-Q-2) for Thai athletes, (2) examined the emotional responses and associated health behavior patterns of Thai national team athletes during the COVID-19 pandemic, and (3) compared emotional states between individual and team sport athletes. Methods: We surveyed 280 Thai national team athletes (146 male, 134 female) preparing for the 19th Asian Games. Participants completed the culturally adapted EST-Q-2, measuring 5 dimensions: depression, general anxiety, panic disorder, fatigue, and insomnia. We compared emotional responses by sport type. Results: The most prominent symptoms reported were fatigue and insomnia (M = 3.26), general anxiety (M = 2.84), depression (M = 2.35), and panic disorder (M = 2.17). We found no statistically significant differences between individual and team sport athletes across emotional dimensions. The adapted EST-Q-2 demonstrated strong reliability (Cronbach’s α = 0.80). Conclusion: The COVID-19 pandemic adversely affected the emotional states and health behavior of Thai national athletes, with high levels of fatigue and sleep disturbances. The lack of significant differences between sport types indicates a universal psychological impact, underscoring the need for targeted mental health interventions regardless of sport category.
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A mathematical model of water pollution measurement in a stream using a collocation method with a higher order Legendre polynomial
(2025-08-01)
Thongtha, Kaboon
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Pochai, Nopparat
In environmental research, challenges with water contamination assessment are generally prevalent. Through data collection, pollution levels in a system may be determined. This is quite challenging and involved; the measurements of what was measured vary from one point to another in every location. The governing equations for a uniform flow pollution dispersion model are used in water quality modeling. The advection-diffusion-reaction equation used in water quality model-ing for a uniform flow stream is a stable pollution dispersion model. This study presents a one-dimensional mathematical model for measuring stream water quality by collocation higher order Legendre polynomial functions. A water pol-lutant concentration can be approximated using the collocation method. A related water quality quantification method may also be employed with the suggested mathematical simulation to approximate the solution.
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A simple mathematical model for assessing water quality in a closed-system shrimp farm
(2025-08-01)
Thongtha, Kaboon
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Pochai, Nopparat
The problem of wastewater from shrimp farming affects the environment, both in terms of wastewater discharge and soil deterioration. Wastewater management is also quite expensive for the production costs of shrimp farmers. Therefore, the approach to using shrimp farming technology in closed-system farms is proposed, which reduces wastewater discharge into the environment and reduces the cost of wastewater treatment for farmers. This research presents a simple mathematical model for assessing water quality in such closed-system shrimp farms. The method for determining various parameters for determining the mathematical model is presented. The model solution is estimated by the Runge-Kutta method of the fourth order. This research simulates the situation to compare the different parameter values in each situation, which affect the level of water quality in closed-system shrimp farms at different times. The research found that the initial water quality, the rate of chemical reaction of pollutants, the rate of pollution formation, the rate of pollution decomposition, the rate of decrease in pollution concentration due to water circulation between the farm and the water treatment pond, and time all affect water quality. The results from the calculation can help closed-system shrimp farmers know the trend of pollution concentration changes in closed-system shrimp farms in order to find ways to develop techniques for improving water quality.
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A Simulation of Shoreline Evolution with a Groin Structure Using an Alternative Machine Learning Algorithm
(2025-07-01)
Manilam, Surasak
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Pochai, Nopparat
Uneven sediment transport is a major cause of coastal erosion. Using groin structures is one method to help slow the outflow of sediment from the shoreline. Studying coastal behavior and forecasting future shoreline changes are crucial for managing and assessing the viability of remediation strategies. This research presents simulations of shoreline evolution with a single groin structure using two different methods, such as mathematical modeling and an alternative machine learning. A mathematical model is a representation of a real-world shoreline evolution phenomenon using partial differential equations. A machine learning algorithm is designed to learn patterns and relationships directly from real data. In this research, an alternative machine learning algorithm is designed to learn patterns and relationships directly from mathematical simulation data and let the machine make a decision in a situation that it has never learned before. For mathematical modeling, we introduced a one-dimensional model to predict the shoreline evolution. The initial and the boundary conditions with related parameter settings are introduced. The Saulyev finite difference method is used to obtain the approximated solution. An alternative machine learning algorithm for unexpected shoreline evolution prediction is also proposed. For alternative machine learning simulations, we identified six suitable features for the training dataset and developed an alternative K-nearest neighbor algorithm. It provides a way of predicting the evolution of the shoreline with a single groin structure. Additionally, an exact solution in an ideal scenario is used to test the precision of the simulation as well. The results show that the Saulyev technique outperforms an alternative K-nearest neighbor algorithm due to the lower root mean square error value. Both results of them are closed together. According to the research, mathematical modeling outperforms the KNN regression technique in terms of computational effectiveness during time periods of 0.5, 1, 5, 10, 15, and 20 years. Based on the modeling configuration and parameter simplicity, the KNN algorithm is still a good option for non-expert users.
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A Zero-dimensional Mathematical Model of PM2.5 Measurement due to Daily Vehicle Density in Bangkok
(2025-05-01)
Khum-Un, Seree
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Pochai, Nopparat
Air pollution, particularly particulate matter smaller than 2.5 microns (PM2.5), has grown to be a serious issue that has an impact on people's health, especially the respiratory system. There are several studies that have found that the level of PM2.5 in the Bangkok region is high, as is how it affects individuals with respiratory illnesses. In this research, a numerical simulation of PM2.5 concentration was performed using a zero-dimensional model of PM2.5 measurement due to the daily vehicle density in Bangkok. It is evident that the wind speed and daily vehicle density have an impact on the simulated PM2.5 concentration in Bangkok. The daily density of vehicles greatly influences PM2.5 emissions. Wind speed was measured in this experiment. The hourly vehicle density in Bangkok, which was represented by calculating functions for wind speed and PM2.5 emission rate, is what produces the computed PM2.5 emission rate. The simulation includes three 24-hour scenarios: low vehicle density with medium wind speed, high vehicle density with low wind speed, and medium vehicle density with high wind speed. All of the models indicated that the PM2.5 level would drop as wind speed increased and vehicle density decreased. The daily vehicle density and wind speed are two factors that affect the PM2.5 level. Focusing, especially on wind speed, will not always lead to PM2.5 reductions. However, daily vehicle density also has a significant role in PM2.5 management. Wind speed and vehicle density influence PM2.5 concentrations, with three scenarios demonstrating that higher wind speed and lower vehicle density reduce PM2.5 levels. While wind speed helps to reduce PM2.5 levels, vehicle density also has a substantial impact on emissions. Managing PM2.5 requires addressing both daily vehicle density and wind speed, as focusing on only wind speed may not always result in reductions.
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A One-Dimensional Numerical Simulation of Oil Spill Control in a Coastal Bay Using a Fourth-Order Explicit Finite Difference Method
(2025-01-01)
Kasamwan, Teerat
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Pochai, Nopparat
Oil spills in the sea have both short-term and long-term consequences that need proper management and restoration. The damage can take years or even decades to recover fully. Methods like absorbents, dispersants, bioremediation, mechanical recovery, and in-situ burning are used to mitigate the impacts of oil spills. Each method has its limitations and should be chosen carefully based on the severity of the spill to minimize environmental damage and restore marine ecosystems effectively. This research considers a one-dimensional mathematical model for an oil spill in a coastal bay, incorporating delayed removal mechanisms. The governing equation for an oil spill in this coastal bay context with delayed removal is introduced, alongside the initial condition and boundary conditions associated with oil spill scenarios. A mathematical model is proposed to simulate delayed removal mechanisms. The model solutions are approximated using a fourth-order forward time-centered space finite difference method. The simulations explore two scenarios: instant and delayed removal mechanisms. In the instant removal scenarios, simple average rates of oil removal and basic water flow behaviors are modeled, while the delayed removal scenarios simulate more realistic oil spill conditions. Consequently, the concentration of oil relative to source rate over time is analyzed. The simulations reveal that as the efficiency of the removal mechanism improves, the oil concentration decreases over time. Physically, this reflects that effective management of oil removal leads to a progressive reduction in oil concentration as time advances. According to the research, oil spill concentration is reduced when oil removal mechanisms are more effective. By contrasting a second forward time center space technique and a fourth-order forward time center space technique, it shows the significance of selecting the most effective method for a given simulation circumstance. The simulation results indicate that the concentration associated with the delayed removal mechanism yields less favorable recovery outcomes compared to the prompt removal mechanism across all scenarios. This observation is consistent with the fundamental principle that effective oil spill management should result in a reduction in oil concentration within marine environments. The findings of this study underscore that, in all cases, postponed oil removal exacerbates the detrimental impact on seawater recovery relative to expeditious removal. Consequently, the prompt and efficient removal of oil spills is imperative in mitigating the extent of oil contamination in marine waters.
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One-dimensional Numerical Simulations of Oil Spill in a Coastal Bay with Delayed Removal Mechanisms
(2025-01-01)
Kasamwan, Teerat
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Pochai, Nopparat
Oil spills in marine and coastal areas can result from various activities, such as oil drilling, transportation, shipping, tank cleaning, illegal disposal of oil-contaminated or used water, and accidents like ship collisions or sinking incidents. These events result in oil slicks or tar balls that form in the sea and eventually drift towards the coast. There are many methods for addressing oil spills, such as containment, employing skimmers, chemical dispersants, bioremediation, burning, beach cleanup, environmental restoration, and monitoring and assessing long-term impacts on the shoreline. A delay in oil spill response can have severe consequences for both the environment and local economies. When oil spills occur, rapid and effective action is essential to minimize damage. Unfortunately, delays in response can exacerbate the problem and lead to more extensive environmental harm. In this research, a one-dimensional mathematical model for an oil spill in a coastal bay with delayed removal mechanisms is considered. The governing equation for an oil spill in a coastal bay with delayed removal mechanisms is introduced. The initial and boundary conditions for an oil spill in a coastal bay are also presented. A mathematical model incorporating delayed removal mechanisms is proposed. The solution of the proposed model is approximated using a finite difference method, specifically the forward time-centered space (FTCS) method. In the simulations, two scenarios are illustrated, namely, the instant removal mechanism scenarios and the delayed removal mechanism scenarios. In the instant removal mechanism scenarios, various average removal rates and basic water flow behaviors are simulated. In the delayed removal mechanism scenarios, realistic oil spill situations are considered. Therefore, the spillage rate and removal mechanism rate throughout the simulation period are analyzed. The simulation results show that the concentration of the late-coming removal mechanism leads to a poorer recovery outcome than the faster-coming removal mechanism in all scenarios. This aligns with the reality that when oil spill removal is effectively managed, the concentration of oil in the sea should decrease. The findings of this study demonstrate that, under all circumstances, delayed oil removal has more detrimental effects on seawater recovery than speedy removal. Therefore, removing oil spills quickly and effectively will significantly reduce the amount of oil in the water.

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