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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
;
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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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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THE EFFECTIVENESS OF COLD BEVERAGES VERSUS ICE-SLURRY DRINKS ON THE ATHLETIC PERFORMANCE OF THAI FUTSAL PLAYERS USING K-MEANS CLUSTERING
(2025-01-01)
Pariyavuth, Pariya
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Panurushthanon, Phichayavee
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Punthipayanon, Sirichet
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Klabchom, Kreethanat
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Thongtha, Kaboon
Cooling interventions during futsal halftime breaks show substantial individual variability in physiological responses, yet standardized protocols fail to account for athlete-specific thermal stress susceptibility. This study employed This study used K-means clustering to compare the effectiveness of cold beverages versus ice slurry and to identify distinct physiological response phenotypes for personalized cooling strategy optimization. Ten competitive male futsal players (22.4 ± 2.1 years; 68.5 ± 8.2 kg) completed a randomized crossover design. Following the Futsal Intermittent Shuttle-Run Protocol (FIRP), participants consumed either ice slurry (-1°C) or cold sports beverages (4°C) at 7.5 g/kg body mass during 10-minute recovery. Futsal-specific reactive agility tests (RAG-D, RAG-T), blood lactate, heart rate, urine specific gravity, and perceived exertion were measured. K-means clustering analysis with silhouette validation identified response patterns. Three distinct physiological phenotypes emerged (silhouette coefficient = 0.67). Cluster 1 (High-Response, n=4): elevated blood lactate (>8.0 mmol/L), highest cardiovascular stress, superior ice-slurry response. Cluster 2 (Moderate-Response, n=3): balanced responses to both modalities. Cluster 3 (Low-Response, n=3): conservative responses with maintained performance, preferential ice-slurry benefits. Strong correlations existed between body mass and response magnitude (r = 0.78, p < 0.01). Unsupervised machine learning effectively discerned unique cooling response phenotypes, facilitating evidence-based customization of cooling therapies. This signifies a substantial progression in the accuracy of sports performance enhancement.
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An k-Nearest Neighbors Machine Learning Algorithm for the PM2.5 Early Warning System in Bang Khun Tian, Bangkok, Thailand
(2024-12-02)
Thongtha, Kaboon
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Pochai, Nopparat
The problem of particulate matter with a diameter of less than 2.5-10 microns, such as PM2.5-PM10 in Bangkok, affects the health of people because there are small particles that can penetrate deep into the alveoli. If there is an early warning system to warn people about the harmful levels of PM2.5 in Bangkok, such as an early warning of 2-3 days, it can help the people have time to prevent themselves. In this research, an early warning system to warn people about the harmful levels of PM2.5 in Bangkok is proposed. The air quality data of the Bang Khun Tian station, Bangkok, for 2 months, from December 1, 2020, to January 31, 2021, were selected because the area is an air-quality-worrying area. A proposed early warning system for the harmful levels of PM2.5 around Bang Khun Tian, Bangkok, was developed using the k-nearest neighbors machine learning algorithm. As the results show, the proposed technique gives an agreeable prediction for the earliest warning by 4 days.
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A PM2.5 Forewarning Algorithm Using k-Nearest Neighbors Machine Learning at Changpuek, Chiang Mai, Thailand
(2023-08-29)
Pochai, Nopparat
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Thongtha, Kaboon
In Chiang Mai, Thailand, the air pollution issue caused by atmospheric particulate matter with a diameter of less than 2.5 μm, or PM2.5, has been identified as an ongoing crisis. PM2.5 not only has a direct impact on people's health and way of life, but it also has a negative impact on the national economy. Residents in such PM2.5-polluted locations are particularly susceptible to respiratory diseases, skin diseases, inflammatory eye diseases, and cardiovascular problems. As a result, this study is going to analyze PM2.5 data using the k-nearest neighbors machine learning algorithm as a guideline to warn people, particularly in Changpuek, Chiang Mai, Thailand, to handle the PM2.5 characterization problem.
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Numerical Simulations of Water Quality Measurement Model in an Opened-Closed Reservoir with Contaminant Removal Mechanism
(2018-01-01)
Thongtha, Kaboon
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Kasemsuwan, Jaipong
The mathematical simulation of water contaminant measurement is often used to assess the water quality. The monitoring point placement for water quality measurement in an opened-closed reservoir can give accurate or inaccurate assessment. In this research, the mathematical model of the approximated water quality in an opened-closed reservoir with removal mechanism system is proposed. The water quality model consists of the hydrodynamic model and the dispersion model. The hydrodynamic model is used to describe the water current in the opened-closed reservoir. The transient advection-diffusion equation with removal mechanism provides the water pollutant concentration. The water velocity from the hydrodynamic model is plugged into the dispersion model. The finite difference techniques are used to approximate the solution of the water quality model. The proposed numerical simulations give a suitable area of zonal removal mechanism placement. The proposed simulations also give the overall and specified approximated water quality for each point and time when the exit gate is opened on the different periods of time. In addition, the proposed techniques can give a suitable period of time to open the exit gate to achieve a good agreement water quality by using contaminant removal mechanism.
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Analytical solution to a hydrodynamic model in an open uniform reservoir
(2017-12-01)
Thongtha, Kaboon
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Kasemsuwan, Jaipong
The hydrodynamic model is used to determine the water wave flow. In this research, a nondimensional form of a two-dimensional hydrodynamic model with generalized boundary condition g(x, t) and initial conditions for describing the elevation of water wave in an open uniform reservoir is proposed. The separation of variables method with mathematical induction is employed to find an analytical solution to the model. An example of flow calculations in an open uniform reservoir is also demonstrated.

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