Journal of Science Ladkrabang

Health Promotion and Disease Control in Aquaculture through Alternative Therapeutic Approaches

2025-06-10T14:35:34+07:00

Aquaculture is facing various threats from climate change, ecosystem deterioration, and disease outbreaks leading to decreased yield, food security and sustainability. Throughout the years, disease management in aquaculture has largely relied on the use of antibiotics and chemical treatments. However, the indiscriminate and excessive application of these chemicals has resulted in severe consequences, e.g. the spread of antimicrobial-resistant bacteria, disruption of metabolic processes in aquatic species, environmental degradation, accumulation of hazardous chemical residues in aquaculture products, and potential threat to human health. To ensure the sustainability of aquaculture, it is imperative to establish good aquaculture management and implement effective biosecurity measures. Moreover, effective alternative approaches should be simultaneously considered to enhance growth performance and control disease outbreaks in farmed aquatic species, which will lead to sustainable farming system management. This article reviews literature discussing viable alternative approaches applied in aquaculture, including phytotherapeutics, probiotics, bacteriophage therapy, and nanotherapeutics. These innovative approaches offer promising benefits in enhancing growth performance and disease control, while simultaneously promoting the sustainability of aquaculture industry. However, the practical application of these findings at farm level in aquaculture systems still requires further investigation, including legal and food safety regulations, cost-effectiveness, efficacy, potential ecological risks and harms, as well as user understanding and acceptance to ensure the safety, economic viability, and sustainability of aquaculture in Thailand.

Image Classification of Transfer Slips and Optical Character Recognition

2025-03-14T12:13:28+07:00

This article presents the classification of transfer slip images and Optical Character Recognition (OCR) use of automatic learning techniques that mimic the human neural network. The approach utilizes a Convolutional Neural Network (CNN) algorithm specifically designed for classifying transfer slip images, comprising three 2D convolutional layers combined with Max Pooling to prevent overfitting during the classification process. Subsequently, image segmentation is performed using YOLOv5 to identify the bank names, followed by OCR using Tesseract OCR technology to read the information on the transfer slips. The dataset was randomly split into a training set (80%) and test set (20%). Within the training set, 20% of the data was used as a validation set. The model was trained using a batch size of 32 and 50 epochs, achieving a classification accuracy of 99%. After classification, the segmented images were used to identify the bank names, and once identified, the images were subjected to OCR to extract the text. The results were displayed in a human-readable format and exported in JSON format.

Phytochemicals, Antioxidant Activity, Anti-tyrosinase Activity of Herbal Extracts, and the Development of Skin Care Lotion from Gooseberry (Phyllanthus emblica L.) Extract

2025-02-17T12:07:13+07:00

This research aimed to develop a skin care lotion product from Thai herbal extracts with tyrosinase‑inhibiting and antioxidant properties. Ten herbs, Moringa oleifera, Clitoria ternatea, Nelumbo nucifera, Morus alba, Daucus carota, Cucurbita moschata, Terminalia chebula, Centella asiatica, Raphanus sativus and Phyllanthus emblica, were macerated in 95 % ethanol. All extracts were analyzed for phytochemical contents, antioxidant capacity, and tyrosinase inhibition. The result showed that total phenolic content was highest in Terminalia chebula extract reaching 12,083.08 ± 232.18 mg gallic acid equivalents per 100 mg extract. Clitoria ternatea extract showed the greatest total flavonoid content at 3,397.07 ± 41.01 mg quercetin equivalents per 100 mg extract. Terminalia chebula extract exhibited the strongest DPPH radical‑scavenging activity with an IC₅₀ of 10.23 ± 0.63 µg/ml. However, Phyllanthus emblica extract provided the highest antioxidant activity in the ABTS and FRAP assays, with IC₅₀ values of 41.426 ± 13.485 mM/mg and 27.44 ± 2.05 mM Fe²⁺/mg, respectively. In the Dopachrome tyrosinase‑inhibition assay, Morus alba extract was the most potent inhibitor with an IC₅₀ of 123.82 ± 4.67 µg/ml, followed by Phyllanthus emblica extract with an IC₅₀ of 226.63 ± 13.74 µg/ml. Therefore, Phyllanthus emblica was chosen to develop as a skin care lotion product. A lotion formulated with Phyllanthus emblica extract (MKP) was compared with a control lotion (C) and a kojic‑acid lotion (KJ) through seven heating–cooling cycles to evaluate physical and chemical stability. It was found that the MKP and KJ lotion formulations exhibited higher antioxidant activity and tyrosinase inhibition than the control formulation (C). The lotion containing Phyllanthus emblica extract showed the highest antioxidant activity, whereas the lotion containing kojic acid demonstrated greater tyrosinase inhibitory activity than the formulation with Phyllanthus emblica extract.

Assessing the Performance of Data Mining Techniques for Breast Cancer Patient Screening

2025-04-28T15:18:53+07:00

This research aimed to evaluate the performance of various data mining techniques in constructing predictive models for breast cancer screening. Seven classification methods were compared, namely Neural networks, Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbors (k-NN), Decision tree, Deep learning, and Ensemble vote. The dataset used in this study comprised 569 patient records obtained from the University of Wisconsin and made publicly available on www.kaggle.com. The analysis was conducted following the CRISP-DM process, which included variable selection, handling of missing data, and defining the roles of each attribute. The results revealed that the Neural Network technique yielded the best performance, achieving an accuracy of 98.07%, sensitivity of 99.15%, specificity of 96.21%, and an overall efficiency of 98.47%. These findings demonstrate the potential of this technique to significantly support the early detection and diagnosis of breast cancer.

Comparing the Forecasting Abilities of Stock Returns of Electricity Sector Stocks in the Stock Exchange of Thailand Using ARIMA and ARIMAX Models

2025-04-02T10:45:08+07:00

This research aims to study and compare the forecasting ability of stock returns for the power plant energy sector listed on the Stock Exchange of Thailand (SET) over a period from January 2018 to December 2023, a total of 72 months. This study employed the ARIMA and ARIMAX models to forecast the stock prices of 10 companies in the power plant energy sector: BGRIM, GPSC, GULF, ACC, CKP, EA, GUNKUL, NOVA, SOLAR, and SSP. The forecast accuracy was evaluated using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) as criteria to identify the model with the least error. The results demonstrate that the ARIMAX model, which incorporates independent variables such as global crude oil prices, the Thai baht exchange rate, and the stock market index, significantly enhances forecasting accuracy. In most cases, ARIMAX outperformed the ARIMA model, yielding lower MAD and MAPE values. For example, for SOLAR, ARIMA produced a MAPE of 12.365%, while ARIMAX achieved a lower MAPE of 9.749%. Similarly, for GPSC, the MAD decreased from 4.596 (ARIMA) to 3.420 (ARIMAX). The results indicate that incorporating relevant independent variables can significantly enhance the accuracy and efficiency of the forecasting model.

The Application of Data Mining Techniques for Analyzing Electricity Usage Behavior of Consumers in Thailand

2025-03-04T09:37:13+07:00

This study aims to investigate the relationship between electricity user types and their rank of monthly electricity consumption, analyze the electricity usage patterns using the Apriori algorithm, and propose energy policies based on the analysis results. The sample data comprises 3,562 records of electricity consumption from a government database, covering residential, general business, large-scale industry, backup electricity users, and specialized business types. This approach differs from other research which often focuses on smart meter or prepaid bill data. The research technique employed is data mining using the Apriori algorithm, which has an advantage over Clustering or Prediction methods as it can discover association rules without requiring a pre-defined number of groups. The statistical measures used are confidence and association (lift). The findings show that large-scale industries and businesses have the highest electricity consumption volume every month, while backup electricity users have the lowest usage. Residential users consuming less than 150 units and general businesses fall into the middle rank. Furthermore, the Apriori algorithm demonstrated high accuracy in discovering significant relationships, which can be used to develop policies, such as optimizing energy resource allocation for industries, restructuring backup electricity tariffs, and promoting efficient electricity consumption in residential and general business sectors to create a sustainable energy system, requiring significant results such as the relationship between industry and high electricity usage, can be practically applied in planning to increase production capacity during high-demand periods.

Game of Moving (k,m)-Pieces where (k,m)∈{(1,2n+1),(2,2n+8),(3,4n+5),(3,4n+7)}

2025-02-06T17:49:35+07:00

Let m and k be positive integers such that m≥k. The game of moving (k,m)-pieces is a one-player game on a 1×m board involving two colors of pieces, with a total of m-k pieces. Each color has an equal number of pieces. At the beginning, we place all first color pieces from the rightmost to the leftmost followed by all second color pieces. The goal of this game is to move k consecutive pieces to the empty spaces on the board until the pieces are arranged in alternative colors placed from the leftmost position onward. In this study, for any positive integer n≥1, we obtain 1) when (k,m)=(1,2n+1), the minimum number of moving is 2n-2[n/2] 2) when (k,m)=(2,2n+8), the minimum number of moving is 2n+3 3) when (k,m)=(3,4n+5), the minimum number of moving is 4n-1 and 4) when (k,m)=(3,4n+7), the minimum number of moving is 4n+2.

Building a Model and Evaluating the Performance for Colon Cancer Screening Using Histopathological Images

2025-02-27T09:49:33+07:00

Colon cancer remains a major global health concern. Early detection is critical for improving treatment outcomes. Although conventional diagnostic methods are generally reliable, they can be time-consuming and heavily dependent on the expertise of medical professionals. This study aims to evaluate the effectiveness of machine learning models in classifying colon cancer using 10,000 histopathological images obtained from www.kaggle.com. The data were analyzed following standard data mining procedures using four image classification techniques: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Neural Network (NN), and Decision Tree (DT). The results showed that the k-NN technique achieved the highest accuracy at 91.86%, along with the highest sensitivity and specificity values at 92.24% and 91.48%, respectively. These findings indicate that the k-NN technique is highly suitable for developing classification models for colon cancer, contributing to the creation of essential tools for early detection and supporting more effective treatment planning.

The Solutions of the Benney-Luke Equation and the Modified Equal-Width Using the Riccati-Bernoulli sub-ODE Method

2025-03-04T10:19:08+07:00

This research article aims to find solutions to the Benney-Luke equation and the modified equal-width equation using the Riccati-Bernoulli sub-ODE method. This method is frequently employed for solving nonlinear partial differential equations. The solutions obtained for both the Benney-Luke and the modified equal-width equations are expressed in terms of hyperbolic functions and trigonometric functions. Furthermore, some solutions are generated in the form of kink waves and periodic waves, and are represented by two-dimensional and three-dimensional graphs.

Mathematical Modeling of the Dynamics of Lymphatic Filariasis in Phuket

2025-04-04T09:43:13+07:00

For this study, the scientific research studied and develop the mathematical model for the impact of the epidemic of elephantiasis (Elephantiasis), a contagious disease caused by roundworms in the genus Filariodidae, and the consider getting of influenza vaccination. Finding the equilibrium point with both disease-free point and endemic equilibrium points with epidemic. In addition, this research article found the basic reproductive number (R0) by analyzing the stability equilibrium according to Routh-Hurwitz condition. When R0<1, the equilibrium point with no epidemic has a local stability value and when R0>1, the equilibrium point with epidemic has a local stability value. Then, the numerical solutions were derived by selecting parameters based on previous research on Elephantiasis outbreaks, with simulations of varying parameters reflecting different outbreak scenarios. The analysis revealed that at the disease-free equilibrium point, the basic reproductive number was R0= 0.65738, while at the endemic equilibrium point, R0= 287.355. The findings suggested that vaccination efforts significantly impact the spread of the disease. Thus, it is recommended to increase vaccination rates to control and prevent new infections, potentially reducing the number of cases or eliminating them in the future.