Journal of Science and Technology Mahasarakham University

Study of the optimal ratio of biodegradable plant Pot from cassava rhizome and bagasse

Kant Wirunphan — 2025-02-28

This research aimed to study the effect of different mixing ratios of cassava rhizome waste and bagasse on the mechanical and physical properties of plant pot products. The cassava rhizome waste and bagasse were crushed into powder and sieved using a No. 80 mesh sieve. The powders were then mixed at ratios of 90:10, 80:20, 70:30, 60:40, 50:50, 40:60, 30:70, 20:80, and 10:90 (cassava rhizome waste: bagasse) by weight, with wet starch used as the binder. The mixtures were processed under a compression force of 10 tons for 5 minutes. The resulting plant pots were dried at 60°C for 6 hours and then tested for mechanical and physical properties, including compressive stress, expansion rate when exposed to moisture, and deformation under actual usage conditions. The study found that a mixture of cassava rhizome waste and bagasse could effectively form plant pot products. The optimal mixing ratio for achieving the best mechanical and physical properties was 10:90 by weight. This ratio exhibited a compressive stress of 0.19 ± 2.27 N/mm², an expansion rate of 7.97% when exposed to moisture, and minimal deformation over a 14-day period of actual use. The presence of sticky fibers in bagasse contributed to better structural integrity and reduced deformation in the plant pots. These findings highlight the potential of using cassava rhizome waste and bagasse as sustainable materials for developing plant pot products with desirable mechanical and physical properties.

Selection of bacteria with probiotic properties from fish processing wastewater for use as ingredient in Oreochromis niloticus (nile tilapia) feed

Nisarat Tamsamak — 2025-02-28

The aim of this research was to select bacteria with probiotic properties from fish processing wastewater and to investigate the effects of using probiotics as an ingredient in feed for Nile tilapia. The isolation and morphological characterization of bacteria including FM7 FM11 and FM12 from fish processing wastewater, found that all isolates could produce protease and amylase enzymes without blood hydrolysis. In addition, all isolates also grow at the pH range of 2.0-10.0, NaCl concentration of 0-10% (w/v), and in fresh sterile bile of Oreochromis niloticus (Nile tilapia) in the concentration of 10, 30, and 50% (v/v). The pathogenic inhibition of Aeromonas hydrophila by antagonistic test revealed that only isolate FM11 showed the inhibition zone. The biochemical and molecular identification by VITEK^® 2 Compact and 16S rRNA gene sequencing found that isolate FM11 was closely related to Bacillus siamensis KCTC13613 with an identity of 99.77%. The growth measurement of Nile tilapia with 0.3 percent (v/W) probiotic bacteria mixed with feed found that the highest growth was found when culturing Nile tilapia with B. siamensis isolate FM11 with a weight of 2.15 ± 0.13 g/fish/day. The survival rate was 87.50 ± 5.89%. While culturing with feed only, the weight was 0.96 ± 0.11 g/fish/day with a survival rate of 79.17±29.46 with statistical significance (P<0.05). Therefore, Nile tilapia should be fed by the conventional feed together with the bacterial isolate FM11 for promotion of the efficiency of the tilapia feed to increase growth, weight gain, and survival rates, which can replace the sole conventional feed.

Land snail diversity and abundance in limestone hill areas at Pa Dong Sam Thong, Pa Dong Nong Phai, and Pa Dong Pha Sam Yod National Reserved Forest, Si Bun Rueang district, Nong Bua Lumphu province

Kitti Tanmaungpuk — 2025-02-28

This study aimed to investigate the diversity and abundance of land snails in limestone hill areas at Pa Dong Sam Thong, Pa Dong Nong Phai, and Pa Dong Pha Sam Yod National Reserved Forests, Si Bun Rueang District, Nong Bua Lamphu Province, during the late rainy season to early winter, from November 2021 to February 2022. Sampling was conducted using 20 x 20-meter plots across four stations (A, B, C, and D), with six plots per station, resulting in a total of 24 plots.The findings revealed a total of 8,069 land snail specimens, belonging to 22 genera and 26 species. Cryptozona siamensis exhibited the highest population, while Georissa sp. showed the highest relative abundance. The diversity index (H') was 1.891, the maximum diversity index (H_max) was 3.258, and the evenness index (J') was 0.580. The exponential of Shannon entropy (exp^H'), reflecting species adaptability to the environment, was 6.628. The dominance index (C) was 0.239, and the highest similarity coefficient (Ss) was observed between stations C and D, at 0.838. In this study, microsnails of the genera Dicharax sp. and Hypselostoma sp. were discovered in bamboo forest habitats. This is the first report of these genera in the northeastern region of Thailand.

Antioxidant and anti-inflammatory of Antigonon leptopus flower extract

Nadaphast Koomklang — 2025-02-28

The purpose of this study was to investigate the antioxidant and anti-inflammatory activities of Antigonon leptopus flower extract from Suphan Buri province. The extract was obtained through maceration in ethanol at 15°C for 3 days. The results demonstrated significant antioxidant activity, with IC50 values of 0.59 mg/ml for DPPH and 0.54 mg/ml for ABTS assays. Anti-inflammatory properties were evaluated through protein degradation inhibition and proteinase activity assays. The extract, at concentrations ranging from 2.0–10.0 mg/ml, showed protein degradation inhibition of 7.40–68.69% and proteinase activity inhibition of 16.97–87.87%, respectively. These findings suggest that, in addition to its traditional uses as an ornamental plant or food decoration, Antigonon leptopus flowers have the potential for development into dietary supplements and cosmetic products due to their antioxidant and anti-inflammatory properties.

Detection of lead ion in water by the reagent extracted from pomegranate peel

Wankassama Haron — 2025-02-28

This research investigated the potential of pomegranate peel extract as a reagent for detecting lead ions in water. Pomegranate peel was extracted using ethanol as a solvent, and the active compounds were analyzed using LC-MS and UV-Vis techniques. The results revealed the presence of phenolic compounds, specifically gallic acid, and catechins, which are expected to form complexes with lead ions. Lead detection was performed in synthetic wastewater at concentrations ranging from 0.001 to 0.1 M. The findings showed that the pomegranate peel reagent caused a color change from orange to yellow and produced suspended precipitates. The amount of sediment increased proportionally with the concentration of lead ions.

At lead concentrations between 0.01 and 0.1 mg/L, a noticeable color change occurred at the water surface. At 0.05 mg/L, the water surface turned yellow due to the formation of fine suspended sediments, which aligns with the surface water quality standard that limits lead concentration to 0.05 mg/L. These results suggest that pomegranate peel extract can be effectively used for qualitative lead detection in water. This method can serve as a simple tool for monitoring and controlling water pollution while also promoting the utilization of agricultural waste for environmental purposes. If a yellow discoloration is observed on the water surface, it can be inferred that the lead concentration exceeds acceptable levels, indicating the need for water treatment before discharge into community water sources.

A model for analyzing the severity level of adverse drug reactions using machine learning

Sasiwimon Kobua — 2025-02-28

Psychiatric drugs are a class of central nervous system stimulants with a high number of reported adverse drug reactions, ranking among the top five drug classes for adverse events. Pharmacovigilance (PV) is a crucial process for identifying, evaluating, and preventing potential adverse events. This process is complex, time-consuming, and dependent on the experience and analytical knowledge of medical personnel. Therefore, this study aims to develop a model for analyzing the severity of Adverse Drug Reactions (ADRs) using Machine Learning. The process includes model preprocessing, feature selection, and learning data sets. Five machine learning techniques were applied: K-Nearest Neighbors, Linear Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. The evaluation of model performance using various techniques showed that the Artificial Neural Network model performed best in classifying the severity of ADRs. The model's performance, evaluated using Stratified 10-Fold Cross Validation, yielded an accuracy of 80.60% and an overall efficiency of 77.85%. The model demonstrated a strong ability to classify cases with moderate to severe ADRs as well as non-ADR cases. The key features that contributed to the model's effectiveness in classifying severity include the relevance of PRN or LNC medication administration, receipt of high-alert drugs, history of allergies, ward type, ICD code F250 (main disease), diagnostician, season (winter), urgency, and patient condition upon arrival at the hospital.

Prediction of soil health for plants using deep neural networks

Thawatchai Promrat — 2025-02-28

The objective of this research is to create a soil health prediction model at present by simulating the environmental conditions for growing yellow beans in four different environments. These environments involve three pots each, representing the following conditions: environment 1: watering and chemical fertilizer application, environment 2: watering and organic fertilizer application, environment 3: watering only, and environment 4: no watering or fertilization. Additionally, an Internet of Things (IoT) system was developed to collect soil health data over 2 months, resulting in a total of 475 data points. The collected data was then grouped to assign the most suitable number of clusters using the K-means algorithm. This process yielded three clusters that best fit the soil health data, each corresponding to different levels of soil health: Suitable, Moderate, and Improvement. Labels were assigned to each data point within the clusters, indicating the corresponding soil health level. Subsequently, the data was utilized to create a soil health prediction model using deep-learning neural network algorithms. Through experimentation, the model achieved 98% accuracy in predicting soil health.

Design and development of automatic hatching machine for the bantam breed, Ban Wang Nak area in Nakhon Si Thammarat province, Southern Thailand

Rawit Khamhanpol — 2025-02-28

This research aimed to: 1) design and develop an automatic incubator for Ban Wang Nak bantam chickens in Karor, Nopphitam, Nakhon Si Thammarat, to control temperature and relative humidity with an automatic egg-turning mechanism, and 2) improve the hatch rate of bantam eggs within 21 days. The system processes data using an Arduino UNO microcontroller and employs fuzzy logic theory for decision-making. Nine control rules were established, and a Graphic User Interface (GUI) was developed using LabVIEW to display real-time temperature and humidity data for user monitoring. The incubator dimensions are 30x40x120 centimeters (width x length x height). Experimental results for temperature and relative humidity control showed averages of 36.86°C and 65.16%, respectively. The incubator was tested with bantam eggs to analyze the fertilization rate, hatch rate, and embryo mortality. The findings indicated that the fertilized egg rate at 7 days was 96.67%, while the unfertilized egg rate was 3.33%. Embryo mortality rates at 7 days and 18 days were 1.11% and 3.45%, respectively. Late embryo mortality was minimal, at 1.11%. The hatching rate for fertilized eggs was 95.44%, with an overall hatch rate of 92.22%. In conclusion, the developed automatic incubator effectively met the needs of the Ban Wang Nak community by enhancing the breeding efficiency of bantam chickens. It increased the survival rate of chicks to meet market demand while supporting more frequent incubation cycles.

Effect of tungsten equivalent on hardness of heat-treated semi-multi–alloyed white cast irons with 0% carbon balance

Wattanasak Samruay — 2025-02-28

This study investigates the effect of tungsten equivalent (W_eq) on the hardness of heat-treated multi-alloyed white cast iron with a 0% carbon balance. Specimens with W_eq values ranging from 6.0 to 8.0% were prepared. The specimens were annealed at 950°C and cooled in the furnace. The annealed specimens were then austenitized at 1050°C and 1100°C, followed by quenching with fan air. After hardening, the specimens were tempered at temperatures between 400°C and 600°C. The results showed that the microstructure consisted of austenite dendrites and eutectics (g+MC) and (g+M₂C), with the (g+M₂C) eutectics increasing as the W_eq values increased. Hardness in the as-hardened state decreased with increasing W_eq values. The tempered hardness curve demonstrated secondary hardening due to the precipitation of secondary carbides and the transformation of austenite to martensite. The degree of secondary hardening (DHs) increased with higher W_eq values and austenitizing temperatures. The maximum tempered hardness (H_Tmax) was achieved at a tempering temperature of 500°C in all specimens. The highest H_Tmax values, 879 HV30 and 848 HV0.1 were obtained in the 6.7% W_eqspecimen, hardened at 1100°C and tempered at 500°C.

Geospatial analysis to estimate the solar energy in household scale by geographic information systems

Jirawat Jantongpoon — 2025-02-28

Solar energy is a clean, unlimited energy source that can be utilized without causing pollution or environmental harm. This study aimed to: 1) apply imagery from Unmanned Aerial Vehicles (UAVs) to create a Digital Surface Model (DSM) for generating a map of solar radiation potential areas at the household scale, and to analyze solar radiation using Geographic Information Systems (GIS); and 2) analyze the suitability of solar cell installation. The solar radiation potential areas were categorized into four levels: high, medium, low, and very low, with the very low level deemed unsuitable for installation. The study found that high-potential areas for solar cell installation are those that receive sunlight throughout the day, with an average radiation intensity greater than 5.28 kWh/m²/day, including open spaces and roofs.