Journal of Applied Research on Science and Technology (JARST)

Impact of abiotic stress and micronutrient supplementation on the 2-acetyl-1-pyrroline content in KDML105 rice (Oryza sativa L.)

Thu, 12 Feb 2026 00:00:00 +0700

This study investigated the effects of abiotic stress and mineral supplementation on 2-acetyl-1-pyrroline (2-AP) content in Thai jasmine rice (Oryza sativa L. cv. 'Khao Dawk Mali 105'; KDML105). Rice aroma quality fundamentally influences the market value of aromatic rice. Therefore, enhancing the aromatic compound, 2-acetyl-1-pyrroline (2-AP) could improve rice aroma quality. In this study, KDML105 rice plants at the reproductive stage were used to investigate the effects of abiotic stress (drought, salinity and combined salt-drought stress) and mineral supplementation (Ca, Cu, Mg, Fe, and Zn) on 2-AP content compared to the control plants which were not treated with abiotic stress and exogenous minerals supplementation. The results found that proline levels in plants which were treated with salt stress or combined salt-drought stress accumulated higher leaf proline content (7.7-fold and 9.7-fold increases) compared to those of control plants (30.15 µg/g FW). Although the proline content increased, there were no significant differences in 2-AP levels of the plants. In contrast, the proline content of plants subjected to minerals did not increase while the 2-AP content increased compared to the control plants (3.37 ppm), particularly in plants supplemented with Ca and Cu (6.10 and 5.26 ppm, respectively). These findings indicated that mineral supplements did not stimulate proline production. However, it could induce or suppress other compounds in 2-AP synthesis pathway, resulting in high 2-AP accumulation. This was probably due to the downregulation of the betaine aldehyde dehydrogenase 2 gene (BADH2). Not only gene regulations in the pathway, but also external factors, such as the drying process after harvesting, affect 2-AP content. The results revealed that the solar-dried grain method caused higher 2-AP content compared to hot-air-dried and undried grains. Thus, targeted mineral supplementation and appropriate post-harvest processes are involved in enhancing aromatic quality in jasmine rice. In addition, supplemented plants with Ca significantly enhanced FRAP activity accompanied by remarkable increases in both total phenolic and total flavonoid contents.

Fake news detection through multi-level contextual and sequential modeling

Pimpa Cheewaprakobkit, Rindra Razafinjatovo — Fri, 15 May 2026 00:00:00 +0700

The spread of false information on digital platforms destabilizes democratic societies and undermines public trust. Conventional machine-learning models often fail to capture the subtle contextual cues and long‑range linguistic dependencies required for accurate verification. To address these limitations, we propose a hybrid deep‑learning architecture that integrates three modules: A Contextual Encoder Module (CEM) using RoBERTa to generate relational embeddings for deep semantic extraction; a Bidirectional Sequence Learner (BSL) to model long‑range temporal dependencies; and a Context Refinement Layer (CRL) that employs attention mechanisms to highlight the most salient deceptive markers. We evaluated the model on the WELFake dataset, which contains 72,134 news articles. Our proposed method achieved 99% accuracy, precision, recall, and F1‑score, significantly outperforming state‑of‑the‑art baselines, including BERT (95%) and BiLSTM (97%). The CRL's attention outputs provide transparency into the model's decision‑making process, an essential feature for applications in misinformation detection and automated content moderation. Beyond its technical contributions, this research supports society by enabling early and reliable detection of false information, reducing harm from misinformation, and promoting a safer, more trustworthy digital environment.

Gamma shielding efficiency of sedimentary rock-based concrete blocks under Co-60 irradiation

Pitchpilai Khoonphunnarai, Sutthisa Konruang, Phayao Yongsiriwit — Thu, 04 Jun 2026 00:00:00 +0700

Ensuring human safety requires understanding the radiation shielding capabilities of building materials alongside their strength and durability. Common conventional radiation shielding materials include lead and concrete. However, lead is costly, toxic, and raises environmental concerns, prompting increased interest in alternative shielding materials. Therefore, this research investigates the potential of concrete made from two types of sedimentary rocks, shale and calcareous, for shielding against Co-60 irradiation. The sedimentary rocks were sourced from the Global Geopark region in Satun Province, Thailand. The shielding performance of sedimentary rock-based concrete is compared with standard construction concrete. All concrete blocks were 15 cm × 15 cm × 15 cm and were prepared with a cement:sand:stone ratio of 1:2:4. Concrete cube specimens were prepared to evaluate radiation attenuation properties. The linear attenuation coefficient () was calculated from the reduction in count rate after transmission through the concrete block. Engineering properties, including density and compressive strength, were also analyzed. Radiation shielding efficiency is assessed using the linear attenuation coefficient (), mass attenuation coefficient (), mean free path (MFP), half-value layer (HVL), and tenth-value layer (TVL), All of these parameters were calculated from the measured count rates. The study found that standard concrete had the highest compressive strength at 25.45 MPa, followed by shale concrete at 13.59 MPa, and calcareous concrete at 5.21 MPa. However, shale concrete performed best in radiation shielding, showing a linear attenuation coefficient () of 0.18 cm⁻¹, a mass attenuation coefficient () of 0.09 cm²/g, a mean free path of 5.59 cm, a half-value layer of 3.87 cm, and a tenth-value layer of 12.86 cm. The improved shielding performance of shale concrete may be related to its mineral composition and internal structure. Analysis by XRD and XRF indicated that the main heavy metal compounds present were Al₂O₃ and Fe₂O₃.

A reinforcement learning model for route allocation optimization: A case study of C-130H transport aircraft in the Royal Thai Air Force

Wed, 24 Jun 2026 00:00:00 +0700

This study aims to develop a route-allocation model for transport aircraft in the Royal Thai Air Force's (RTAF) air logistics operations. A Reinforcement Learning (RL) approach was applied to optimize resource allocation by determining the most suitable routes based on cargo capacity distribution, thereby reducing operational flight distances and, consequently, the frequency of aircraft maintenance. This research was conducted in a simulated environment using domestic air transport data as a reference for C-130H transport aircraft of Squadron 601, Wing 6, RTAF. Experimental results show that the developed model significantly improves air transport operational efficiency in support and service provision, facilitating network operations by reducing flight distances and increasing process continuity under varying cargo capacity conditions compared to current practices. Ultimately, this contributes to the improvement and sustainability of defense operations. The proposed work schedule also demonstrates adaptability to dynamic operational constraints and changing demand.

Product development of mangosteen (Garcinia mangostana L) with karonda (Carissa carandas L.) and rice protein sorbet

Mon, 29 Jun 2026 00:00:00 +0700

This study aimed to develop a functional sorbet formulation by combining mangosteen (G. mangostana) and karonda (C. carandas) juices, enriched with rice protein. Mixed fruits juice was prepared by substituting mangosteen juice with karonda juice at concentration of 0, 5, 10, 15 and 20% w/w. The physicochemical properties, including color, pH, total soluble solids (TSS), vitamin C content, and antioxidant activity, were evaluated. Results, indicated that increasing karonda juice concentration enhanced red coloration, DPPH radical scavenging activity, and vitamin C enrichment, the formulation containing 15% w/w karonda juice was selected for further development. Subsequently, sorbets enriched with rice protein at levels of 0, 5, and 10% w/w were prepared and characterized. Rice protein enrichment reduced the melting rate, demonstrating its role as natural stabilizing agent in sorbet formulations. Additionally, rice protein contributed functional and nutritional benefits, including increased energy and protein content, while improving textural properties. Despite these advantages, sensory attributes such as flavor and taste remained critical determinants of consumer acceptance and market success. Overall, the incorporation of karonda juice and rice protein into mangosteen sorbet enhances its nutritional profile and functional properties, highlighting the potential of plant-based proteins and fruit blends in the development of innovative frozen desserts.

Effective detection of Thai fake news using machine learning method

Nopphadol Sumleeroung, Komsan Kanjanasit, Nithizethe Mhuadthongon — Mon, 20 Jul 2026 00:00:00 +0700

The rapid spread of misinformation on digital platforms has become a critical challenge affecting public trust, social stability, and policy communication in Thailand. The aim of this study is to develop and evaluate a reproducible machine learning framework for Thai fake news detection from real world verified data from the Anti-Fake News Center Thailand during 2019 - 2024. The data set comprises four key data groups including governmental policies, health products, financial and stocks as well as disaster news. The suggested research methodology entails thorough data cleaning and text normalization, sentiment and emotions analysis through the usage of AI for Thai, dimension reduction by selecting relevant features and dealing with unbalanced classes through Synthetic Minority Oversampling Technique (SMOTE). For evaluation, three supervised learning algorithms (Naïve Bayes, Support Vector Machine [SVM] and neural networks) were tested using 80:20 and 90:10 train-test split ratios, K-Fold Cross-Validation and hyperparameter optimization. Based on the experimental findings, the neural network classifier achieved better accuracy rates, that is, 92.39%, with other performance metrics including precision, recall, F1-score, ROC-AUC and AUC-PR at 92.64%, 92.39%, 92.38%, 96.79%, 96.07% respectively. Therefore, the methodological framework was effective in identifying false news stories in the Thai language due to the use of linguistic features and oversampling strategies. The main contribution of this study is an interpretable baseline framework that integrates linguistic, affective, and metadata features for Thai fake news detection in a low-resource language setting.

Physical property of 925 sterling silver metal injection molding and morphology of brown parts under varying solvent-based debinding conditions

Montri Kawsuk, Witthaya Daodon, Surat Wannasri — Fri, 07 Aug 2026 00:00:00 +0700

This research aims to study the physical properties of raw parts produced using Metal Injection Molding (MIM), an advanced manufacturing process suitable for the industrial-scale production of highly complex, small, and high-precision three-dimensional metal parts. However, applying this technique to precious metals, especially 925 sterling silver, still has significant limitations and research gaps, particularly regarding the use of atomized metal powders, which are a byproduct and have lower costs than general-purpose specialized metal powders. 925 silver feedstock used to produce the MIM samples consisted of 90% 925 silver powder and 10% binder. Solvent-based binder removal was performed in water for 5 hours at three temperatures: 30°C, 40°C, and 50°C, respectively. Microstructure and particle morphology were examined using a scanning electron microscope (SEM). The goal was to identify the optimal conditions for forming an open porous structure conducive to sintering and shape retention in subsequent industrial processes. The experimental results showed that the optimal material ratio consisted of 90% silver powder and 10% binder by weight, resulting in an average material density of 4.507 g/cm³ and an average material tensile strength of 17.70 MPa. The results indicated that the binder removal process using distilled water at 50°C for 5 hours was the most efficient method.

Isolation and selection of Lactiplantibacillus plantarum CLT-L01 (TISTR P088) from traditional fermented vegetables for solid-state fermentation of soybean meal (SBM)

Phongsupha Chanthachaiyaphum, Yue Wang, Wenbo Wang — Mon, 10 Aug 2026 00:00:00 +0700

Lactic acid bacteria are widely used as beneficial microorganisms in fermentation processes for animal feed production. In soybean meal, which is an important protein source in animal feed, lactic acid bacteria fermentation can improve nutritional quality by promoting acidification and reducing antinutritional factors. This study aimed to isolate lactic acid bacteria from traditional fermented vegetables and evaluate their potential as starter cultures for the solid-state fermentation of soybean meal. Traditional fermented vegetables were selected as the isolation source because they are locally available, inexpensive, and rich in naturally occurring lactic acid bacteria. Nine acid-producing bacterial isolates were obtained from traditional fermented vegetable samples using MRS agar supplemented with calcium carbonate. The isolates were preliminarily characterized by Gram staining and catalase testing. Six isolates, namely CLT-35, CLT-51, CLT-33, CLT-52, CLT-53, and CLT-L01, were selected for the evaluation of acid production efficiency in MRS broth. Among them, CLT-L01 showed the fastest acid production, with the pH decreasing to 3.21 ± 0.02 after 24 h of incubation. Therefore, CLT-L01 was selected for further evaluation in solid-state soybean meal fermentation. The results showed that soybean meal inoculated with CLT-L01 exhibited a rapid increase in lactic acid content, exceeding 10% after 96 h of fermentation. The highest lactic acid content was 11.13 ± 0.06% at 127 h, whereas the uninoculated control reached only 2.43 ± 0.12% at 151 h. These findings indicate that CLT-L01 enhanced acid production and improved fermentation efficiency compared with natural fermentation. Molecular identification based on 16S rDNA sequencing revealed that CLT-L01 showed more than 99% sequence similarity to the reference strain Lactiplantibacillus plantarum. Overall, CLT-L01 has potential as a starter culture for promoting lactic acid production and acidification during soybean meal fermentation.

Enhancing lung cancer screening accuracy through a dual-pipeline deep learning and machine learning framework for integrated risk assessment

Patthanan Uthaititpitak — Mon, 17 Aug 2026 00:00:00 +0700

Lung cancer is a leading cause of death worldwide, with delayed or misdiagnosis further hindering professionals' ability to lower lung cancer-related mortality rates. Misdiagnosis could look like false positive results, where benign lung diseases are mistaken for malignant lung carcinomas, or more life-threatening false negative results, where harmful eosinophilic pneumonia is misinterpreted as a benign lung hamartoma. The objective of this study consists of the following goals: (1) to develop CNN-based classification models for lung nodule analysis, (2) to compare the performance of multiple deep learning models under consistent controlled environments, (3) to evaluate and present the interpretability of the models through a clinically grounded probability equation, and (4) to propose an integrated risk-scoring framework that combines the imaging and clinical pipelines into a single interpretable measure of lung cancer risk. However this framework is only theoretical as imaging and clinical models were developed using separate datasets; future studies should note that a practical development will require validation on a unified multimodel patient cohort. In regard to the creation of the models, the deep learning convolutional neural network, which utilized a YOLOv8n backbone trained on the IQ-OTH/NCCD dataset of 649 annotated CT images, achieved a mean average precision (mAP50) of 0.687 when evaluated against expert-verified annotations. The machine learning branch utilizes a structured clinical dataset of 309 patient records with symptom- and risk-based features, including age, smoking status, chronic disease, fatigue, wheezing, and shortness of breath, all evaluated under standardized conditions. These machine learning models, showed similarly promising results, with the Logistic Regression (Log) achieving the highest mean accuracy of 0.929, while CatBoost (CB) demonstrated the strongest overall balance with a Mean F1 score of 0.953 at optimal hyperparameter settings of 1,000 iterations and a tree depth of 8. Interpretability is further enhanced through a clinically grounded cancer probability equation. This framework that showcases both deep and machine learning models will offer insight into the effectiveness of incorporating artificial intelligence in the screen accuracy of lung cancer classification.