Journal of Current Science and Technology

Renal Toxicity in Snakebite Envenomation: Insights into Pathophysiology, Risk Factors, and Management Strategies

Sat, 20 Sep 2025 00:00:00 +0700

Renal toxicity is one of the most severe complications associated with snakebite envenomation, contributing significantly to morbidity and mortality among affected individuals. This review provides a comprehensive analysis of renal toxicity in snakebite victims, focusing on the underlying pathophysiological mechanisms, risk factors, and current management strategies. Snake venom-induced renal damage may occur through various mechanisms, including direct nephrotoxicity, rhabdomyolysis, and coagulopathy. The extent of renal injury is influenced by factors such as venom composition, dosage, route of entry, and the victim’s pre-existing health conditions. We also conducted a bibliometric analysis of research trends in this field, highlighting a growing body of literature that reflects increased awareness of snakebite-associated renal complications and advancements in research methodologies. This review synthesizes current knowledge on the prevention and treatment of venom-induced renal toxicity, emphasizing the importance of early intervention, supportive care, and appropriate antivenom therapy. Furthermore, it identifies gaps in existing research and proposes future directions to enhance the understanding and management of renal complications caused by snake envenomation. These insights aim to improve patient outcomes and inform clinical practices in regions with a high prevalence of snakebite envenomation.

Biological Potentials of Etlingera elatior Inflorescences Extracted via Microwave-Assisted Extraction for Cosmetic Applications

Watchara Chongsa, Tun Chusut, Teeratad Sudsai — Sat, 20 Sep 2025 00:00:00 +0700

Etlingera elatior, widely known as Torch ginger, is a member of the Zingiberaceae family. It is recognized for its medicinal benefits, such as aiding the healing of skin conditions, alleviating flatulence, and enhancing blood circulation. Therefore, this research aimed to investigate the antioxidant, tyrosinase inhibitory, and anti-inflammatory activities of the extracts, along with the total phenolic and flavonoid contents, from three varieties of Torch ginger inflorescences: red, pink, and white. Each type of inflorescence was extracted using microwave-assisted extraction (MAE) with different ethanol concentrations (100%, 80%, and 50% v/v). The results showed that the 50% ethanolic extract of the red inflorescence exhibited strong antioxidant activity, with the highest DPPH and ABTS radical scavenging effects. Moreover, this extract contained the highest concentrations of total phenolics and flavonoids, measuring 102.7 mg gallic acid equivalents (GAE)/g of extract and 56.7 mg catechin/g of extract, respectively. Regarding melanin production, the 100% ethanolic extract of the red inflorescence showed the greatest reduction in melanin production in B16F10 melanoma cells and the highest tyrosinase inhibitory activity. Furthermore, when anti-inflammatory activity was assessed based on nitric oxide inhibition in RAW264.7 macrophage cells, the 100% ethanolic extract of the red inflorescence demonstrated significant effectiveness without any signs of cytotoxicity, with an IC₅₀ value of 40.4 µg/mL. Overall, MAE of Torch ginger inflorescences using different ethanol concentrations yielded a phenolic and flavonoid rich extract with potent antioxidant, anti‐tyrosinase, and anti‐inflammatory activities, positioning it as a promising candidate for next‐generation skin‐brightening and anti‐inflammatory cosmeceutical formulations.

The Effect of the Resilience Quotient Program with Social Support among the Elderly with Chronic Non-Communicable Diseases

Thitchaya Piyaphattanuschai, Sunisa Chuaktong — Sat, 20 Sep 2025 00:00:00 +0700

Elderly people naturally experience physiological changes, leading to an increased susceptibility to chronic health conditions. Providing social support and enhancing the resilience quotient level can facilitate adaptation and enable them to maintain a fulfilling lifestyle even in challenging circumstances. This study aimed to investigate the effect of the Resilience Quotient (RQ) program with social support on the RQ level among the elderly with chronic non-communicable diseases.
A quasi-experimental research design with a pretest and post-test approach was employed in this study. The sample consisted of 60 elderly people who resided in Pathum Thani Province, Thailand, randomly divided into 30 each in the experimental and control groups. The experimental group underwent the Resilience Quotient program with social support, which was developed by the researchers, while the control group received standard care. The 30-question resilience variable questionnaire was used to measure the Resilience Quotient level. The data were analyzed using descriptive statistics and t-test statistics. The findings demonstrated a statistically significant increase in the average resilience quotient score of the elderly in the experimental group after participating in the program. This improvement was observed compared to their scores before the program (t = 16.73,
p < .001). Furthermore, the average resilience quotient score of the experimental group was statistically superior to that of the control group, which received standard care (t = 16.21, p < .001). The program facilitates a significantly higher resilience quotient level in the experimental group. The results were limited in generalizability to describe the elderly residing in urban areas due to family structure and social service diversity. To comply with Thailand’s Elderly Care Strategic Plan, community health nurses should consider arranging activities that enhance RQ levels into routine primary care for the elderly with NCDs in the community to promote their health, prevent disease, and manage chronic conditions.

Production and Characterization of Edible Beads Using Natural Calcium Extracted from Catfish Bone Powder through Direct and Reverse Gelation Techniques

Worawut Thammawong, Bung-Orn Hemung, Nachayut Chanshotikul — Sat, 20 Sep 2025 00:00:00 +0700

The significant number of catfish heads generated during processing represents an underutilized resource rich in calcium and proteins. This study aimed to valorize this waste by extracting natural calcium from catfish bone powder and using it to produce edible alginate beads through direct and reverse gelation techniques. Natural calcium was extracted from 0.4% catfish bone powder using microwave digestion at 300 W for 60 seconds with 0.1 M citric acid. In the direct gelation process, alginate solutions at concentrations of 0.4, 0.5, 0.6, 0.8, and 1.0% were dropped into the extracted calcium solution. The lowest concentration to form a gel was 0.5%, but 1.0% was selected for firmer gel formation. Reverse gelation involved dropping calcium solutions with 0, 10, 20, 30, and 40% gelatin into a 0.5% alginate solution, with the 20% gelatin formulation exhibiting the highest hardness and thus was chosen. The characteristics and stability of edible beads produced by both gelation methods were comparatively evaluated. Chemical composition analysis showed higher protein and calcium levels in reverse gelation beads than in direct gelation beads. Additionally, beads from reverse gelation demonstrated superior textural properties and greater acceptability compared to direct gelation beads. Both bead types showed limited storage stability when kept in water, as measured by changes in diameter. However, storage in 0.1 M citric acid solution significantly improved their stability.

In conclusion, natural fish bone calcium effectively induces alginate gelation by both direct and reverse methods. Reverse gelation produced nutritionally enhanced, acid-stable edible beads, presenting a promising approach for fish waste valorization with potential applications in acidic beverages. This method offers both economic and environmental benefits by transforming fish processing by-products into functional ingredients.

The Activities of 1'-acetoxychavicol Acetate on SW620 Colorectal Cancer Cells Line

Sat, 20 Sep 2025 00:00:00 +0700

Cancer is a significant cause of mortality worldwide, including in Thailand. However, chemotherapy has serious side effects. Ongoing research on the compound 1'-acetoxychavicol acetate (ACA) has revealed various medicinal properties, including anticancer and anti-inflammatory activities. Although ACA has been found to affect cancer cell lines through different mechanisms, few reports have focused on its effectiveness against colorectal cancer cell lines. This research aims to determine the anticancer activities of ACA on the SW620 cell line. Anticancer activities, including anti-proliferation, anti-migration, and anti-invasion, were evaluated using methylthiazolyldiphenyl-tetrazolium bromide (MTT), colony formation, scratch assays, invasion assays, and qRT-PCR. The results showed that ACA exhibited cytotoxic effects (IC₅₀ values) and anti-proliferative activity in a dose-dependent manner. ACA also demonstrated anti-migration and anti-invasion activities in a dose-dependent manner. Additionally, the qRT-PCR results showed that ACA significantly decreased Kirsten rat sarcoma viral oncogene homolog (KRAS) gene expressions compared to the control group. ACA exhibits anti-proliferative, anti-migratory, and anti-invasive activities in SW620 cells. These findings suggest the potential of ACA as a therapeutic agent and may provide insights that significantly advance our understanding of cancer biology and treatment.

Postharvest Quality Preservation of ‘Thongprasert’ Jackfruit Through Combination of H₂O₂ and 1-Methylcyclopropene

Tanatya Kenkhunthot, Kasideth Onsri, Bunnavit Nathaweesap — Sat, 20 Sep 2025 00:00:00 +0700

The rapid postharvest deterioration of ‘Thongprasert’ jackfruit (Artocarpus heterophyllus Lam. cv. ‘Thongprasert’) poses significant challenges for export and commercial distribution. This study evaluated the combined effects of hydrogen peroxide (H₂O₂) and 1-methylcyclopropene (1-MCP) on extending shelf life and maintaining postharvest quality during cold storage. A completely randomized design was applied with five treatments: untreated control, 0.175% H₂O₂ alone, and 0.175% H₂O₂ combined with 1-MCP at concentrations of 0.03%, 0.06%, and 0.09%. Fruits were stored at 13 ± 1°C and assessed on days 5, 10, 15, and 20 for percentage weight loss, peel and flesh color (L*, a*, b*), firmness, total soluble solids (TSS), and disease incidence. The results showed that H₂O₂ combined with 1-MCP significantly reduced percentage weight loss, with the 0.09% 1-MCP treatment exhibiting the lowest loss after 20 days. However, for other quality parameters such as color, firmness, and TSS, the differences between the 0.06% and 0.09% 1-MCP treatments were not always statistically significant. Peel color retention was improved, with significantly lower L* (day 15) and a* (day 5) values observed in the treated groups. TSS levels increased over time in all treatments, with no significant differences among them, although values were generally higher than in the control. Disease incidence was significantly reduced in all H₂O₂ + 1-MCP treatments from day 10 onward, indicating strong antimicrobial effects. In conclusion, the combined application of 0.175% H₂O₂ and 0.06% 1-MCP was the most effective in prolonging shelf life and maintaining postharvest quality of ‘Thongprasert’ jackfruit during cold storage. These findings support its potential integration into commercial postharvest handling and export protocols to reduce the postharvest losses and preserve fruit quality.

Image-Based Characterization and Statistical Optimization of Silver Nanoparticles Biosynthesized Using Pasteurized Milk

Tarit Apisittiwong, Nuttawan Yoswathana, Vinod K Jindal — Sat, 20 Sep 2025 00:00:00 +0700

Background: Green synthesis of silver nanoparticles (AgNPs) has garnered attention for its sustainability, yet few studies have integrated digital imaging for nanoparticle characterization. Objective: This study aimed to synthesize AgNPs using pasteurized milk as a natural reducing and stabilizing agent, and to optimize synthesis conditions using a rotatable central composite design (CCD), coupled with spectroscopic and image-based analytical methods. Methods: AgNPs were synthesized under varying conditions of milk dilution, AgNO₃ concentration, and reaction time. Response variables - SPR wavelength, absorbance, particle size, and imaging-derived parameters (∆RGB, ∆Lab, MGL) were modeled using second-order polynomial regression. Digital imaging under forward, backward, and transmitted light geometries were used to quantify nanoparticle-induced optical changes. Results: Most models showed high predictive power (adjusted R² > 0.80), with image-based variables (∆RGB, ∆ASM, MGL) strongly correlated with particle concentration and optical density. Optimal conditions (milk:DI 1:15, 2.00 mM AgNO₃, 2 h) yielded AgNPs with a strong SPR response (412 nm), small size (95.8 nm), and distinct visual signatures. Predicted responses matched closely with experimental data, validating the model. Conclusion: This study presents a reproducible, low-cost platform for sustainable AgNP synthesis. The incorporation of digital imaging enhances real-time monitoring and offers promising applications in diagnostics, food safety, and green nanotechnology.

Chessboard-coding Metasurface Antennas with Binary Defects for Anomalous Radiation: Novel and Continuous Development

Tanatorn Tantipiriyakul, Komsan Kanjanasit — Sat, 20 Sep 2025 00:00:00 +0700

This paper presents a numerical analysis of chessboard-coding metasurface antennas, focusing on the impact of binary coding defects on beamforming radiation characteristics. Chessboard-coding metasurface antennas, composed of 1-bit unit cells with binary phase distributions (0° and 180°), enable near-field wavefront control for beam steering applications. Beam tilting is achieved by introducing binary defects, which break phase continuity and affect radiation performance. This study investigates a planar antenna and examines the effects of binary defects in metasurface unit cells by analyzing reflection characteristics, impedance variations, and radiation patterns at 9 GHz. Twelve defect configurations are simulated to observe beam tilting and distortion patterns, revealing a strong dependence on defect location. The spatial distribution of defects within the metasurface lattice is categorized into inner and outer regions, according to their impact on beam characteristics. Numerical results show that binary defects can redirect beams in both azimuth and elevation. The defective cell locations in the 5 × 5 chessboard pattern reveal symmetric beam shifts in azimuth (0°, ±50°, ±110°, and ±137°) and elevation (+17.5° and +22.5°), with antenna gains ranging from 4.1 to 5.3 dBi compared to a 5.57 dBi baseline. Impedance bandwidths are observed approximately within the 8.4–9.5 GHz range. These findings offer valuable design insights for developing robust, reconfigurable metasurface antennas suited for next-generation 6G communication systems operating in the centimeter-wave band.

Production of Reduced-fat Plant-based Salad Dressing Stabilized with Carboxymethyl Cellulose and Aquafaba

Sat, 20 Sep 2025 00:00:00 +0700

In recent years, plant-based foods have gained substantial momentum around the world. Egg yolk, a key ingredient, is commonly used as an emulsifier in most salad dressings. Aquafaba was employed as an egg substitute in the development of plant-based salad dressings. Additionally, carboxymethyl cellulose (CMC) was incorporated as a stabilizing agent to enhance the emulsion stability of the formulation. The results showed that the control salad dressing (made with egg yolk) and the plant-based salad dressing containing 0.2–0.3 wt% CMC were highly stable with an emulsion stability index (ESI) of more than 90%, and a z-potential of approximately ±30 mV. To develop a healthier, lower-calorie alternative, reduced-fat plant-based salad dressings were formulated by decreasing the oil content and substituting it with chickpea-derived aquafaba. The results demonstrated that the oil used in the formulation could be reduced from 60 wt% to 45 wt% without significantly affecting the ESI. The droplet diameter of the reduced-fat plant-based salad dressing containing 45 wt% oil was significantly smaller than that of the full-fat formulation with 60 wt% oil. Furthermore, Wolffia was incorporated into the formulation to develop a functional salad dressing with enhanced health benefits. Fortification with Wolffia significantly decreased the lightness, redness, ESI, and z-potential of the plant-based salad dressing. However, fortification with Wolffia did not significantly affect the yellowness of the plant-based salad dressing. The research findings could benefit further research efforts focused on the development of alternative health-promoting food products.

Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recommendation

Sat, 20 Sep 2025 00:00:00 +0700

This research presents an integrated machine learning approach for optimizing digital workforce matching in Thailand's evolving digital economy. The study develops a novel job recommendation system combining Natural Language Processing (NLP) with Random Forest classification to analyze job market data from Thailand's leading recruitment platforms. Using FastText for initial job classification and a Random Forest model for skill-based matching, the system achieves 75% accuracy in job recommendations across 20 digital job categories. The methodology incorporates automated skill extraction, cross-validated model comparison, and a user-friendly web interface for practical applications. Our findings reveal distinct skill clusters and job-skill relationships in Thailand's digital sector, with the Random Forest model outperforming traditional Decision Tree approaches by 4% in accuracy metrics. The system demonstrates robust performance in real-world testing, achieving 86.67% accuracy in matching previously unseen job postings. This research contributes to both theoretical understanding of skill-based job matching and practical workforce development, offering insights for curriculum development and career planning for workforce development stakeholders in Thailand's digital sector.

Earthquake Early Warning Using Multi-Channels Echo State Extreme Learning Machine

Phiphat Chomchit, Somrawee Aramkul, Yuthapong Somchit, Paskorn Champrasert — Sat, 20 Sep 2025 00:00:00 +0700

Predicting earthquake strong motions is crucial for mitigating seismic risks and enhancing the effectiveness of Earthquake Early Warning (EEW) systems. While conventional models are capable of high precision, they often require substantial computational resources, limiting their practicality for real-time applications. This study proposes the Multi Echo-State Extreme Learning Machine (Multi ES-ELM), an efficient and effective alternative for strong motion prediction. It compares the performance of Multi ES-ELM with two well-established models-Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)-using multi-channel time-series data. The CNN model achieved high performance with an accuracy of 94.65 ± 0.30, recall of 92.84 ± 2.36, precision of 87.34 ± 2.35, and F1-score of 89.95 ± 0.41. In contrast, the RNN model showed significant variability, with an accuracy of 84.83 ± 19.40, recall of 84.93 ± 13.34, precision of 74.18 ± 18.15, and F1-score of 77.80 ± 16.40. Notably, the Multi ES-ELM model demonstrated competitive accuracy (93.46 ± 0.22), high recall (96.50 ± 0.52), precision (81.53 ± 0.53), and F1-score (88.38 ± 0.37), while using significantly fewer resources-only 882 parameters and a model size of 0.003 MB. These results highlight Multi ES-ELM as a highly efficient and reliable model for real-time EEW, overcoming the computational challenges of traditional approaches. Its performance and resource efficiency underscore its potential for practical implementation in seismic risk mitigation and for improving community resilience against seismic hazards.

Predicting Systolic and Diastolic Blood Pressure Response Using Machine Learning: A 96-Feature Analysis in Hypertensive Patients with Comorbidities

Sat, 20 Sep 2025 00:00:00 +0700

Hypertension represents a complex condition that substantially increases the global cardiovascular disease burden and related deaths. This study compares three tree-based machine learning approaches-Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)-using 96 multi-domain features to predict reductions in both systolic and diastolic blood pressure following antihypertensive treatment in patients with varying comorbidity profiles. Our approach utilizes paired t-test analyses to examine blood pressure changes before and after medication across different patient categories, while employing comprehensive decision tree visualisation to create interpretable decision pathways that identifying predictive associations between medications and blood pressure outcomes. Analysis of 160 patients indicated significant blood pressure improvements in all studied patient groups, with systolic blood pressure reductions showing statistical significance (p = 0.001) and diastolic blood pressure changes demonstrating similar significance levels (p = 0.02). The Decision Tree method showed optimal performance for systolic blood pressure prediction, recording 93% F1-score and 83% AUC values, whilst Random Forest demonstrated excellence performance in diastolic blood pressure prediction with 98% F1-score and 92% AUC. XGBoost performed less effectively than the other two algorithms across metrics. Through decision tree analysis, we identified strong predictive associations between diuretics and ACE inhibitors with systolic blood pressure reduction, whilst nitrate compounds and combined medication regimens showed significant predictive relationships with diastolic blood pressure decrease. The machine learning models successfully integrated diverse patient characteristics across multiple domains, including demographics, clinical parameters, lifestyle factors, and socioeconomic determinants. Our findings from this 160-patient cohort demonstrate the clinical utility of interpretable machine learning models for medication response prediction, providing valuable insights that can guide personalized antihypertensive therapy selection and inform clinical decision-making through data-driven treatment approaches.

Survivability of Microencapsulation Probiotic Bacteria in Sodium Alginate-Goat Milk-β-Glucan Matrix under Freeze-drying Conditions for Dog Supplement

Sat, 20 Sep 2025 00:00:00 +0700

The successful oral delivery of probiotics to the canine gastrointestinal tract necessitates protection against harsh environmental conditions. This study investigated the efficacy of a novel alginate-goat milk-beta-glucan matrix for the microencapsulation of three canine-associated lactic acid bacteria (LAB) strains: Agrilactobacillus fermenti Pom1, Limosilactobacillus fermentum Pom5, and Pediococcus pentosaceus Chi8. High encapsulation efficiencies (88.25% to 99.33%) were achieved across all strains and cocktails, indicating successful cell entrapment. Following freeze-drying and two months of storage, microencapsulation significantly enhanced the survival of all probiotic strains and cocktails compared to their unencapsulated counterparts. Furthermore, microencapsulated probiotics demonstrated superior resilience under simulated gastrointestinal conditions. While all cells initially maintained high viability in simulated oral conditions, encapsulation provided robust protection in simulated gastric fluid, where only encapsulated cells remained viable after
60 minutes, whereas free cells were completely inactivated. P. pentosaceus Chi8 exhibited the highest survival in gastric conditions (76.23% after 120 minutes at 2 months), and both encapsulated cocktails survived up to 180 minutes. Similarly, under simulated intestinal conditions, encapsulated cells consistently maintained significantly higher viability than free cells, with A. fermenti Pom1 showing 83.38% viability after 180 minutes at 2 months, compared to 0% for its unencapsulated form. This comprehensive evaluation of the alginate-goat milk-beta-glucan matrix for these specific canine LAB strains under freeze-drying, storage, and simulated gastrointestinal conditions represents a novel contribution. The findings underscore the potential of this matrix as an effective delivery system for canine probiotics, paving the way for the development of stable formulations aimed at improving canine gut health.

Correlation between Serum Levels of Cyclooxygenase II and Microsomal Prostaglandin E Synthase 1 in Iraqi Colorectal Cancer Patients and Healthy Controls

Zainab Ismael Abbas, Ahmed Gh Sabbar, Wajeeh k Obaid — Sat, 20 Sep 2025 00:00:00 +0700

Colorectal cancer (CRC) ranks among the most widespread cancers worldwide, exhibiting considerable mortality rates. Chronic inflammation plays a crucial role in driving tumor development. Cyclooxygenase-II is an inducible enzyme that aids in the production of prostaglandins using arachidonic acid, particularly during inflammatory responses. Microsomal prostaglandin E synthase-1, functioning downstream of COX-2, specifically converts PGH2 to PGE2, and correlates with unfavorable outcomes in CRC. While their roles have been well documented in tissue, limited research has assessed their levels in serum. The present study aimed to evaluate the serum levels of COX-2 and mPGES-1 in CRC patients compared to healthy controls, and to investigate the correlation between these biomarkers across all participants, regardless of clinical grouping. A total of 70 patients, 35 newly diagnosed and 35 undergoing treatments were included, along with 30 healthy individuals as controls. Blood samples were collected from healthy individuals and Iraqi CRC patients. Serum concentrations of COX-2 and mPGES-1 were measured using ELISA. Marked variations in the levels of these enzymes were noted among the examined groups, with newly diagnosed patients showing the highest levels compared to treated patients and healthy control. A robust positive correlation was observed between levels of COX-2 and mPGES-1 in all groups. These outcomes suggest that serum levels of Cyclooxygenase II and Microsomal Prostaglandin E synthase 1 could serve as prospective diagnostic indicators for CRC patients.

Application of Riboflavin and Curcumin as Natural Photosensitizers for Antimicrobial Photodynamic Inactivation against Staphylococcus aureus: A Study for Skin Disease

Sat, 20 Sep 2025 00:00:00 +0700

Atopic Dermatitis (AD) and Actinic Keratosis (AK) are becoming increasingly prevalent in developing countries, including Indonesia. Staphylococcus aureus, a common microorganism associated with both conditions, presents treatment challenges due to the increasing antibiotic resistance and associated side effects. Photodynamic Inactivation (PDI), which employs natural photosensitizers such as riboflavin and curcumin in combination with Omega Light LED-a technology commonly used in aesthetic treatments-offers a potential alternative. In this study, riboflavin and curcumin were applied separately at a concentration of 0.015% (w/v) and irradiated with red (λ = 640 nm), blue (λ = 423 nm), or green (λ = 532 nm) light using an Omega Light LED device (O'melon). Cell viability was assessed using an ELISA reader at 595 nm after irradiation durations of 10, 30, and 60 minutes. Skin toxicity was predicted using Toxtree 3.1.0, Pred-Skin 3.0, and pkCSM web-based tools. Results showed that the photosensitizers without irradiation were not cytotoxic to Staphylococcus aureus. However, the combination of blue light and photosensitizers significantly inhibited bacterial viability. Riboflavin achieved 49.0±4.8% inhibition within 10 minutes, indicating a rapid but transient effect, whereas curcumin elicited a slower yet sustained antibacterial response, achieving 34.2 ± 1.6% inhibition after 30 minutes. Computational toxicity predictions suggested no clear evidence of skin irritation; however, a potential for skin sensitization remains. These findings support the potential of riboflavin- and curcumin-based PDI using Omega Light LED as a promising non-antibiotic approach for managing Staphylococcus aureus infections in AD and AK.

Inhalation Health Risk Assessment of Formaldehyde Exposure among Staff and Students in the Gross Anatomy Laboratory

Sat, 20 Sep 2025 00:00:00 +0700

Staff members, and students are potentially exposed to formaldehyde vapors emitted by cadavers during gross anatomy sessions. This study aimed to assess the inhalation risk of formaldehyde exposure among individuals in gross anatomy laboratories, focusing on both carcinogenic and non-carcinogenic effects. A total of 101 participants, comprising staff, pre-clinical students, and public health students, completed a structured questionnaire to provide demographic and exposure-related data. Simultaneously, five continuous indoor air samples were collected during 8-hour laboratory sessions following NIOSH Method 2541. The samples were analyzed at the Department of Disease Control, Ministry of Public Health. Risk assessments were conducted based on hazard quotient (HQ) and cancer risk values. Descriptive statistics, including frequency, percentage, minimum, maximum, mean, and standard deviation, were applied in the data analysis. The results indicated that the formaldehyde content ranged from 54 to 74 µg/m³, with a mean ± SD of 65±10 µg/m³. Staff members exhibited the highest HQ at 4.07, exceeding the safety threshold (HQ > 1), indicating significant non-carcinogenic risk. In contrast, pre-clinical students and public health students showed HQ values below 1, suggesting relatively lower but notable exposure. Cancer risk values for all participants ranged from 7.40×10⁻⁸ to 1.27×10⁻⁹, all below the accepted threshold of 1×10⁻⁶, implying an acceptable level of carcinogenic risk. The findings are significant as they highlight a measurable health risk for staff regularly working in gross anatomy laboratories. It is recommended that institutions establish policies for the implementation of efficient ventilation systems, enforce the use of personal protective equipment, and consider formaldehyde alternatives where possible. In addition, future studies should look into DNA adducts and their effects on cells, particularly those related to blood disorders and long-term cancer risks linked to formaldehyde exposure.

Deep Learning Approach for Predicting Thermal Behavior of Hydropower Generator-Stator: A Case Study of a Hydropower Power Plant in Thailand

Sat, 20 Sep 2025 00:00:00 +0700

Hydropower generation is a cost-effective and environmentally friendly energy source that converts the kinetic energy of flowing water into electricity. However, temperature control in power generators, particularly in the conductor windings in the stator, remains a significant challenge for maintaining power generation performance. Several factors influence temperature, and their relationships are quite complex, making it difficult to solve the problem using standard theoretical approaches. This research developed a deep learning model to monitor temperature trends in the conductor windings of a 125 MW hydropower plant in Thailand. Data collected between 2018 and 2021 on electricity generation, reservoir water levels, water and air flow rates, inlet temperatures at the heat exchanger, and conductor winding temperatures were used to train and validate the models. The study implemented three neural network models: a Feedforward Neural Network (FNN), a Multilayer Feedforward Neural Network (MFNN), and a Long Short-Term Memory (LSTM) network. The results showed that the LSTM model provided the most accurate predictions, with a Mean Squared Error (MSE) of 0.00373. Shapley Additive exPlanations (SHAP) values were used to interpret the model predictions, identifying key variables such as electricity generation, water temperature, and water flow rate as the most influential factors affecting system behavior. The findings suggest that deep learning models can effectively predict temperature variations, enabling proactive maintenance and improving operational efficiency in hydropower plants.

Security Analysis and Mitigation of SSL Stripping, Homograph Redirection, and Keylogging Attacks: A Case Study on Thai Web Platforms

Sat, 20 Sep 2025 00:00:00 +0700

The cybersecurity of critical Thai digital infrastructure is a pressing concern for national security. This research, conducted in collaboration with Thailand's Department of Special Investigation (DSI), presents a comprehensive security assessment of 27 specifically selected websites across financial, commercial, and educational sectors. Our investigation focuses on three critical attacks: SSL stripping, homograph redirection attacks, and keylogger injection. The findings reveal that 96.3% (26/27) of the examined websites are vulnerable to SSL stripping attacks due to inadequate HTTP Strict Transport Security (HSTS) implementation. Notably, even the sole website with proper HSTS Preload configuration demonstrated susceptibility to homograph attacks. Furthermore, all examined websites were susceptible to keylogger injection after successful Man-in-the-Middle (MITM) attacks, even when password hashing was used. To counter these threats, we propose an enhanced security framework integrating a Time-based Salted Hash Password (TSHP) mechanism and an On-Screen Keyboard (OSK) for login interfaces. Experimental evaluation shows that TSHP improves resistance to brute-force and replay attacks by generating dynamic, time-variant hashes, while OSK input prevented 100% of JavaScript keylogger captures when used exclusively. These countermeasures offer practical, low-cost solutions to strengthen Thailand’s digital services, enabling immediate deployment without infrastructure overhaul. Our findings provide actionable recommendations for policymakers and system administrators to enhance the cybersecurity posture of Thai web platforms, with broader implications for securing digital economies globally.

Optimization of Base Oil Recovery from Used Lubricating Oil through Extraction-Adsorption using Response Surface Methodology

Patcharee Kamthita, Budsaba Leelasinlatham — Sat, 20 Sep 2025 00:00:00 +0700

Response surface methodology, based on the central composite design, was successfully applied to study the optimum conditions and the statistical effects of the variables on base oil recovery from used lubricating oil using a two-step process for oil recovery: solvent extraction with 1-butanol compared to methyl ethyl ketone, followed by adsorption using activated clay. 1-Butanol was found to be more effective than methyl ethyl ketone for oil extraction and sludge removal. The optimum conditions for oil extraction were determined to be a 1-butanol-to-oil ratio of 3.2:1 at 57ºC and a mixing speed of 600 rpm. The removal of sludge was achieved at 12.08% by weight. Residual contaminant adsorption from the extracted oil was optimized using 55% activated clay by weight, stirred at 200 rpm 130°C. The physical properties of the treated oil were analyzed, revealing a specific gravity of 0.84, a viscosity index of 144, and a color reduction from 8.0 to 1.0. The chemical properties were analyzed by IR spectroscopy, showing reduced nitration, oxidation, sulfation, total base number, and the absence of water. Additionally, the GC-MS composition analysis of the treated oil revealed that 99.64% of the oil was composed of alkanes, with a trace amount of aromatic compounds and no naphthalene or polycyclic aromatic hydrocarbons (PAHs). The elemental content analysis by atomic emission spectroscopy showed that the residue was less than 1 ppm. As a result of the recovery under optimum conditions, the treated oil has chemical and physical characteristics that make it suitable for repurposing as base oil in industrial applications.