Scopus 2025
Permanent URI for this collectionhttps://repository.li.mahidol.ac.th/handle/123456789/102712
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Item Metadata only Factors Predicting Stroke Survivors’ Activities of Daily Living from Family Caregivers’ Perspectives in Thailand(2025-12-01) Terathongkum S.; Suanpan N.; Terathongkum S.; Mahidol UniversityBackground: Stroke survivors tend to face disabilities that impact activities of daily living (ADL). However, limited studies, particularly across diverse cultural contexts in Thailand, have examined the factors affecting the ADL of stroke survivors from the perspectives of family caregivers. Purpose: This study aimed to examine factors predicting the activities of daily living of stroke survivors from the perspectives of family caregivers. Methods: A cross-sectional correlational research design using secondary data was employed. Ninety-nine family caregivers from diverse cultural backgrounds who met the inclusion criteria were recruited into the study using stratified random sampling and completed seven questionnaires, including demographics, perceived self-efficacy, ADL, family relationships, social support, caregiver stress, and illness beliefs. All data were analyzed using Pearson’s product-moment correlation coefficient, chi-square test, and multiple linear regression with the stepwise method. Results: Family caregivers perceived that stroke survivors had a moderate level of ADL (M = 12.88, SD = 6.23). Age, gender, communication ability, and severity of stroke were significantly correlated with ADL (p <0.01). Moreover, the severity of stroke, gender, improved symptoms, education, and age of stroke survivors were significant predictors of ADL, accounting for 41.6% of the variance (F = 13.27, p < 0.001). Conclusion: This study indicated that the severity of stroke, gender, improved symptoms, education, and age of stroke survivors could predict ADL. These findings offer valuable insights for nurses, highlighting the importance of effectively rehabilitating stroke survivors before discharge from the hospital to home to achieve better clinical outcomes and an improved quality of life.Item Metadata only Evaluating the Efficacy of Machine Learning Techniques in Ransomware Detection(2025-01-01) Meechanchuang K.; Sitsaengchai P.; Bowornsujaritkul K.; Tritilanunt S.; Phienthrakul T.; Meechanchuang K.; Mahidol UniversityRansomware continues to pose a critical threat to computer systems worldwide, requiring effective detection strategies that can generalize across evolving variants. This paper presents a comparative evaluation of multiple machine learning algorithms for ransomware detection using dynamic analysis. Behavioral features were extracted from ransomware samples via Cuckoo Sandbox, and standard classifiers including Decision Tree, Random Forest, Gradient Boosting, and XGBoost were evaluated with appropriate train-test splits and feature selection. Results show that Random Forest consistently achieves superior performance on unseen ransomware families, highlighting its robustness and practical applicability.Beyond accuracy, this study examines computational considerations, revealing that tree-based models offer favorable tradeoffs between detection efficacy and inference latency, making them suitable for near real-time deployment. Feature importance analysis further indicates that registry modifications, file operations, and cryptographic API calls are key behavioral traits distinguishing ransomware activity.Nevertheless, the study faces limitations, including a relatively small dataset (582 ransomware samples), basic class imbalance handling, and the absence of deep learning baselines. To address these gaps, future work will explore dataset expansion, advanced imbalance handling techniques, neural architectures, and large-scale deployment evaluation. By emphasizing both detection accuracy and forensic interpretability, this work contributes practical insights for improving ransomware defense in real-world environments.Item Metadata only Dormlander: a Smart Dormitory Management System with Utility Metering and Digital Payment Solution(2025-01-01) Tangsripairoj S.; Srijirapattanakul S.; Ritiluechai T.; Kaeodumkoeng P.; Tangsripairoj S.; Mahidol UniversityThe dormitory business offers strong income potential and long-term investment value, but it often faces many challenges including manual operations, time-consuming, human errors, and inefficient payment tracking. This study introduces Dormlander, a smart web and mobile application designed to streamline dormitory management and improve operational efficiency. The primary objective of this project is to analyze, design, and develop an all-in-one platform that benefits dorm owners, staff, and renters. Dormlander integrates Optical Character Recognition (OCR) technology to automate electricity and water meter readings, significantly reducing manual errors and saving time for dorm staff. The system features a web platform for dorm staff and owners to manage rooms, renters, utilities, billing and receipts. It also includes a comprehensive dashboard that visualizes key business metrics, enabling data-driven decision-making. For renters, the mobile application offers real-time utility tracking, digital bill payments, built-in chat, and calendar features to enhance communication and convenience. By leveraging cloud-based technologies, Dormlander improves operational transparency, enhances communication, and supports efficient dormitory administration. Ultimately, Dormlander helps transform traditional dorm operations into a modern, scalable, and user-friendly experience.Item Metadata only Classification of Sugarcane Leaf Diseases Using Vision Transformers and CNN Models(2025-01-01) Silapachote P.; Srisuphab A.; Wutthiumphol K.; Tanprathumwong Y.; Pohboonchuen T.; Silapachote P.; Mahidol UniversityA globally prominent economic crop, sugarcane is an indispensable raw material for over 80% of sugar production worldwide. In Thailand, the sugarcane and sugar industry holds a top position in export markets. The loss of sugarcane crops due to diseases is a devastating problem that can never be overstated. Not only does it affect the economy, but it is also the primary source of income for many farmers in the provinces. To prevent a wide spread of any disease, farmers have long been heavily relying on visual inspections and their expertise to detect any signs of disease as early as possible. To assist farmers, this work applied computer vision and machine learning technology to help classifying sugarcane diseases from its leaves. Deployed on mobile devices, our application allows farmers to easily send to our chat-bot a photo of their suspected sugarcane leaves, and get a real-time response specifying the name of the disease or none if it is deemed healthy. Trained and fine-tuned on public data sets, our classifier, which is a vision transformer model, outperformed previous works. Tested on a newly collected local data set, ours achieved a high accuracy 79.64%.Item Metadata only The Symbolic Interplay of Korean Pensive Buddha Statue and Colour: A Case Study in Thailand(2025-01-01) Yoon Y.; Hung P.C.K.; Hsieh C.W.; Narupiyakul L.; Tseng H.A.; Badalov K.; Cheung V.; Jiang A.; Yoon Y.; Mahidol UniversityIn winter 2025, an observational experiment was conducted at Mahidol University, Thailand, to investigate how lighting colour influences individuals' perceptions of religious imagery. The study examined how participants emotionally and symbolically respond to a pensive Korean Buddha statue when presented under different colored lighting conditions. We experimented with 116 participants who viewed the statue inside a black box illuminated by five colors-red, yellow, blue, green, and white-in randomized order. After each exposure, participants completed semantic differential scales measuring formality, authenticity, sacredness, memorability, perceived value, and durability. White lighting received the highest ratings for sacredness, perceived value, and authenticity; yellow excelled in memorability and sacredness; blue scored lowest on authenticity and formality; red was near-neutral. These findings inform the design of religious spaces and the presentation of sacred artifacts.Item Metadata only Mind Bloom: a Mobile Application for Mental Health Self-Care(2025-01-01) Tangsripairoj S.; Kodcharin S.; Autthasom H.; Kumeak K.; Tangsripairoj S.; Mahidol UniversityIn recent years, mental health has become an increasingly prominent concern in society due to rapidly changing lifestyles, heightened stress, anxiety, and mounting pressures. Mental health care is therefore essential, as it directly impacts personal well-being and quality of life. Tools that support systematic assessment, monitoring, and promotion of mental health self-care can empower individuals to take proactive and consistent care of their mental well-being. Mind Bloom is a mobile application designed to support holistic mental health care. It comprises eight core functions: mental health assessment, mood tracking, wellness activities, emotional expression, mental health education, notification reminder, supporting services, and motivation building. This application enables users to systematically assess, monitor, and manage their mental health by recording their daily emotions, engaging in personalized activities, and accessing relevant information and resources. The goal of the application is to improve users' quality of life, promote sustainable mental well-being, and contribute to building a mentally healthier society. The evaluation results with 40 users showed high satisfaction levels, with 92.5% of participants rating their experience as 'Satisfied' or 'Very Satisfied,' and no users expressing dissatisfaction. The most beneficial features identified were mental health assessment, mood tracking, and self-care learning resources.Item Metadata only Botulinum Toxin A: Aesthetic Uses(2025-01-01) Suwanchinda A.; Suwanchinda A.; Mahidol UniversityBotulinum toxin type A (BTX-A) is the cornerstone of modern aesthetic practice, transforming facial rejuvenation with precision and versatility. Acting by transiently blocking acetylcholine release, it induces controlled muscle relaxation and glandular suppression. This chapter highlights the evolution of BTX-A, from its origins to current advances in first- and second-generation formulations with differing immunogenic profiles. Core indications include dynamic facial lines, masseter and temporalis hypertrophy, platysma bands, hyperhidrosis, and parotid gland hypertrophy. Off-label innovations such as microdroplet intradermal injection (“Microbotox/Mesobotox”) are emphasized for skin refinement, lifting, and contouring with natural outcomes. Practical insights into dosing, dilution, and anatomy-driven injection techniques are provided, along with strategies to avoid complications and resistance. Blending evidence-based science with clinical pearls, this chapter delivers an authoritative guide for safe, effective, and artistry-driven BTX-A treatments.Item Metadata only Indoxyl sulfate: clinical implications for anemia management in chronic kidney disease(2025-12-08) Rattanasompattikul M.; Srithongkul T.; Tantisattamo E.; Kalantar-Zadeh K.; Noppakun K.; Rattanasompattikul M.; Mahidol UniversityPurpose of review – This review examines the role of indoxyl sulfate, a gut-derived uremic toxin, in the development of anemia in chronic kidney disease. It dissects the cellular and biochemical mechanisms through which indoxyl sulfate suppresses erythropoietin production, disrupts iron metabolism, and promotes oxidative stress and inflammation. Recent findings – Indoxyl sulfate interferes directly with the hypoxia-inducible factor pathway, thereby reducing the transcriptional activation of erythropoietin. In parallel, indoxyl sulfate-induced oxidative stress damages red blood cells and accelerates premature cell death, while its stimulation of pro-inflammatory pathways further downregulates erythroid progenitor cell function. Therapeutic strategies such as dietary protein modulation, gut microbiota interventions, oral adsorbents, and enhanced dialysis modalities have shown promise in lowering indoxyl sulfate levels and, consequently, improving erythropoietin responsiveness and iron homeostasis in chronic kidney disease patients. Summary – The review synthesizes evidence from clinical and experimental studies that position indoxyl sulfate as a central yet underappreciated mediator of anemia in chronic kidney disease. Indoxyl sulfate establishes a vicious cycle that exacerbates anemia and contributes to erytropoiesis-stimulating agent hyporesponsiveness. The article advocates for targeted interventions aimed at reducing indoxyl sulfate burden, which could transform anemia management in chronic kidney disease and pave the way for personalized treatment strategies.Item Metadata only Interrelationship between fatigue and recovery of physical function in stroke survivors: a longitudinal mixed-methods study(2025-10-01) Teng C.H.; Anderson R.A.; Zou B.; Wu J.R.; Phonyiam R.; Leak Bryant A.; Lutz B.J.; Davis L.L.; Teng C.H.; Mahidol UniversityBACKGROUND: Research has shown that late fatigue post-stroke is associated with poorer long-term outcomes, but the association between early fatigue with concurrent outcomes like physical function within six months is underexplored. AIM: To explore the interrelationship between stroke survivors' adaptation to fatigue and physical function changes during hospitalization and at one, three, and six months post-stroke. DESIGN: A prospective longitudinal cohort study with a convergent mixed-methods design. METHODS: Adults (≥18 years) with first-ever ischemic stroke were included. Fatigue, physical function, and data from semi-structured interviews were collected at four time points. A mixed-effect model was used to explore the quantitative relationship, with physical function as the dependent outcome and fatigue as the fixed-effect variable. Directed content analysis was used for qualitative data. A side-by-side display was used to present mixed-methods findings. RESULTS: Thirty-two survivors were in the quantitative arm; nine of those were in the qualitative arm. Quantitative analysis showed that each unit increase in fatigue decreased physical function by 0.27, adjusting for age, depression, and time. Qualitative findings confirmed that fatigue hindered recovery and pre-stroke activity resumption. Survivors described a vicious cycle between fatigue and function, with varying fatigue patterns and exacerbating factors within six months. CONCLUSIONS: Fatigue and physical function were interrelated within six months after stroke. Given the small, single-center sample, these results should be interpreted cautiously. Still, our findings highlight the value of early, systematic fatigue assessment and collaborative discussions between survivors and health professionals to guide individualized management strategies. CLINICAL REHABILITATION IMPACT: Managing post-stroke fatigue requires both survivor-led strategies (e.g., self-monitoring, rest, pacing) and professional support to address contributing conditions. Routine follow-up should include systematic fatigue assessment, collaborative discussion of management options, and periodic re-evaluation to optimize recovery.Item Metadata only Characterization of lipase-producing halophilic bacteria and partial purification of lipase from Virgibacillus halodenitrificans SKP5-4(2025-01-01) Yiamsombut S.; Daroonpunt R.; Namwong S.; Visessanguan W.; Savarajara A.; Tanasupawat S.; Yiamsombut S.; Mahidol UniversityAims: The aim of this research was to isolate and characterize lipase-producing halophilic strains and partially purify lipase from Virgibacillus halodenitrificans SKP5-4. Methodology and results: Eighteen strains of moderately halophilic bacteria were isolated from shrimp paste (Ka-pi) in Thailand. Based on the phenotypic and genotypic characteristics, ten lipase-producing halophilic bacteria were identified as Bacillus amyloliquefaciens subsp. plantarum (SKP1-4), Bacillus salacetis (SKP7-4), Thalassobacillus hwangdonensis (SKP1-5), Staphylococcus saprophyticus subsp. bovis (SKP2-1 and SKP2-3), Oceanobacillus manasiensis (SKP5-5), Allobacillus halotolerans (SKP2-8), Allobacillus salarius (SKP4-8), Allobacillus saliphilus (SKP8-2), and V. halodenitrificans (SKP5-4). Strain SKP5-4 exhibited high lipase production and was selected for partial purification using HitrapTM DEAE FF anion exchange chromatography and 40-60% cold acetone precipitation. The molecular mass of the protein was estimated at 12, 30, 43 and 50 kDa based on Native-PAGE (10% w/v). Optimal conditions for enzyme activity were pH 7.0, temperature 40 °C and 2% (w/v) NaCl. Additionally, the halophilic lipase showed higher activity at low ionic strength and maintained 80% relative activity at 3-9% (w/v) NaCl. Conclusion, significance and impact of study: This study characterized ten moderate halophiles and demonstrated the secretion of halophilic lipases from V. halodenitrificans SKP5-4. The halophilic lipase identified may contribute to the production of volatile fatty acids in shrimp paste, enhancing flavour and aroma.Item Metadata only Enhancing Cooperative Learning in Mathematics Education through Sociomathematical Norms in the Era of Society 5.0(2025-01-01) Yulia Y.; Kustati M.; Nelmawarni N.; Perrodin D.D.; Afriadi J.; Rahmadhani M.K.; Yulia Y.; Mahidol UniversityThe research analyzes the efforts to develop student cooperation profiles in mathematics subjects through sociomathematical norms. Society 5.0 is an era where humans collaborate with machines and technology. This is believed to diminish human relationships in society. This research applies a qualitative approach with literature studies described clearly, objectively, systematically, analytically, and critically to develop student cooperation profiles through sociomathematical norms. Research literature data was obtained from previous research articles published in reputable national and international journals. Data was collected using the Google Scholar search engine.Item Metadata only Offshore Platform Decommissioning Option Analysis Using the AnalyticHierarchy Process (AHP) Method: A Comparative Study of OnshoreDisposal (OD) and Carbon Capture Hub (CCH)(2025-01-01) Ng C.Y.; Yusof N.F.; Kang H.S.; Punurai W.; Chiu G.L.F.; Asavadorndeja P.; Ng C.Y.; Mahidol UniversityThis study evaluates offshore platform decommissioning options, comparing Onshore Disposal (OD) withrepurposing as a Carbon Capture Hub (CCH). OD involves high costs and environmental risks due tothe transportation of materials and waste handling. CCH offers a sustainable alternative by repurposingplatforms for long-term carbon sequestration, aligning with global sustainability goals. The AnalyticHierarchy Process (AHP) is used to assess OD and CCH based on platform type, weight management,logistical requirements, and structural integrity. Expert surveys and pairwise comparisons are conducted.The priority weights are derived using the Row Geometric Mean Method (RGMM) and Weighted GeometricMean Method (WGMM), while Geometric Consistency Index (GCI) is used to verify the consistency.Results indicate that Logistic Requirement is the most critical factor, followed by Platform Type andStructural Integrity. AHP rankings show that CCH (0.5736) is the preferred option over OD (0.4264). CCHminimizes environmental impact, optimizes infrastructure reuse, and aligns with regulatory frameworksfor carbon capture and storage. This study provides a structured decision-making framework for offshoreplatform decommissioning. Findings confirm CCH as a viable alternative, offering long-term environmentaland economic benefits over OD. Future research should validate structural feasibility and explore integrationwith renewable energy solutions.Item Metadata only "Application of Electronic Nose Technology for Aroma Profiling and Quality Assessment of Seasonings"(2025-01-01) Chujan K.; Somaudon V.; Kerdcharoen T.; Chujan K.; Mahidol UniversitySeasonings such as fish sauce, seasoning sauce, soy sauce, and oyster sauce are essential for augmenting taste and fragrance in culinary practices. Their sensory attributes are shaped by ingredients, fermentation, and production techniques. Conventional sensory evaluation techniques dependent on human panels frequently exhibit subjectivity and inconsistency. This study utilizes an electronic nose (e-nose) with eight sensors to detect volatile organic compounds (VOCs) emitted from four types of spice sauces. Principal Component Analysis (PCA) was employed to examine sensor responses, derive aromatic characteristics, and classify scent profiles. The findings indicate that the electronic nose successfully differentiated among sauce varieties, with Sensor 4 exhibiting the greatest sensitivity to critical volatile organic chemicals, including sulfur compounds and amines. PCA demonstrated clear clustering patterns among samples, indicating variations in raw materials and processing methods. These findings validate the efficacy of e-nose technology as a dependable instrument for product categorization, differentiation, and quality assurance in the food sector. The integration of sensor-based VOC detection with statistical modeling underscores the potential for real-time quality evaluation in food production settings.Item Metadata only Seismic Magnitude Prediction in Thailand Using Machine Learning(2025-01-01) Wasayangkool K.; Krutphong K.; Napasiripakorn B.; Suebyeam S.; Srisomboon K.; Lee W.; Wasayangkool K.; Mahidol UniversityEarthquake prediction plays a critical role in disaster preparedness, particularly in regions with growing infrastructure and seismic vulnerability, such as Thailand. This study presents a machine learning-based framework to predict the magnitude of earthquakes using geospatial and temporal features. A dataset of seismic events from 2010 to 2023 was compiled from public sources, covering Thailand and neighboring regions. After data cleaning and feature engineering, three regression models - Linear Regression, Random Forest, and XGBoost were trained and evaluated using standard metrics: MAE, RMSE, R2, and Explained Variance Score. The results demonstrate that XGBoost significantly outperforms the other models, achieving the lowest prediction errors and highest explanatory power. Feature importance analysis confirms the influence of spatial variables such as depth and latitude over temporal features. Residual analysis further supports the reliability and stability of tree-based models, particularly in handling nonlinear relationships. This research highlights the practical applicability of machine learning for earthquake risk mitigation and provides a foundation for integrating predictive models into urban planning, early-warning systems, and disaster resilience strategies in Thailand.Item Metadata only Optical evaluation of antioxidant activity: a comparison between a spectrophotometer and a homebuilt photometer.(2025-12-18) Luengviriya P.; Kiatsommart K.; Leelakanok C.; Patumorathai S.; Sibsiri K.; Wangkham T.; Luengviriya P.; Mahidol UniversityThe antioxidant activity of an orange peel extract is determined using a DPPH (2,2-diphenyl-1-picrylhydrazyl) solution. Sample absorption is measured with both a commercial spectrophotometer and a homebuilt photometer equipped with a 525-nm LED. Small, dried pieces of orange peel are extracted using 95% ethanol. Seven DPPH solutions with concentrations ranging from 0.1 to 0.4 mM are measured to establish calibration equations. For the spectrophotometer, absorbance exhibits a linear increase with DPPH concentration at both 517 nm (absorbance peak) and 525 nm (matching the LED in the photometer). In the homebuilt photometer, three piecewise linear fittings for concentration 0.1– 0.4 mM provide a better representation of absorption data compared to a single overall fitting across 0.1–0.4 mM. Different volumes of orange peel extract are added into the DPPH solution (0.4 mM DPPH after mixing) and stored in darkness for 30 minutes before the measurement. In both spectrophotometer and photometer analyses, antioxidant activity increases with the extract volume. Using spectrophotometer measurements as reference, the photometer with piecewise fittings produces significantly lower errors compared to those obtained with overall fitting.Item Metadata only Modification and Characterization of Waxy and Native Tapioca Starches for Stabilizing Resveratrol Pickering Emulsion(2025-12-18) Wannarat R.; Pleanklay A.; Boonsith S.; Thepwatee S.; Wannarat R.; Mahidol UniversityTapioca starch is a promising biopolymer for use as a solid particle stabilizer in Pickering emulsions. This study aimed to enhance the emulsifying performance of native (NS) and waxy (WS) tapioca starches through antisolvent precipitation followed by esterification with 3% (w/v) octenyl succinic anhydride (OSA). Structural and functional changes were characterized by SEM, XRD, DSC, FTIR, contact angle measurements, and degree of substitution analysis. The modifications transformed the starch morphology from smooth granules into rough, aggregated particles and reduced crystallinity, gelatinization enthalpy, and the intensity of characteristic functional groups. Both starch types exhibited similar structural changes; however, the modified native starch (NSp-OSA) showed higher hydrophobicity (contact angle: 103.73 ± 0.50°) than the modified waxy starch (WSp-OSA, 84.83 ± 0.60°), resulting in improved emulsion stability. Emulsion screening using jojoba oil identified optimal starch-to-oil ratios of 2.0:1.2 g/g for NSp-OSA and 1.0:1.2 g/g for WSp-OSA. These conditions were then applied for encapsulating resveratrol (RES). Due to the poor solubility of RES in oil, a ternary solvent system comprising of PEG 400, jojoba oil, and olive oil (0.1:0.5:0.5:0.5 g/g) was formulated to enhance solubility and emulsion uniformity. The NSp-OSA formulation produced more stable RES-loaded emulsions than WSp-OSA. This study demonstrates the potential of modified tapioca starches as bio-based stabilizers for emulsion-based delivery systems.Item Metadata only A Prompt-Driven Modular Framework for LLM-Based Agents in Scalable Interactive Learning Systems(2025-01-01) Li Y.; Kusakunniran W.; Wiratsudakul A.; Intagorn S.; Li Y.; Mahidol UniversityThis paper presents a prompt-driven modular architecture for integrating Large Language Model (LLM)-powered agents into adaptive learning systems. The framework enables real-Time, context-Aware dialogue and feedback through natural language configuration, eliminating the need for retraining or code changes. It features a four-layer design-covering environment control, agent logic, UI/UX, and feedback-implemented in a Unity-based learning application. A case study in medical education demonstrates how structured prompt templates support agent role assignment, behavior definition, and fallback strategies. Evaluation includes prompt reusability, dynamic role switching, and LLM accuracy comparison across multiple models, which show high correctness on domain-specific tasks. The system also supports low-cost deployment, making it feasible for scalable and accessible intelligent learning environments. This work offers a generalizable and configurable agent design method, contributing to the development of LLMintegrated tutoring systems and broader applications in natural language-driven interactive learning.Item Metadata only Practical Aspects of Edge-Cloud Platform for Health and Wellbeing(2025-01-01) Borwarnginn P.; Sa-Nguansook K.; Thongpakdee N.; Lertkijroongreung N.; Kusakunniran W.; Haga J.; Borwarnginn P.; Mahidol UniversityThis paper presents the lessons learned in deploying an edge-to-cloud-based platform to support the health and well-being of the elderly. Data was collected from various sensors in different formats and converted on edge devices into suitable types for further processing. We implemented and compared multiple architectural approaches for data analysis using Amazon Web Services (AWS). The results were visualized as interactive graphs on a web browser interface, achieving an average response time of around 7 seconds. Our study highlights performance considerations, architectural choices, and practical insights, offering best practices for the efficient deployment of cloud-based services in elderly care applications.Item Metadata only TraceCraft: A Tool for ISO/IEC 29110 Traceability Management(2025-01-01) Khumto N.; Tomyim P.; Thonguran P.; Choetkiertikul M.; Ragkhitwetsagul C.; Sangaroonsilp P.; Palakvangsa-Na-Ayudhya S.; Sunetnanta T.; Khumto N.; Mahidol UniversityThe software development process produces interdependent artifacts, including requirements, designs, code components, and test cases. Traceability records link these artifacts across stages to ensure that requirements are implemented in design, realized in code, and verified through testing. Maintaining such links is essential for quality, compliance, and efficient change management, but it is often time-consuming, and many existing tools lack comprehensive traceability support tailored for small projects. We present TraceCraft, a web-based platform designed to manage traceability and change tracking in alignment with ISO/IEC 29110, a standard widely adopted in Thailand to improve the quality of software in very small entities. TraceCraft streamlines artifact linkage, supports systematic verification and validation, and provides automated change impact tracking to maintain consistency across development stages. The platform offers visual indicators and detailed reports to highlight missing links, outdated items, and dependencies requiring attention, enabling teams to address potential issues early. In a usability study with nine practitioners in Thailand, TraceCraft achieved an average System Usability Scale (SUS) score of 73.17, indicating good usability and positive feedback, particularly for reducing the effort and complexity of maintaining ISO/IEC 29110-compliant traceability. A video demonstration is available at https://bit.ly/tracecraft.Item Metadata only Guidelines for Organizations on Protecting Against Cyber Threats through the use of Virtual Private Networks (VPN)(2025-01-01) Khantamonthon N.; Patpituck P.; Chimmanee K.; Khantamonthon N.; Mahidol UniversityThe increasing use of Virtual Private Networks (VPN) among organizations and industrial facilities has effectively addressed the demand for secure and convenient access to systems and data. However, reliance on VPNs introduces significant cyber threat risks, particularly ransomware attacks, which can encrypt critical information and render it inaccessible. This research aims to develop strategies for mitigating ransomware risks within VPN environments through a mixed-methods approach. This includes analyzing four cases of ransomware attacks using the 2SMatrix and identifying preventive measures using the NIST Cybersecurity Framework, complemented by focused discussions on specific issues. The findings reveal that, despite the implementation of robust NIST-compliant protective measures, human errors remain a significant concern, leading to the incorporation of the IT governance framework (COBIT) as an additional safeguard to enhance cybersecurity protection, such as the creation of a comprehensive VPN policy and the assessment and monitoring of policy compliance with NIST security standards. The novelty of this study lies in the introduction of the 2SMatrix framework, which provides a structured and VPN-specific approach to ransomware analysis, distinguishing it from broader threat modeling tools.