Ubon Ratchathani University

To compare pharmacist-led telepharmacy with standard pharmaceutical care on stroke and medication knowledge, medication adherence, and clinical outcomes (blood pressure [BP], low-density lipoprotein cholesterol [LDL-C], and glycated hemoglobin [HbA1c]) in patients with post-ischemic stroke; and to characterize medication-related problems (MRPs) and their severity in the telepharmacy arm. In a randomized controlled trial, 126 hospitalized adults with ischemic stroke were assigned 1:1 to receive either standard pharmaceutical care alone or standard care plus structured telepharmacy follow-ups at 1 week, 1, and 2 months post-discharge (n = 63/arm). Outcomes were assessed using validated tools, including knowledge questionnaires; the Medication Adherence Scale for Thais (MAST); the Pharmaceutical Care Network Europe (PCNE); classification v9.1; and the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) severity index. Compared with controls, telepharmacy improved post-discharge knowledge (11.75 ± 2.38 vs 9.79 ± 2.43; P < .001) and adherence (MAST 37.00 ± 3.45 vs 33.26 ± 3.76; P < .001). BP target attainment (<130/80 mmHg) was higher with telepharmacy (52.4% vs 30.2%; P = .011). Follow-up BP favored telepharmacy: systolic Δ -3.67 mmHg (95% CI -6.79 to -0.55; P = .041) and diastolic Δ -3.11 mmHg (95% CI -5.99 to -0.23; P = .040). Between-group differences were not significant for LDL-C or HbA1c. In the telepharmacy arm, 76 MRPs were identified; the most frequent problem was unnecessary drug treatment (38.16%), commonly due to patients' decision to use unnecessary drugs (28.93%). Patient counseling was the commonest intervention (40.00%); 83.64% were fully implemented and 92.10% of MRPs were completely resolved. An ~36.84% of MRPs were NCC MERP level E. Telepharmacy improved knowledge, medication adherence, and BP control after ischemic stroke, and enabled effective identification and resolution of MRPs. These findings support incorporating pharmacist-led telepharmacy into post-discharge transitional care pathways. The trial was retrospectively registered (TCTR20250401004). https://www.thaiclinicaltrials.org/show/TCTR20250401004.

Molecular docking and virtual screening are computational methods for drug discovery that can help predict the interaction between ligands and targets at high speed. This chapter describes recent developments with a particular focus on artificial intelligence (AI) integration, experimental validation, and applications: cancers, neurodegenerative diseases, and cardiovascular diseases. The presented cases demonstrate that hybrid workflows, consensus scoring, and AI-driven library design can generate targets with potential clinical relevance while simultaneously lowering time-related costs and driving molecular docking toward scalable containerized cloud discovery. Finally, the chapter discusses viewpoints on future hurdles and scalability in addition to the democratization of docking with cloud-based and automated platforms.

Drying is one of the most energy-intensive industrial processes, often resulting in excessive energy consumption and inconsistent product quality. This study experimentally investigated a photovoltaic–thermal (PVT) porous solar dryer, designed to overcome these limitations by enhancing heat utilization and improving drying performance. The system integrates a PVT panel, a double-pass solar collector, and porous materials to enhance thermal and drying performance. Experiments were conducted under humid tropical conditions to dry kaffir lime leaves at air velocities of 0.07, 0.09, and 0.10 m/s using porous materials with a porosity of 0.98. Results showed that applying porous materials markedly improved drying performance. The drying rate increased by 39–44%, while specific energy consumption (SEC) decreased by 28–30% compared to without porous. The drying process mainly occurred in the falling-rate period, governed by internal diffusion. Statistical analyses using two-way ANOVA and Tukey’s HSD confirmed that both air velocity (p &lt; 0.05) and porous material (p &lt; 0.01) significantly affected performance, with the porous material being the dominant factor. The observed enhancements resulted from increased turbulence, an expanded heat-transfer surface, and improved thermal retention, leading to stable drying temperatures and efficient convective heat exchange. The PVT-porous dryer maintained high efficiency even under low airflow conditions, demonstrating reduced sensitivity to air velocity. Porous materials were thus identified as crucial design elements for optimizing drying efficiency and supporting the utilization of renewable energy. This study provides valuable insights into sustainable agricultural drying, contributing to enhanced energy efficiency and reduced greenhouse gas emissions under tropical climatic conditions

Radiographic imaging remains a cornerstone of diagnostic practice. However, accurate interpretation faces challenges from subtle visual signatures, anatomical variability, and inter-observer inconsistency. Conventional deep learning approaches, such as convolutional neural networks and vision transformers, deliver strong predictive performance but often lack anatomical grounding and interpretability, limiting their trustworthiness in imaging applications. To address these challenges, we present SpineNeuroSym, a neuro-geometric imaging framework that unifies geometry-aware learning and symbolic reasoning for explainable medical image analysis. The framework integrates weakly supervised keypoint and region-of-interest discovery, a dual-stream graph-transformer backbone, and a Differentiable Radiographic Geometry Module (dRGM) that computes clinically relevant indices (e.g., slip ratio, disc asymmetry, sacroiliac spacing, and curvature measures). A Neuro-Symbolic Constraint Layer (NSCL) enforces monotonic logic in image-derived predictions, while a Counterfactual Geometry Diffusion (CGD) module generates rare imaging phenotypes and provides diagnostic auditing through counterfactual validation. Evaluated on a comprehensive dataset of 1613 spinal radiographs from Sunpasitthiprasong Hospital encompassing six diagnostic categories-spondylolisthesis (n = 496), infection (n = 322), spondyloarthropathy (n = 275), normal cervical (n = 192), normal thoracic (n = 70), and normal lumbar spine (n = 258)-SpineNeuroSym achieved 89.4% classification accuracy, a macro-F1 of 0.872, and an AUROC of 0.941, outperforming eight state-of-the-art imaging baselines. These results highlight how integrating neuro-geometric modeling, symbolic constraints, and counterfactual validation advances explainable, trustworthy, and reproducible medical imaging AI, establishing a pathway toward transparent image analysis systems.

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control.

Prediction rules for computed tomography (CT) use in mild traumatic brain injury (MTBI) often have limited generalizability due to restrictive inclusion criteria and development in high-income countries. A clinical-based prediction score was recently developed for a broader MTBI population in a low- to middle-income country. This study aims to externally validate this score in a new patient dataset. We prospectively collected data from adult patients with MTBI (Glasgow Coma Scale score 13-15) presenting to a hospital in Thailand between 2014 and 2016. The performance of the 13-predictor clinical-based score was evaluated for two outcomes: traumatic intracranial findings on CT and need for neurosurgical intervention. We assessed discrimination using the area under the receiver operating characteristic curve (AuROC) and diagnostic accuracy at predetermined cut-points. The study included 565 patients. Seventy-two patients (12.7%) had a positive CT scan, and 8 (1.4%) required neurosurgical intervention. The AuROC was 0.70 for predicting a positive CT scan and 0.78 for predicting neurosurgical intervention. At a cut-point score of ≥2, the sensitivity for a positive CT was 97.2% with a specificity of 12.6%. At a cut-point score of ≥3, the sensitivity for neurosurgical intervention was 100% with a specificity of 21.0%. The clinical prediction score demonstrated acceptable and safe diagnostic performance in an external validation cohort, maintaining high sensitivity for critical outcomes. The score may be a useful tool to help guide management policy for MTBI patients, particularly in low- to middle-income countries.

This study aimed to investigate the validity and reliability of the instruments to measure parental involvement, students’ motivation, self-efficacy, and English learning achievement among primary EFL students in Thailand. Adapted questionnaires were administered to 306 student–parent dyads. A two-step approach to the analysis was adopted, utilising Exploratory Factor Analysis (EFA) to extract the factorial structure in the first step and then Confirmatory Factor Analysis (CFA) to verify the measurement model in the second step. Results of the EFA supported a multidimensional structure within the constructs, and all retained items had loadings above 0.50, demonstrating theoretical coherence. CFA also endorsed the proposed model as fitting the data very well (χ²/df = 1.521, CFI = .962, TLI = .955, RMSEA = .042, SRMR = .80, AVE &gt; .50), supporting its general validity. Discriminant validity was verified through correlations among parental constructs, which demonstrated their distinctiveness (parental involvement, motivation, and self-efficacy). The results indicate that the adapted tools are both psychometrically sound and culturally relevant when used with Thai young learners. The validated measurement model of the study provides a reliable methodological underpinning for future SEM studies and contributes to the development of proper diagnostic tools for educators and policymakers to evaluate and improve parental engagement and learner motivation in English language learning.