Journal of Current Science and Technology

Real-Time Stress Profiling in University Students During Post-COVID-19 Recovery and PM2.5 Exposure Using a Web Application

Mon, 30 Mar 2026 00:00:00 +0700

This study compared stress profiles of Thai university students during post-COVID-19 recovery (2024) and peak PM_2.5 exposure (2025) using the Find My Stress Progressive Web Application (PWA). A cross-sectional design enrolled 613 students (post-COVID-19: n = 303; PM_2.5: n = 310). Participants completed PWA-based assessments including demographic profiling, task-related stressor ratings (0–10 scale), Subjective Workload Index (SWI) computation, and activity-based evaluations across four daily domains. Handgrip strength normalized by BMI (HG/BMI) was measured in the PM_2.5 cohort. Usability was assessed via a 14-item questionnaire (n = 372). Data were analyzed using independent-samples t-tests, Pearson correlations, and stepwise regression (p < .05). The post-COVID-19 cohort exhibited significantly higher SWI (M = 3.09, SD = 0.85) than the PM_2.5 cohort (M = 2.37, SD = 0.99; p < .001, Cohen’s d = 0.78), reflecting elevated psychosocial strain. The PM_2.5 cohort reported greater environmental discomfort (air quality, dust, illumination) and biomechanical burden (adverse posture, restricted movement). Stepwise regression identified six predictors of HG/BMI: time, noise, dust, vibration, organizational factors, and gender (r = 0.674, p < .001). SWI correlated positively with fatigue and task complexity and negatively with motivation and autonomy. The PWA demonstrated excellent reliability (Cronbach’s α = 0.957). The Find My Stress PWA effectively captured context-specific stress patterns: elevated psychosocial workload during post-pandemic recovery and heightened environmental strain under PM_2.5exposure. These findings support the integration of scalable digital ergonomics tools into university health systems for real-time stress monitoring.

Risk Factors for Tuberculosis in Children: A Case-Control Study of Environmental, Nutritional, and Contact-Related Determinants

Maidartati, Tita Puspita Ningrum, Sindy Nabilla, Anggi Saputra — Wed, 25 Mar 2026 00:00:00 +0700

Tuberculosis (TB) remains a major pediatric health concern, particularly in high-burden settings such as Indonesia. Objective: To identify environmental, nutritional, and contact-related risk factors for childhood tuberculosis. A case-control study of 50 TB cases and 50 controls was conducted at Garuda Health Centre, Bandung, Indonesia (August 2024). Data on nutritional status, contact history, parental factors, and housing conditions were collected through structured questionnaires. Statistical analysis included chi-square tests and multivariate logistic regression. Proper ventilation was the strongest protective factor (adjusted odds ratio [OR] = 0.264, p < 0.001), reducing TB risk by 73.6%. Contact history significantly increased risk (adjusted OR = 2.631, p < 0.001), and proper lighting provided substantial protection (adjusted OR = 0.451, p = 0.007). In univariate analysis, undernourished children had five times higher odds of TB (OR = 5.00, 95% confidence interval [CI]: 2.05 − 12.20, p = 0.001), though this effect was attenuated in multivariate models. Socioeconomic factors showed no independent associations. The model demonstrated good discriminatory performance (accuracy = 75%, area under the Receiver Operating Characteristic curve [AUC] = 0.834). Environmental factors, particularly ventilation and lighting, are key modifiable determinants of childhood TB risk. Housing improvements should be prioritised alongside traditional TB prevention strategies in comprehensive control programmes.

Effect of Magnetic Field on Heat Transfer Enhancement and Pressure Drop of Fluid Flow with Magnetic Particle Suspension in a Curled Pipe

Varut Emudom, Monta Singhasani — Wed, 25 Mar 2026 00:00:00 +0700

The characteristics of heat transfer enhancement and pressure drop in the curled pipe under the effect of a magnetic field are presented in this paper. The suspensions, which are composed of g-Fe₂O₃(gamma-phase iron oxide) magnetic particles with a median diameter of 15–20 nm dispersed in plain water have been used. The magnetic particles at different concentrations by volume of 0.50%, 0.75% and 1.00% were used in the pipe flow experiments. The suspension enters the curled pipe at the innermost turn, flows under a uniform surface heat flux, and exits at the outermost turn. To increase the rate of heat transfer, three different strengths of an external magnetic field of 600 Gauss (G), 1,200 G, and 1,800 G were utilized by the electromagnets mounted on plates located at the top and bottom of the curled pipe. The effects of magnetic field strength, concentration by volume of magnetic particles, and curve ratios on the heat transfer enhancement and pressure drop are shown. The results show that the Nusselt number increases with increasing magnetic field strength, particle volume concentration, and curve ratio. The Nusselt number increased by up to 14.34%, 19.19%, and 26.26% for magnetic field strengths of 600 G, 1,200 G, and 1,800 G, respectively.

Recent Advances in Cogging Torque Reduction for Radial-Flux PMSMs: A Systematic and Bibliometric Review

Wed, 25 Mar 2026 00:00:00 +0700

Cogging torque remains a critical barrier to achieving torque smoothness, energy efficiency, and structural reliability in radial-flux permanent magnet synchronous machines (RF-PMSMs), particularly in low-speed and variable-speed renewable energy systems. This study presents a systematic literature review (SLR) of 64 peer-reviewed journal articles, categorizing cogging torque mitigation strategies into five key domains: stator geometry, rotor geometry, magnet geometry, winding layout, and non-geometric techniques. To complement the synthesis, bibliometric mapping was performed using VOSviewer, and a Thematic Map was generated via Biblioshiny (Bibliometrix R) from article metadata. This integrated approach not only identifies widely adopted techniques but also uncovers underexplored yet high-potential research fronts. In particular, niche and emerging themes are highlighted as promising directions for innovation. The review also underscores the importance of multi-objective design optimization, advanced material strategies, and real-world validation under manufacturing and operational constraints. Overall, the study provides a structured and forward-looking contribution to advancing RF-PMSM design, with specific novelty in cogging torque reduction for high-performance and sustainable energy applications.

A Study of Opinion and Satisfaction of Physical Therapists About the Prototype Exercise Machine for Lower Limb Strengthening in Children with Cerebral Palsy

Mon, 30 Mar 2026 00:00:00 +0700

Children with spastic cerebral palsy commonly exhibit muscle spasticity, generalized weakness, and postural instability, with deficits in lower extremity strength markedly impairing their ability to perform functional activities such as standing and walking. In response to this clinical challenge, a prototype exercise machine was developed to enhance lower limb strength. However, the opinions and satisfaction of physical therapists regarding this machine constitute an important aspect that has not yet been systematically evaluated. Therefore, this study aimed to investigate their perspectives and satisfaction with the prototype. Thirty physical therapists were purposively recruited based on their clinical experience in pediatric physical therapy. Data were collected using a validated questionnaire, with an Index of Item-Objective Congruence (IOC) greater than 0.50 and a mean IOC of 0.98. Descriptive statistical analysis was conducted to examine participant demographics and satisfaction levels across various aspects. The mean satisfaction scores were as follows: design and structure (4.35 ± 0.63), safety (4.47 ± 0.57), usability (4.07 ± 0.79), and usefulness (4.27 ± 0.74). In conclusion, the equipment received high satisfaction ratings in all aspects. Further development is recommended in accordance with industry or medical device standards, along with additional studies involving a broader sample that includes both typically developing children and those with cerebral palsy.

Optimization and Characterization of Tiwai (Eleutherine americana L. Merr.) Extract Encapsulation for Potential Bioavailability of Flavonoids

Mon, 30 Mar 2026 00:00:00 +0700

Tiwai (Eleutherine americana L. Merr.) is a traditional medicinal plant from Kalimantan, rich in bioactive compounds, particularly flavonoids, which exhibit strong antioxidant activity and therapeutic benefits. However, flavonoids in free form tend to be unstable, making encapsulation necessary to enhance their stability, bioavailability, and shelf life. This study optimized the encapsulation of Tiwai extract using the spray drying method, with parameters such as the ratio of Tiwai extract to maltodextrin (ET:MD), chitosan concentration, and drying inlet temperature adjusted using Response Surface Methodology (RSM) with Box-Behnken Design (BBD). The study provided detailed results, including an encapsulation efficiency (EE) of 93.24%, loading capacity (LC) of 95.76%, total flavonoid content (TFC) of 69.23 mg QE/g, and antioxidant activity (DPPH) of 60.01 µg/mL. A significant discrepancy was found between the quercetin content determined by HPLC (148.30 µg/g) and the TFC obtained using the AlCl₃ method (69.23 mg QE/g). This difference is attributed to the different principles of the two methods, as the AlCl₃ method reacts with flavonoids and other antioxidant compounds, whereas HPLC specifically identifies quercetin. FTIR analysis confirmed successful encapsulation, showing changes in the functional groups of the encapsulated product. These findings suggest that the optimized Tiwai encapsulation formula has potential applications in functional foods and pharmaceuticals.

A Hybrid Deep Learning and Machine Learning Approach for Predicting Aqueous Solution Concentrations

Rong Phoophuangpairoj, Panida Sampranpiboon — Mon, 30 Mar 2026 00:00:00 +0700

Assessing solution concentration is essential across multiple scientific disciplines; however, it is often complicated by limitations in instrument precision, sample impurities, and environmental variables. Low concentration levels frequently necessitate sophisticated methods such as spectroscopy or chromatography, which require specific apparatus and expertise. Conventional methods might be laborious and occasionally inadequate for accurate measurements. Consequently, researchers continually develop better, more efficient, and economically viable methodologies. Recent technological advancements, including deep learning and machine learning, facilitate the development of efficient, cost-effective systems for determining solution concentration levels, applicable to environmental monitoring and food safety tests. Therefore, this research developed a methodology for estimating solution concentrations through deep learning feature extraction and machine learning-based prediction. Images of the solution at varying concentrations were used to train models that apply deep learning for feature extraction. Linear regression (LR), artificial neural network (ANN), support vector regression (SVR), and random forest (RF) were then evaluated for using the extracted features to forecast the concentrations. Using features extracted from Visual Geometry Group 16-layer Convolutional Neural Network (VGG16) with LR, ANN, SVR, and RF yielded absolute prediction errors of 0.056229, 0.080000, 0.112172, and 0.026640, respectively, for concentration class prediction (classes 1–10). When the concentrations of classes 1 to 10 were evenly changed from 0 ppm to 4500 ppm, using VGG16 to extract features and RF to predict concentrations resulted in an average absolute error of 13.32 ppm, an RMSE of 0.072531 (normalized class scale) and 36.31 ppm (concentration scale), and an R² of 0.999361. The findings indicated that the proposed inexpensive method could efficiently classify the solution in different concentration classes and forecast their concentrations.

AVCSNPs, a novel alternative antibiotic derived from chitosan nanoparticles loaded with Aloe Vera flavonoids

Saba Mejdhab Badr, Rana A.H. Al-Lami, Hanady S. Al‑Shmgani — Mon, 30 Mar 2026 00:00:00 +0700

The treatment of burn and wound infections is becoming more challenging due to the emergence of antibiotic-resistant bacteria. This study investigated the synergistic antibacterial efficacy of green-synthesized chitosan nanoparticles (CSNPs) loaded with Aloe vera gel flavonoid extract (designated AVCSNPs), which were effective against MDR and XDR Staphylococcus aureus isolates. The primary goal was to evaluate the antibacterial efficacy of AVCSNPs compared with flavonoid extract alone. Using AVCSNPs as an alternative to antibiotics for Staphylococcus aureus is a low-toxicity and cost-effective approach. The study also examined how AVCSNPs affected the expression of genes associated with antibiotic resistance, such as mecA and aac(6′)-Ie-aph(2″)-Ia. Clinical samples were obtained from Ghazi Al-Hariri Hospital and Burns Hospital in Baghdad. The MIC of the flavonoid extract was determined. AVCSNPs were characterized by UV-Vis spectroscopy and particle size analysis after biosynthesis. Using log2-fold change analysis, the effects of treatment on gene expression were investigated. AVCSNPs exhibited greater antibacterial activity than the flavonoid extract, with a MIC of 18.75 µg/mL compared with 50 µg/mL. Studying genes affected by AVCSNPs was essential for understanding antibiotic resistance. Treatment with AVCSNPs significantly reduced the expression of the mecA gene, with a mean log2-fold decrease of -14.64. This notable decline indicates that nanoparticles may circumvent the primary resistance mechanism in MRSA bacteria. However, although the decline was less pronounced (-3.37), the expression of the aac(6′)-Ie-aph(2″)-Ia gene also declined. Due to their potent and targeted action on the mecA gene, AVCSNPs may be a viable and biocompatible alternative to conventional antibiotics for the treatment of MRSA infections.

Optimization of Extraction Parameters and Functional Evaluation of Samia ricini Pupae Protein for Starch-Based Bioplastic Films

Palida Tanganurat, Nunchanok Nanthachai, Intira Lichanporn, Pradit Kumnongphai — Mon, 30 Mar 2026 00:00:00 +0700

The escalating global demand for sustainable protein sources and eco-friendly packaging necessitates the valorization of underutilized agricultural by-products. This study systematically optimized the processing of Samia ricini (Eri) silkworm pupae, a high-quality sericulture by-product, to maximize protein isolation and evaluate its application in biocomposite edible films. A three-stage optimization process was implemented: 1) steaming pretreatment, 2) ethanol defatting, and 3) alkaline protein extraction. The optimal parameters identified were 6-8 minutes of steaming (for lowest moisture and the highest initial protein content), a 16-hour ethanol defatting duration (achieving 64.19% protein content post-defatting), and a brief 30-minute alkaline extraction (yielding a high-purity protein isolate of 94.94%). The resulting optimal protein isolate was then combined with different starch sources (corn, tapioca, and blend) to produce edible films. Protein incorporation significantly enhanced the film's functional properties, notably reducing the water vapor permeability (WVP) across all formulations (p ≤ 0.05). The protein–corn–tapioca starch blend demonstrated superior barrier performance with the lowest WVP value of 2.49 ± 0.10 g/h⋅m². Conversely, while the incorporation of protein and different starches did not result in statistically distinct tensile strength values (p > 0.05), films made with corn starch exhibited the best qualitative handling properties (uniformity and peelability). These findings demonstrate the potential of Samia ricini pupae protein as a bio-derived functional ingredient for developing high-performance, sustainable bioplastic films and supporting the circular utilization of sericulture waste resources.

Artificial Artificial Neural Network–Genetic Algorithm Integrated Approach for Optimizing Residual Stress and Crystallite Size in Incremental Forming of Ti–6Al–4V Alloy

Apisit Keawchaloon, Thanatep Phatungthane, Suriya Prasomthong, Chaiya Chomchalao — Mon, 30 Mar 2026 00:00:00 +0700

This study develops an integrated Artificial Neural Network–Genetic Algorithm (ANN–GA) approach to optimize process parameters in incremental sheet forming (ISF) of Ti–6Al–4V alloy, aiming to minimize residual stress (RS) and maximize crystallite size (D) to improve product quality. Three parameters tool radius (R), incremental step depth (S), and feed rate (F) were arranged using a Taguchi L9 orthogonal array. An ANN model (3–5–2 architecture), trained with the Levenberg–Marquardt algorithm, predicted RS and D, while GA was employed to determine optimal parameter combinations for simultaneous multi-response optimization. Experimental results showed RS between −157.11 MPa and −86.99 MPa and D from 19.67 to 21.87 nm. The ANN–GA method achieved superior prediction accuracy. The ANN model achieved a training RMSE of 0.0301 MPa for RS and 0.1394 nm for D, whereas validation RMSE values were 1.842 MPa and 0.229 nm, respectively, confirming good generalization performance. The optimal settings (R = 8.725 mm, S = 0.2588 mm, F = 1 mm·min⁻¹) reduced the magnitude of residual stress by 9.18% and increased D by 5.27% compared with the best Taguchi results. This integrated framework enhances process reliability, enables precise control of surface integrity, and provides practical guidelines for manufacturing high-performance titanium components for aerospace and biomedical applications.

Computational Modeling of Laser Ablation Therapy for Cervical Intraepithelial Neoplasia: Optimization of Bioheat Transfer

Mon, 30 Mar 2026 00:00:00 +0700

Cervical intraepithelial neoplasia (CIN) is a common precancerous condition that is treatable with laser therapy. This study presents a computational thermal analysis of CIN tissue under laser ablation, focusing on CIN1, CIN2, and CIN3 stages. Using computational fluid dynamics (CFD) and high-resolution meshing, thermal responses were evaluated under laser power settings of 20 W/cm² to 50 W/cm². Mesh complexity increased with lesion severity: CIN1 included 683 vertices and 172 triangular elements (average quality 0.9147), CIN2 had 707 vertices and 241 elements (average quality 0.9226), and CIN3 used 7,181 vertices and 14,023 elements (average quality 0.945). Thermal analysis showed that CIN1 reached 38.28 °C at 20 W/m² and 39.49 °C at 50 W/m², with heating rates of 0.0024 °C/s and 0.0083 °C/s, respectively. CIN2 peaked at 39.71 °C and 44.51 °C with heating rates of 0.0090 °C/s and 0.0250 °C/s while CIN3 reached 43.91 °C and 55.07 °C, with heating rates of 0.0230 °C/s and 0.0602 °C/s, respectively. The results indicate that higher power settings lead to more aggressive thermal gradients and faster heating, particularly in advanced CIN stages. These findings emphasize the importance of power modulation in simulating ablation outcomes.

Rainfall Variability Analysis Using Rolling Statistics in Chiang Rai Province

Wed, 25 Mar 2026 00:00:00 +0700

This study analyzed long-term rainfall variability in Chiang Rai Province (1981–2024) using rolling statistics and the non-parametric Mann–Kendall test to detect temporal changes in both the mean and variability of annual rainfall. Annual rainfall data from five meteorological locations were examined using 3-, 5-, 7-, and 12-year moving windows to characterize short-, medium-, and decadal-scale fluctuations. Although previous studies in northern Thailand have examined trends in total or extreme rainfall, multi-scale variability has not been systematically assessed, leaving uncertainty about how rainfall behavior is changing across different temporal windows. Results indicate that mean annual rainfall remains statistically stable (p > 0.05) at most locations, except for Wiang Pa Pao, which shows a significant upward trend (Z = 3.79–5.35). In contrast, the rolling standard deviation increased consistently across all locations, suggesting intensifying interannual variability. These findings indicate that rainfall in northern Thailand has become more unpredictable, with larger departures from the mean despite relatively stable long-term averages. The results point to practical needs for updating design-rainfall criteria and integrating variability-based assessments into regional water-resource and climate-adaptation planning.

Simulation-Based Evaluation of Reinforcement Learning-Enhanced Location-Aware Routing in Urban Vehicular Ad-hoc Networks (VANETs)

Arvind Kumar, Shobha Tyagi, Prashant Dixit, S.S. Tyagi — Wed, 25 Mar 2026 00:00:00 +0700

Vehicular Ad Hoc Networks (VANETs) need robust routing protocols to ensure rapid and reliable data transmission in urban environments characterized by high mobility and highly dynamic topologies. Traditional routing protocols lead to excessive routing overhead, increased hop counts, prolonged end-to-end delays, and reduced packet delivery ratios (PDR), which collectively hinder reliable and efficient data dissemination within intelligent transportation systems (ITS). This research presents a simulation-based evaluation of a Reinforcement Learning (RL)-enhanced Location-Aware Routing (LAR) protocol. By integrating RL with the traditional LAR protocol, the proposed framework dynamically adapts to network fluctuations, thereby minimizing routing overhead, hop counts, and end-to-end delay. Compared against classical routing protocols such as AODV, DSR, and LAR across sparse (50 vehicles), moderate (150), and dense (300) urban traffic scenarios using NS-3 and SUMO, RL-LAR demonstrates superior performance. Improvements ranging from 3% to 12% were observed in PDR, while average end-to-end delay was reduced by 9.7% to 13.8%. Additionally, routing overhead decreased by 4.3% to 8.7%, hop counts were reduced by 15% to 23% and throughput increased by 15% to 31% relative to baseline protocols. These gains were also validated by ANOVA (p < 0.01) and found to be suitable for routing in smart cities for future intelligent transportation systems.

Detection of Estrus in Dairy Cows: A Proof-of-Concept Near-Infrared Milk Sensing and Machine-Learning Study

Wed, 25 Mar 2026 00:00:00 +0700

Accurate estrus detection is critical for reproductive management in dairy herds, yet current methods are either labor-intensive or require dedicated hardware. This proof-of-concept study investigated whether near-infrared (NIR) milk spectra, combined with routine milk-composition data, can be used to detect estrus in dairy cows. Five clinically healthy Thai milking cows were monitored for 21 days each (total 593 milk samples), with estrus labels assigned based on experienced stockperson observations confirmed by behavioral signs and tail-paint rubbing. For each sample, inline NIR transmission spectra (860–1754 nm) and milk composition (fat, protein, lactose, solids-not-fat, density, pH, daily yield) were acquired. During estrus, milk composition showed modest but consistent shifts: protein increased by approximately 0.29 percentage points and lactose by 0.41 percentage points, while daily milk yield decreased by about 7.4 ± 2.7 kg/day relative to non-estrus days. A leakage-aware, leave-one-cow-out cross-validation framework was used to compare six classifiers. Logistic regression, gradient boosting, and decision-tree-based ensembles achieved internal accuracies of between 0.99 and 1.00, with Extra Trees and random forest yielding F1-scores of 0.99 and 0.92, respectively. Feature-importance analysis indicated that specific NIR bands in the water and protein-related regions, together with milk yield and protein percentage, contributed most strongly to estrus discrimination, whereas density and pH had minimal influence. These proof-of-concept results (N = 5, single site) demonstrate technical feasibility but require multi-site validation in substantially larger cohorts (N ≥ 50) before any clinical or on-farm adoption can be recommended.

Process Indicators of 2018 WHO Cervical Cancer Elimination in Personnel of a Medical School and Their Daughters

Benya Pattarakiatjaroen, Bandit Chumworathayi, Saisamon Leeladapattarakul — Mon, 30 Mar 2026 00:00:00 +0700

This study aimed to evaluate progress toward the WHO's 2018 cervical cancer elimination targets (90-70-90: % vaccinated, % screened, and % treated) among medical school personnel. This prospective descriptive-analytic study enrolled female medical school personnel aged 20-65 who participated in annual health examinations from March to December 2024. We collected Human Papillomavirus (HPV) vaccination uptake among participants' daughters aged 11−20, cervical cancer screening uptake, and further management data for participants who received abnormal results. Main outcomes were benchmarked against the WHO elimination targets. Among a total of 4,127 female medical school personnel aged 20-65, 3,034 came for the 2024 health check, but only 1,185 participated in cervical screening, and 669 gave their informed consent. Thirteen of them were further excluded because of a previous total hysterectomy, leaving only 656 for analysis. The HPV vaccination rate among the participants' daughters (n = 125) reached only 45.6%, which was significantly below the 90% target. Age-stratified cervical screening rates were 65.36% in women < 45 years (n = 393), and 75.09% in women ≥ 45 years (n = 263)—only those aged ≥ 45 years achieved the 70% target. Sixty-four participants (9.94%) tested positive for HPV. Further management compliance for the HPV-positive cases (n=64) was as high as 98.4%, exceeding the 90% target. Despite high treatment compliance, critical gaps persist in their daughters’ vaccination and screening uptake among medical school personnel. Institution-specific interventions addressing accessibility and workflow optimization are essential to achieve the WHO targets. Such improvements would demonstrate that medical school personnel can serve as a model for community-wide cervical cancer elimination efforts.

Performance of CRYSTALS-Kyber on Raspberry Pi 5 under Embedded-System Constraints: A Comparison with RSA and EC-KEM

Nattapon Junlachaiworakun, Nirundon Panit, Kritsanapong Somsuk — Wed, 25 Mar 2026 00:00:00 +0700

The emergence of quantum computers presents a significant risk to public key cryptography algorithms, including RSA and elliptic curve cryptography (ECC). CRYSTALS-Kyber, also called Kyber, was standardized by NIST as the Module-Lattice-based-Key Encapsulation Mechanism (ML-KEM, FIPS 203) to address quantum threats. However, its performance on modern embedded ARM platforms remains uncharacterized. The aim of this study is to benchmark the Kyber family, including Kyber-512, Kyber-768, and Kyber-1024, on the Raspberry Pi 5, which is selected to represent embedded-system constraints. In addition, RSA and EC-KEM were chosen as baseline comparisons. Execution time, memory consumption (RSS), and key/ciphertext sizes were measured over 1,000 iterations using custom Python scripts (liboqs 0.14.0 and the Python cryptography library) on 64-bit Ubuntu Linux under controlled parallel execution. The results show that Kyber achieves sub-millisecond execution times (0.05-0.21 ms) for key generation, encapsulation, and decapsulation with low variability (SD < 0.02 ms). Nevertheless, RSA-2048 requires 250-264 ms per operation, while EC-KEM ranges from 0.29 ms for X25519 to 3.04 ms for secp521r1. Memory consumption is comparable across all algorithms (24-25 MB RSS). Kyber’s larger keys and ciphertexts (800-1568 bytes vs. 32-294 bytes for RSA/EC-KEM) present a latency-bandwidth trade-off for embedded deployments. Therefore, Kyber is computationally viable for latency-sensitive operations on modern ARM embedded systems. The evaluation focuses on discrete cryptographic operations, protocol-level integration and energy measurements are deferred to future work.

Efficacy of Topical Hydroquinone 4% with or without Intradermal Autologous Platelet-rich Plasma in Lichen Planus Pigmentosus: A Split-face Randomized Controlled Trial

Polpatt Jitpakdee, Chanisa Kiatsurayanon — Wed, 25 Mar 2026 00:00:00 +0700

Lichen planus pigmentosus (LPP) is a chronic acquired hyperpigmentation disorder predominantly affecting sun-exposed areas of the face and neck, for which effective treatment remains challenging. Platelet-rich plasma (PRP) contains growth factors and bioactive mediators, including transforming growth factor-β1 (TGF-β1) and macromolecular activators of phagocytosis, which may modulate melanocyte activity, inflammation, and dermal pigment clearance. This study evaluated whether intradermal autologous PRP provides additive benefit when combined with topical 4% hydroquinone (HQ) in patients with LPP.

In this randomized, single-blinded, split-face controlled trial, 15 patients received intradermal PRP injections on one randomly assigned side of the face at baseline, week 2, and week 4, while both sides received nightly topical 4% HQ throughout the 20-week study period. Outcomes included the melanin index measured by Mexameter®, the hemi-modified acquired macular hyperpigmentation and severity index (DPASI), patient satisfaction, and adverse events, assessed through week 20.

Both treatment arms showed significant reductions in melanin index from baseline to week 20. Earlier reductions were observed on the PRP-treated side, particularly among patients with mild disease and those naïve to PRP. These findings are consistent with PRP-mediated modulation of melanocyte activity and inflammatory pathways. However, no statistically significant between-side differences were detected at any time point, and effect sizes were small. DPASI scores improved modestly in both groups without significant intergroup differences, while patient satisfaction favored the PRP-treated side. Adverse effects were mild and transient.

In conclusion, PRP may exert early localized biological effects on pigmentation through growth factor-mediated mechanisms but did not confer a sustained or clinically dominant advantage over HQ monotherapy. PRP appears to be best positioned as a safe adjunctive therapy in selected patients with LPP.

An IoT-Enabled Cyber-Physical System Architecture with Adaptive Control: A Case Study in Household Bio-Fermentation

Laddawan Champa, Nanthawan Hadthamard, Natthaphong Thongpan — Wed, 25 Mar 2026 00:00:00 +0700

The emergence of the Internet of Things (IoT) in Cyber-Physical Systems (CPS) has advanced real-time monitoring in smart agriculture; however, a critical gap exists in household bio-fermentation, where existing IoT-based systems lack adaptive mechanisms to manage the energy–stability trade-off under resource constraints. This study addresses this limitation by developing a three-layer IoT-enabled CPS architecture integrated with an optimization-guided adaptive scheduling algorithm that minimizes energy consumption while maintaining process stability above a γ threshold. Five 30-L fermenters were tested over 14 days under different headspace conditions using pH, temperature, and electrical conductivity sensors to evaluate physicochemical, microbiological, and reliability responses. The adaptive scheduling model reduced fermentation time by 30% while maintaining system availability above 95%, and the HS50 headspace condition yielded the most stable process behavior and the highest nutrient quality, meeting national organic fertilizer standards. The novelty lies in adapting optimization-based scheduling to resource-constrained household bio-fermentation and validating it against biological outcomes, thereby linking CPS reliability indicators with physicochemical and microbial performance. This work contributes theoretical insight into CPS optimization and offers a practical, scalable approach for sustainable smart agriculture.

Analysis of Imputation Methods for Missing Not at Random (MNAR) Data: A Comparative Study of Air Pollution Data in Bangkok, Thailand

Sattawat Saeyang, Khairil Anwar Notodiputro, Wandee Wanishsakpong — Wed, 25 Mar 2026 00:00:00 +0700

Environmental datasets are often large in scale and frequently contain missing observations due to interruptions. Addressing these missing values is essential for ensuring the reliability of subsequent analyses. In many practical cases, missingness depends on the unobserved values or the technical issues themselves. This missing status is called Missing Not at Random (MNAR), which remains one of the most challenging missing data mechanisms. This study investigates the MNAR pattern in an air pollution dataset from Bangkok, Thailand. A simulation framework used the variable as the target variable for random missing with MNAR patterns at varying rates. The methods used for missing-value imputation were K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations (MICE), and the Expectation Maximization (EM) algorithm. Imputation accuracy was evaluated using Root Mean Square Error (RMSE). Furthermore, imputation efficiency was tested by conducting ARIMA, LSTM, and RF forecasting models, and model performance was evaluated using Mean Absolute Percentage Error (MAPE). The simulation results showed that KNN consistently achieved the lowest RMSE (1.66-2.68) for missing-value imputation at 10%-70% missing rates. In addition, KNN-based models showed better performance in ARIMA, and the LSTM models achieved the lowest MAPE (2.31–2.46) across all missing rates, while EM-based models excel at the RF model better than KNN. When applied to the actual air pollution dataset, KNN-based models also performed most effectively for variables containing MNAR. However, for other missingness types, MICE- and EM-based models outperformed KNN-based models. Overall, this study highlights practical and efficient approaches for handling MNAR missingness in environmental datasets and provides insights that can help future researchers better recognize, manage, and mitigate MNAR-related issues in real-world data collection.