The design of peptide sequences to perform anti-HIV activities is a particularly time-consuming stage in the production of AIDS medications. One way to solve this issue is by computer modeling. Predictive models for anti-HIV peptides help decrease the time and cost of producing anti-HIV peptide drugs. This paper introduces the C400KNN model, developed by a machine learning approach that uses non-antimicrobial peptides as the negative dataset and incorporates a feature selection procedure of 400 variables using the chi-square test, followed by training with the KNN algorithm. The amino acid sequences were gathered from databases containing anti-HIV, antiviral, and antimicrobial information. Next, all features were extracted from 12 descriptors, encompassing both the chemical structure and physicochemical properties. Ten classifiers were used to create the models. The models were evaluated to discover descriptors using AUC and ACC. Then, the features of those descriptors were combined, and feature selection methods were used to choose the most important features. The best model was selected based on its performance. The findings indicate that the descriptor groups that improve model efficiency are AAC, DPC, PAAC, and APAAC. The Chi-square method was used in the feature selection step. Additionally, the accuracy of the model was found to be 0.83. The C400KNN model is thought to be effective and might help researchers in creating anti-HIV peptides for use in pharmaceutical manufacturing.
The low efficiency of electro-Fenton (EF) technology in generating hydrogen peroxide (H2O2) is one of the most frequently encountered obstacles. Thus, the search for high-performance electrodes and reactors is necessary to increase system efficiency. In this study, porous graphite served as the control group to increase oxygen mass transfer in the reactor, while foam alloy was used as the cathode for producing in situ H2O2. X-Ray diffraction analyzer (XRD) and scanning electron microscope (SEM) were utilized to investigate the microstructure of the electrodes. Response surface methodology (RSM) was used to examine the various operational parameters affecting the reduction of methylene blue (MB) dye. A FeSO4.7H2O concentration of 0.5 mM, a current density of 8 mA/cm2, and a reaction time of 30 min were the optimal conditions for the electro-Fenton technique. Under these conditions, the MB removal efficiency (RE%) was 97.73%, and the energy consumption was 11.76 kWh/kg MB. The two most important factors controlling dye reduction in the electro-Fenton process are the FeSO4.7H2O concentration and the current density, which contributed 68.33% and 15.44%, respectively, in this model. The coefficient of multiple correlation (R2) was 99.59%, which demonstrated the statistical significance of the regression analysis. Therefore, the foam alloy electrode represents a new and viable strategy for contaminant degradation in the electro-Fenton process.
Efficient planning of large-scale agricultural transportation requires balancing travel distance, fleet utilization, and factory capacity constraints. While mixed-integer linear programming (MILP) becomes computationally intractable for large-scale instances and conventional metaheuristics rely on static operator-selection mechanisms, adaptive learning-guided approaches for multi-objective capacitated transportation remain limited. This study proposes a reinforcement learning–guided hybrid NSGA-II + ALNS framework to minimize total transportation distance and truck trips in sugarcane logistics. A real-world case involving 199 subdistricts and four processing plants (796 origin–destination pairs) in northeastern Thailand is examined. Compared with a greedy nearest-assignment baseline, the proposed method reduces total transportation distance from 123,313.52 km to 109,245.22 km (by 11.41%), fuel consumption from 28,131.83 L to 25,108.96 L (by 10.75%), and CO₂ emissions from 75,955.94 kg to 67,794.20 kg (by 10.75%), resulting in an estimated fuel cost saving of approximately 96,550 Thai Baht per cycle. Statistical validation using ANOVA and Tukey’s HSD confirms that performance differences are significant at the 95% confidence level. The results demonstrate that reinforcement learning–guided operator adaptation improves convergence stability, Pareto-front quality, and environmental performance in large-scale bi-objective agricultural transportation systems.
This study compared stress profiles of Thai university students during post-COVID-19 recovery (2024) and peak PM2.5 exposure (2025) using the Find My Stress Progressive Web Application (PWA). A cross-sectional design enrolled 613 students (post-COVID-19: n = 303; PM2.5: 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 PM2.5 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 PM2.5 cohort (M = 2.37, SD = 0.99; p < .001, Cohen’s d = 0.78), reflecting elevated psychosocial strain. The PM2.5 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 PM2.5 exposure. These findings support the integration of scalable digital ergonomics tools into university health systems for real-time stress monitoring.
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.
Family violence among young people remains a significant public health concern, yet primary care screening remains inconsistent in Thailand. To understand which specific types of violence most strongly predict adolescent stress and why these problems remain hidden during healthcare visits, we enrolled 350 young people aged 13 – 24 years from Pathum Thani Province between February and April 2025. Participants completed surveys measuring six types of violence, psychological stress using the ST-5 instrument, and family relationship quality. The 138 participants scoring ST-5 ≥ 8 subsequently completed in-depth interviews. Associations were analyzed using Pearson correlations and stepwise regression, and interview transcripts were then coded thematically. Of the 365 young people approached, 350 participated, yielding a response rate of 95.9%. Violence exposure affected 46% of participants, while 39.5% demonstrated high-to-severe stress levels. Stepwise regression revealed that witnessing parental arguments was the only violence-related predictor independently associated with stress, accounting for 23.6% of the variance (β = .486, p < .001). Shared family activities provided modest protection (β = -.127, p = .007), increasing the total explained variance to 25.2%. Paradoxically, 57.1% of violence-exposed participants described their family relationships positively overall, reflecting the coexistence of violence and support within Thai family systems. Based on these findings, brief ST-5 screening combined with culturally adapted indirect inquiry about parental conflict may represent a promising direction for improving the detection in primary care settings, although prospective evaluation is required. Clinicians must recognize that violence and support can coexist within Thai families, requiring interventions that validate young people’s distress while preserving essential family bonds.
This study proposes a compact and high-performance waste-heat recovery system designed for small food-service establishments using biomass cookstoves (BCS). The system incorporates elliptical-tube helical coil heat exchangers (HHEs) to intensify secondary flows and enhance convective heat transfer compared with that of conventional circular-tube designs. Four geometric configurations (H01–H04), differing in coil diameters, pitch lengths, and tube ovalities, were evaluated through an integrated framework consisting of: (i) CFD-based thermal assessment under wall-temperature conditions of 160°C, 180°C, and 200°C; (ii) techno-economic analysis, including annual energy cost savings, net present value (NPV), and payback period; and (iii) environmental impact assessment focusing on CO₂ emission reduction. Heat-transfer oil was used as the working fluid at a constant inlet temperature of 30°C. The CFD results confirm the strong influence of coil geometry on heat-transfer enhancement. H01 consistently demonstrates the highest thermal performance, outperforming H02, H03, and H04 by 2.78%, 4.10%, and 12.83%, respectively. Deploying H01 as both the exhaust-gas ( ) and hot-water ( ) recovery units under optimal conditions (200°C and ṁ = 2.5 L/min) yields 17,144.34 kWh/year of recoverable thermal energy, equivalent to 71,422.27 THB in electricity savings. Techno-economic indicators further reveal strong feasibility, with an NPV of 263,887.97 THB and a payback period of only 1.36 years. CO₂ emission reduction reaches 3,948.17 kg/year, highlighting the system’s environmental significance. Overall, the elliptical-tube HHE configuration (H01) offers superior energy, economic, and environmental benefits for BCS-based food enterprises, aligning effectively with renewable-energy goals and contributing to more sustainable thermal practices in the food-service sector.
This systematic review and meta-analysis evaluated the efficacy of injectable collagen biostimulators for facial aesthetic enhancement. The primary outcome was the Global Aesthetic Improvement Scale (GAIS), with adverse events assessed as a secondary outcome. A comprehensive literature search was conducted across five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL) following the PRISMA 2020 guidelines. Eligible studies included healthy adults receiving facial injections of poly-L-lactic acid (PLLA), polydioxanone (PDO), calcium hydroxylapatite (CaHA), polycaprolactone (PCL), or poly-D,L-lactic acid (PDLLA); however, no eligible PDLLA studies were identified. The included studies consisted of randomized controlled trials and observational studies. Data were synthesized using a random-effects meta-analysis model, and the risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Twenty-four studies were included in the quantitative synthesis. The pooled mean GAIS score was 3.88 (95% CI: 3.63–4.14), indicating moderate to marked aesthetic improvement. Subgroup analysis revealed the highest GAIS scores with PDO (4.20), followed by PLLA (4.13, single study), PCL (4.00), and CaHA (3.33). Reported adverse events were mostly mild and transient, including erythema, swelling, and tenderness. Based on the GRADE assessment, the overall certainty of evidence was low for efficacy and very low for adverse events. Collagen biostimulators demonstrated favorable efficacy profiles for facial rejuvenation. Although all four agents were effective, outcomes varied by product type, suggesting the importance of personalized treatment selection. These findings provide evidence-based support for clinical application and highlight the need for future comparative studies with standardized outcome reporting.
This study analyzes the impact of modeling simplifications on the dynamics and control performance of the Furuta pendulum. A complete multibond graph model is developed, incorporating full system dynamics, and providing an energetically consistent and modular framework. The model is validated against a Simulink-Simscape reference, achieving a maximum normalized root mean squared error (NRMSE) of 0.2035 × 10–3. The commonly used simplified model, which neglects secondary inertias, is compared with full-dynamics models under open- and closed-loop conditions. Open-loop results show appreciable discrepancies, with NRMSE values increasing notably for both θ1 and θ2 as the inertias are progressively increased. A nonlinear control scheme based on energy shaping, collocated partial feedback linearization, and linear quadratic regulation (LQR) is designed using the simplified model. In closed loop, the controller achieves swing-up and stabilization in all cases within 10 seconds. For nominal and moderately increased inertias, performance degradation is minimal, with settling times around 7.4 s and control effort between 0.453 and 0.475 N2m2s. However, for large inertias, the settling time increases to 8.91 s, control effort rises to 0.655 N2m2s, and an additional oscillation is required. These results show that simplified models are suitable for control design under typical conditions, while full-dynamics models are essential for validation and robustness assessment.
In this research, sustainable production of 5-hydroxymethylfurfural (5-HMF) from macroalgae Ulva lactuca (MUL) was methodically studied in a closed system in the presence of a deep eutectic solvent (DES). Herein, the DES consisted of choline chloride (ChCl) and hydrochloric acid (HCl) in acetonitrile solvent, which could be practically applied in relevant reactions such as hydrolysis, isomerization, and dehydration. The roles and benefits of the DES and the types of organic solvents utilized in the reactions were described in preliminary detail. Polymeric humins, as undesirable by-products, were favorably formed via further condensation and/or polymerization of 5-HMF when excessive amounts of HCl or ChCl were utilized under harsh conditions in the catalytic system. Important parameters, such as HCl amount, ChCl amount, reaction time, and temperature, were scrupulously investigated to determine the optimum conditions for 5-HMF production. As anticipated, a maximum yield of 5-HMF (93.9%) was achieved at 120°C for 90 min using 2.4 mmol of HCl and 28 mmol of ChCl. The long-term reusability of ChCl in the catalytic system was also tested under optimum conditions, and the results showed that spent ChCl could be successfully recrystallized and reused four times with only slight reductions in 5-HMF yield. These studies pave the way for future advancements in green catalytic processes for the specific production of high value-added chemicals. This research offers an alternative route for sustainable production of 5-HMF from MUL feedstock, and is also potentially applicable to practical bio-refinery processes.
This study investigates the effects of moderate drought on growth, physiology, and heavy metal accumulation in Colocasia esculenta cultivated in cadmium (Cd) and zinc (Zn) contaminated soils. Plants were grown for 30 days under moisture levels of 100% (well-watered), 60%, and 40% field capacity (FC), representing moderate drought, in soils containing 100 mg/kg Cd and Zn. Drought had no impact on the relative growth rate (RGR), whereas metal exposure significantly reduced dry weight and stem height (p < 0.05) of the plants. The photochemical efficiency of PSII (Fv/Fm) and chlorophyll content remained stable (p > 0.05), but the water content (WC) of the leaves decreased under drought stress (p < 0.05). Translocation factors (TF) for both metals were above 1 in all treatments, indicating that heavy metal predominantly accumulated in shoots, with Cd exhibiting greater bioaccumulation factors (BAF) (2.40–3.03) than Zn (1.39–2.98). Overall, moderate drought limited biomass production but enhanced the accumulation of Cd and Zn and translocation to the shoots. These findings suggest that C. esculenta is suitable for the phytoextraction of Cd- and Zn-contaminated soils under drought-stress conditions after short-term exposure of 30 days. However, reduced biomass under drought may decrease overall phytoextraction despite higher tissue concentrations. Therefore, field-scale investigations under variable rainfall conditions are still required.
The suspension system plays a crucial role in ensuring the smoothness, stability, and safety of high-speed trains. This paper provides a comprehensive overview of the development of passive, semi-active, and active suspension systems. Advanced intelligent control architectures, such as Proportional Integral Derivative (PID), Linear Quadratic Regulator/Gaussian (LQR/LQG), and Sliding Mode Control (SMC), are compared with modern approaches, specifically fuzzy logic control frameworks based on Particle Swarm Optimization (PSO), Adaptive Neural Networks (ANN), and Adaptive Nonlinear Control (ANC). The paper evaluates the advantages and disadvantages of each strategy by considering five core criteria: passenger comfort, vibration suppression capability, disturbance rejection, adaptability, and implementation complexity. The synthesis of results indicates that intelligent and adaptive controllers provide significant quantitative enhancements; for instance, a PSO-optimized hybrid Fuzzy-PID controller achieves a 42.8% reduction in root-mean-square (RMS) acceleration. Most notably, the ANC strategy attains the highest improvement, enhancing ride comfort by up to 68.2% compared with passive systems. Finally, future research directions are outlined, emphasizing the necessity of high-fidelity multi-physics modeling and the development of computationally optimized, data-efficient reinforcement learning frameworks directly integrated into fault-tolerant control loops.
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.
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.
Road traffic crashes (RTCs) remain a leading global cause of mortality, yet conventional safety analyses often overlook how individuals perceive road risk, a key behavioral dimension influencing crash likelihood. In many developing cities, the absence of comprehensive crash databases further limits evidence-based safety planning. This study addresses these challenges by developing a transferable ensemble machine learning framework across junctions that predicts crash-risk hotspots using perceived risk as a proxy under data-scarce conditions. The framework integrates GIS (Geographic Information Systems)-based infrastructure mapping, KDE (Kernel Density Estimation)-derived spatial perception maps, PROMETHEE-based parameter prioritization, and an ensemble of Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT) to enable scalable, microspatial safety analysis. Perceptions of risk across behavioral, infrastructure, and environmental factors were systematically mapped using Geographic Information Systems (GIS) and modeled through the ensemble model. The optimized ensemble achieved high predictive performance (R² = 0.92, kappa = 0.779), effectively integrating psychological perception with spatial infrastructure data at a micro-spatial resolution of 1 m × 1 m. Strong correlations emerged between perceived risk and determinants such as tailgating tendency (r = 0.9347), building density and land-use mix (r = 0.9497), and poor lighting in dense areas (r = –0.3714). The resulting Crash Risk Perception Index (CRPI) revealed distinct spatial clusters, distinguishing validated danger zones from perceptual–empirical divergences. Hidden hazard zones (6.1% of cells) - high crash frequency but low perceived risk - and near-miss zones (12.7% of cells) - low crash frequency but high perceived risk - were identified as priority locations for targeted awareness, enforcement, and design interventions. Validation across three additional urban junctions (kappa = 0.72–0.83) demonstrated robustness and transferability. The framework enables cities lacking crash databases to proactively identify high-risk and near-miss zones, supporting perception-informed and evidence-based strategies for safer, more resilient urban mobility systems.
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.
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.
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.
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.
This research assessed the effects of land use change and climate change on the rates of erosion and sediment transport volumes. To quantify erosion and sediment mobilization, the Modified Universal Soil Loss Equation was applied in conjunction with sediment transport analysis. Precipitation projections from the Community Earth System Model Version 2 under the Shared Socioeconomic Pathway SSP2-4.5 scenario were employed to evaluate future climate impacts. Model validation was improved using high-resolution Unmanned Aerial Vehicle (UAV) footage. The study area was the Kricik River watershed, located in East Java, Indonesia. This watershed strongly affects the hydrological and sedimentary processes of the broader Brantas River basin. A comparative analysis of land use across 2010, 2021, and 2025 revealed a decline in forest and plantation cover, accompanied by an increase in dryland and urban development. Under the 2021 condition, the design peak discharge increased from 22.82 m³/s to 24.97 m³/s, while sediment yield rose to 101.94 tons, up from 75.21 tons in 2010. Climate change forecasts for 2026–2060 under SSP2-4.5 showed a substantial increase in hydrological and geomorphological hazards, with the expected peak discharge estimated at 30.83 m³/s. The sediment yield in this scenario escalated to 129.05 tons, whereas the capacity of debris flow transport increased from 80,596.71 m³ to 99,484.95 m³. The study provides actionable insights for watershed managers and policymakers, highlighting the importance of climate adaptation and sustainable land use planning to alleviate future hazards of erosion, sedimentation, and flooding. The findings emphasize that policymakers should prioritize the development and enforcement of land use zoning regulations and watershed conservation policies that limit impervious surface expansion, encourage reforestation or agroforestry, and protect upstream buffer zones to control runoff coefficients.
