Engineering and Applied Science Research

Drone-based application of inflorescence-inducing chemicals in longan orchards: Effects of flight altitude and mixing ratios

Phutakrit Chueapradit — 2026-06-23

Drone-assisted chemical application in longan orchards is feasible but requires refined methods for efficient and uniform aerosol delivery. This research aimed to determine the optimal drone flight altitude and chemical-to-water mixing ratio for inducing inflorescence in longan trees. Three chemical mixtures were tested: the conventional farmer-used ratio (50 g/10 L), a higher concentration (50 g/5 L), and a reduced dosage (25 g/5 L). Droplet deposition at different flight altitudes above the longan canopy was evaluated using water-sensitive paper to identify the most effective spraying conditions. The results revealed that at a drone flight altitude of 2 m above the longan canopy, the droplet deposition percentage from the drone sprayer reached a maximum of 76.22% on water-sensitive paper. This flight altitude was selected for the flight test to determine the appropriate mixing ratio of inflorescence-inducing chemicals for spraying in longan orchards. The reduced dosage treatment (25 g/5 L) resulted in the greatest number of inflorescences and the highest yield. Although the conventional and reduced treatments share the same chemical concentration (5 g/L), the latter involved a lower total amount of chemical applied per tree due to the reduced spray volume during drone operation. This approach enables farmers to decrease total chemical usage and operational costs while enhancing inflorescence induction and yield in longan orchards. Additionally, it enables faster drone operation, effectively doubling the treatment area per flight.

Forecasting dengue severity using machine learning and environmental predictors in Chanthaburi, Thailand

Pitcha Ratanawong — 2026-03-24

Dengue fever is a vector-borne disease whose dynamics are substantially driven by climate change and environmental variability. To this end, this study proposed the establishment and assessment of machine learning models for predicting the severity of dengue incidents in Chanthaburi province, Thailand, based on climatic and environmental variables from 2018–2022. Both classification accuracy and confusion matrix analyses were used to compare the implementation of four machine learning algorithms: Random Forest, Gradient Boosting, Extra Trees, and CatBoost. The highest-performing model was able to predict a sum score of 0.50 correctly 86.70% of the time, which suggests that the developed system has good predictive ability, especially in homing in on low-severity dengue cases. Still, challenges associated with recognizing high-severity cases persist. Shapley Additive Explanations (SHAP) sensitivity analysis revealed that specific air pollutant levels (PM10, PM2.5), time-lag parameters, and indicators such as temperature and humidity, particularly during certain periods, were significant predictors of dengue severity. Our results reveal the intricate relationships between environmental variables and the pattern of dengue transmission, arguing for a judicious use of machine learning tools as evidence-based support to inform disease control policies. Future studies that consider other variables, longer time series data, and advanced modeling techniques are needed to increase the accuracy of predictions, especially with respect to improving sensitivity for high-severity dengue outbreaks.

Lightweight IOT-friendly image encryption scheme based on Latin square and dynamic DNA coding

Sameeh Abdulghafour Jassim — 2026-04-29

The fast growth of Internet of Things (IoTs) devices calls for the demand to accomplish an efficient and lightweight image encryption. Also, protecting the privacy of sensitive visual information on devices with limited resources presents significant challenges. Existing techniques and methods are not able to provide both powerful security and low computational requirements. Therefore, we propose an innovative encrypting scheme that combines three methods (Latin squares, dynamic DNA sequencing, and chaotic systems). This architecture is designed for the IoT environments where it is important that transactions are made both efficient and secure. Additionally, our approach offers strong security assurances and suitable for the constraints of edge devices. The performance evaluations of the proposed technique against modern encryption algorithms display a promising result. Our proposal demonstrates almost perfect randomness. It achieved an image entropy of (7.9994) and a minimal correlation of (-0.00472). The proposed system also demonstrated its high ability to resilience the differential attacks, with NPCR and UACI of (98.671) and (34.098), respectively. Also, the keyspace of the proposed method is ( ), that providing a robust defense against brute-force attacks. Despite this high security level, the technique maintains practical performance, encrypting a (512×512) image in (1.4026) seconds. These outcomes confirm the algorithm's effectiveness for protecting sensitive images in IoT applications, offering both security and suitable efficiency for real-world use.

Integrating low-cost hydroacoustic surveying for canal sedimentation monitoring: a case study from southern Thailand

Phuwisa Kimtan — 2026-06-17

Efficient sedimentation monitoring is vital for maintaining navigability and hydraulic performance in canal systems, yet conventional hydrographic surveys remain costly and logistically demanding. This study integrates a low-cost hydroacoustic surveying method using a recreational-grade single-beam echosounder to provide cost-effective and practical solution for canal sedimentation management. The approach was first validated in Songkhla Lake, representing mixed freshwater, brackish, and saline conditions, and subsequently applied to the Samrong Canal in southern Thailand as a real-world case study. Controlled experiments compared echosounder-derived depths with reference measurements to evaluate vertical accuracy under different water types following the International Hydrographic Organization (IHO) S-44 Special Order criteria. Results showed a standard deviation (SD) of 0.05 m and RMSE₉₅ = 0.14 m in controlled settings, with no statistically significant effect of water type on accuracy. Semi-controlled field surveys yielded SD values of ±0.06 m (fresh), ±0.05 m (brackish), and ±0.09 m (saline), all within the IHO tolerance limits. The validated setup was then applied for 0.5-m contour bathymetric mapping in Samrong Canal, successfully delineating shoaling zones and siltation hotspots critical for maintenance planning. The findings demonstrate that integrating low-cost echosounders into canal monitoring workflows can produce IHO compliant, high-utility bathymetric data, offering a scalable and cost-effective alternative for sedimentation assessment in shallow tropical waterways.

The role of citronella oil as a bio-additive on laminar burning characteristics of iso-octane

I Made Suarta — 2026-04-23

This study focuses on the laminar burning velocity of iso-octane fuel with a citronella additive. The main objective is to investigate and synthesize the composition of citronella bio-additive in iso-octane fuel and how it affects laminar burning velocity. Laminar burning velocity testing was conducted in a cylindrical explosion combustion chamber at constant pressure with six compositions (IC-0 to IC-5) at equivalence ratios (ɸ) of 0.8 to 1.2. Functional groups were examined by Fourier Transform InfraRed (FTIR) and volatility was characterized by Thermogravimetric Analysis (TGA). The IC-2 mixture increased the Research Octane Number from 92.0 to 92.3; whereas, the smallest decrease in laminar burning velocity was 4.8% compared to IC-0. TGA testing showed an increase in volatility in IC-2. Overall, the addition of low dose citronella bio-additives, especially IC-2, provided a moderate octane increase with a minimal reduction in laminar burning velocity and improved evaporation, supporting its potential as a practical bio-additive for spark ignition fuels.

Free vibration analysis of a composite cantilever beam using a Timoshenko beam model: analytical, numerical, and experimental approaches

Rabiâ Abdeldjebar — 2026-06-09

This study investigates the free vibrations of a glass/polyester composite cantilever beam through an integrated analytical, numerical, and experimental approach. An analytical formulation, based on the Timoshenko beam model and incorporating bending-torsion coupling, was developed to compute natural frequencies and mode shapes. Numerical modeling, using finite element methods, simulated the vibrations while accounting for transverse shear and rotary inertia effects. Concurrently, an experimental modal analysis was performed by exciting the beam at multiple points and measuring natural frequencies via frequency response functions. The findings reveal strong agreement between the analytical and numerical approaches, with a relative error below 0.15%, but notable discrepancies with experimental data, exceeding 15%. These discrepancies are attributed to two main physical factors neglected in idealized models: the inherent material damping and the imperfect stiffness of the experimental support system. Adjusting the numerical model reduced these discrepancies, enhancing the method’s reliability. This approach provides a robust framework for designing composite structures, while highlighting the need to incorporate quantified damping and accurately defined material and boundary characteristics in future research for improved predictive accuracy.

The development of crop data recording system using NFC technology and economic feasibility analysis

Supawan Chamcha — 2026-01-28

This research presents the development and evaluation of a system that integrates Near Field Communication (NFC) tags, a mobile application, and a cloud-based database, enabling real-time crop cultivation traceability through QR codes accessible to vegetable consumers. The system was tested in a hydroponic lettuce farm (10 plots, three planting cycles), where six cultivation activities were recorded and transmitted, with accuracy verified through 300 peer-to-peer transmissions per activity. User satisfaction was evaluated through surveys of 400 farmers and 400 consumers, while an investment analysis was performed on a 6 × 12 m hydroponic greenhouse (four units). The NFC-based system records and transmits data with an average precision of 98.7%. Both farmers and consumers expressed high satisfaction, particularly regarding convenience, durability, and data accuracy. When the selling price of vegetables cultivated with the proposed system was assumed to be 10% higher than that of conventional cultivation, the economic feasibility analysis indicated a payback period (PBP) of 3.84 years, a return on investment (ROI) of 130.09%, a net present value (NPV) of 5,076.07 THB, an internal rate of return (IRR) of 9.46%, and a benefit–cost ratio (BCR) of 1.005. NFC technology, therefore, enhances product credibility and can be regarded as a promising tool for advancing modern agricultural practices.

MLSEL: A tuned multilayer stacking ensemble learning with meta feature for flood risk prediction

Rini Sovia — 2026-04-29

While stacking ensemble methods have been widely adopted for disaster risk classification, most traditional implementations are limited to shallow single-layer architectures, lacking generalization capacity and adaptive meta-feature design. This study proposes Multilayer Stacking Ensemble Learning (MLSEL), a novel approach that addresses these limitations through three key innovations: (i) a deep three-layer stacking architecture, (ii) Meta Feature Augmentation (MFA) to enrich inter-layer representations, and (iii) automated hyperparameter optimization using Optuna to enhance meta-learner performance. The model is evaluated on a multiclass flood risk dataset consisting of 50,000 structured records. Results reveal that the proposed MLSEL particularly the configuration using XGBoost + Optuna + MFA achieves superior accuracy of 98.69%, significantly outperforming both the best-performing baseline and conventional stacking models. This research demonstrates that combining deep-layered learning, meta-feature engineering, and adaptive optimization effectively overcomes overfitting, feature redundancy, and scalability issues inherent in traditional stacking. The MLSEL framework establishes a robust foundation for accurate and reliable disaster risk prediction systems.

Influence of rice husk ash calcination temperature in determining compressive strength and microstructure of mortar

Carlos Eduardo Ramos Brast — 2026-04-17

The construction industry consumes large quantities of cement and is therefore a major source carbon dioxide (CO₂) emissions. As a more sustainable alternative, rice husk ash (RHA) can partially replace cement, taking advantage of its pozzolanic properties. The reactivity of RHA varies depending on the calcination temperature, affecting the strength and microstructure of the mortar (MM). This study evaluates how calcination temperature of RHA affects compressive strength and MM. RHA was heat treated at 600, 650, 700 and 750 °C to analyze the compressive strength of the mortar. Mortar specimens were prepared with RHA replacing cement at 5, 10, 15 and 20% to replace cement and compressive strength tests were performed at 7, 14, 21 and 28 days. In addition, the MM was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) were used to evaluate the MM. The results indicate a maximum gain in compressive strength up to 55.87% when 15% of the cement was replaced with RHA calcined at 700 °C, whose amorphous silica content was 69.40%. Moreover, microstructural analyses evidenced the formation of C-S-H and C-A-S-H gels, which densified the mortar, improved the interfacial zone (ITZ), and reduced overall porosity. It is concluded that the calcination temperature significantly influences the pozzolanic reactivity of RHA, the mechanical strength, and MM; RHA calcined at 650 - 700 °C exhibited the best mechanical performance, attributable to higher amorphous silica content and greater microstructural densification. It is recommended for future works to investigate calcination temperatures above 750°C, analyze the relationship between RHA particle size and its amorphous silica content, and evaluate the mortar´s long-term durability. In addition, studies should assess the interaction of RHA with other alternative materials to identify synergistic effects and optimize the RHA dosage.

Corrosion behavior of Sn-0.7Cu-xIn solders on simulated acid rain

Thammaporn Thublaor — 2025-12-19

This study investigated the effect of minor indium additions (0.1, 0.5, 1.0 wt.%) on Sn-0.7Cu solder, resulting in Sn-0.7Cu-0.1In, Sn-0.7Cu-0.5In, and Sn-0.7Cu-1.0In solders. The influence of indium addition on microstructure, melting temperature, microhardness, and corrosion resistance of the alloys was investigated. Corrosion resistance was evaluated in simulated acid rain with a pH of 3.5 using immersion and potentiodynamic polarization tests. Results indicated that the melting temperature decreased from 225.9 °C to 223.1 °C with increasing indium content. The addition of indium also refined the microstructure, reduced the β-Sn phase fraction, and improved the microhardness of the solder. Analysis of corrosion products identified SnO and SnO₂, confirming that tin is the primary element susceptible to corrosion in Sn-0.7Cu-xIn solders exposed to acidic conditions. Polarization curves revealed that corrosion resistance improved significantly with increasing indium content, with the lowest corrosion rate observed at 1.0 wt.% of indium.

Experimental and modelling evaluation on the protective performance of bulletproof plate composites for building protection

Phongthorn Julphunthong — 2026-04-10

Critical infrastructure such as police stations, military bases, and security checkpoints is increasingly exposed to armed threats, while most existing structures lack adequate ballistic protection. Conventional construction materials are generally ineffective against high-velocity projectile impacts, highlighting the need for practical, retrofit-compatible protective systems. This study aims to develop and optimize composite bulletproof panels that meet NIJ 0108.01 Level III requirements for resistance against 7.62×51 mm NATO M80 ammunition, with emphasis on structural feasibility and cost-effectiveness. An experimental program was conducted using three materials: standard structural steel (SS400), high-hardness steel (HS450), and asphalt cement (AC). These materials were configured as single-layer, double-layer, and sandwich panels. Ballistic tests were performed using 7.62×51 mm NATO projectiles at an average velocity of 847±9.1 m/s. Projectile velocities before and after impact were recorded to evaluate energy absorbed. Finite element simulations using ABAQUS were employed to validate experimental results and analyze stress-wave propagation and failure mechanisms. The results indicate that material hardness plays a more significant role than thickness in enhancing ballistic resistance. While 6 mm SS400 absorbed 35.81% of impact energy, a thinner 3 mm HS450 layer achieved 28.95%, demonstrating the efficiency of high-hardness materials. Layered configurations significantly improved performance, with SS400-6/AC25 and HS450-3/AC25 absorbing 62.39% and 78.35% of impact energy, respectively. Notably, sandwich configurations (SS400-6/AC25/SS400-6 and HS450-3/AC25/SS400-6) achieved complete projectile arrest. Numerical results confirm that a high-hardness strike face combined with a viscoelastic backing layer maximizes energy dissipation. The optimized HS450-3/AC25/SS400-6 panel provides full ballistic protection with reduced weight compared to conventional steel armor. Field validation demonstrates its applicability for retrofitting existing infrastructure. This study contributes practical design guidelines for developing cost-effective ballistic-resistant building systems in high-risk environments.

Identification and quantification of quality of intact durian fruits using NIR spectroscopy

Lakkana Pitak — 2026-01-12

Quality classification of durian fruits is based on the dry matter (DM) content of the pulp. According to Thai agricultural standards, durian fruit (Monthong variety) must contain at least 32% DM. This study aimed to develop a classification model for assessing durian quality based on DM content, categorizing fruits as either “rejected” (DM < 32%) or “accepted” (DM ≥ 32%). Near-infrared (NIR) spectra were collected as the durian fruits moved along a conveyor belt. The models were developed using two spectral ranges: short-wavelength near-infrared (SWNIR; 4501000 nm) and long-wavelength near-infrared (LWNIR; 8601750 nm). Owing to the imbalance in the dataset between the two classes, the data were adjusted using the synthetic minority oversampling technique to create a balanced dataset. Prediction models were built using different spectral preprocessing methods and algorithms. For the LWNIR range, the models constructed using LDA, SVM, KNN, and SDA achieved accuracies of 95%, 90%, 93%, and 93%, respectively, for the test set. The SWNIR models, developed using the same algorithms, achieved accuracies of 90%, 88%, 90%, and 90%, respectively, for the test set. PLS-regression was used to predict the DM content from both LWNIR and SWNIR data. With the 2nd derivative preprocessing method, the models achieved R² values of 0.89 and 0.79, SEP values of 5% and 6.89%, and RPD values of 2.29 and 1.66, respectively. The wavelength range significantly influenced the model performance, whereas spectral pretreatment had a minor effect on the model's predictive ability. Overall, NIR spectroscopy demonstrated the potential for nondestructive quality grading of whole durian fruits. This work is the first to establish real-time, in-line models for durian grading based on DM content, advancing beyond the previous destructive method. The findings demonstrate the feasibility of automated, nondestructive, and objective quality assessment, supporting industrial automation, precision agriculture, and export quality assurance.

Assessing carbon dioxide emissions for long-span cable stay bridge of railway construction project

Panithi Nakhonthong — 2026-06-19

The purpose of this study was to assess the quantity of greenhouse gas emissions associated with the long-span cable stay bridge of railway construction project. The amount of greenhouse gas in term of carbon dioxide equivalent (CO2-eq) emission was calculated from the construction project of a double-track railway cable stay bridge across the Mae Klong River in Thailand. Then, the results of the calculation were classified according to work types such as bored pile, foundation, column and pier head, pylon and segment, pre-stressed wire and cable. The study also included the emission of machinery and material transportation on the site, from cradle to gate and gate to gate. Both national and international database of carbon dioxide emissions coefficients were applied. The results showed that total CO₂-eq emission emit 10,144,522 kg CO₂-eq or 29,837 tons CO₂-eq per kilometer. The types of work that emit the most carbon dioxide is pylon and segment which accounted for 36.0%. Meanwhile, the CO₂-eq emission from bored piles, footing, column and pier head, prestressed wire and cable, and transportation of machinery and material within site accounted for 23.0%, 18.0%, 10.0%, 9.5%, and 3.5%, respectively. To sum up, the CO₂-eq emission is caused by up to 89.0% of construction materials production.

CFD-based stepwise integration and analysis of hull appendages on the resistance performance of a displacement vessel

Chairat Napasaksri — 2026-04-29

This study presents a comprehensive numerical investigation into the hydrodynamic effects of commonly adopted hull appendages, which are the bulbous bow, skeg, and sonar dome, on a displacement ship operating across a wide Froude number range (Fr = 0.16 to 0.41). Simulations were conducted using Reynolds-Averaged Navier–Stokes (RANS) equations in combination with the Volume of Fluid (VOF) method to capture free-surface effects. A four-stage stepwise configuration scheme was employed: bare hull, hull with bulbous bow, with bulbous bow and skeg, and finally the fully appended hull including the sonar dome. Results indicate that the bulbous bow contributed the most significant reduction in total resistance, which is up to 8.62% at Fr = 0.26, through wave interference and improved pressure distribution. The skeg aided aft-body flow alignment while the sonar dome showed negligible effect on resistance within typical operating conditions. All appended configurations exhibited increased frictional resistance due to surface area growth, with the full configuration showing an average increase of 6.97%. However, these increases were offset by considerable reductions in pressure resistance, particularly in transitional speeds, with a maximum pressure drag reduction of 29.03% at Fr = 0.24. The novelty of this study lies in its systematic, configuration-based evaluation of multiple appendages under consistent CFD conditions, enabling clear quantification of trade-offs between frictional and pressure resistance. The findings offer practical insights into appendage integration strategies for performance optimization in displacement vessels and guidelines for naval architects in selecting and integrating appendages to optimize resistance characteristics during the early design stages of displacement vessels.

Electrospinning nanofibers for wound healing using antioxidant from Rang Jued (Thunbergia laurifolia Lindl.) extract via subcritical fluid extraction

Nichapa Areepong — 2025-12-02

Rang Jued (Thunbergia laurifolia Lindl.) is a local Thai plant known for its bioactive compounds. In this study, subcritical ethanol extraction was used to extract antioxidant from Rang Jued (RJ) leaves. A total of 15 experiments were designed using Box-Behnken design. Response surface methodology was employed to determine the optimal condition, which yielded the highest DPPH scavenging activity of 94.91% and a total phenolic content of 30.35 mg GAE/g under the conditions of 1.64 g of Rang Jued powder, an extraction temperature of 190 ºC, and an extraction time of 15.14 minutes. Furthermore, the electrospinning technique was used to fabricate antioxidant wound dressing nanofibers. The process was conducted by varying the ratio between PVA and RJ extract, as well as the voltage supply. Scanning electron microscopy (SEM) was used to investigate the morphology of the nanofibers. The average diameter ranged from 248 to 362 nm. The highest antioxidant activity of the nanofibers was observed at 72.25%, using a PVA:RJ extract ratio of 7:3 and a voltage of 40 kV.

Development and validation of free flow speed estimation models for multilane highways in Thailand

Moe Sandi Zaw — 2025-12-16

This study developed and evaluated free flow speed (FFS) estimation models for multilane highways in Thailand by applying multivariable linear regression. Data from 272 highway segments were analyzed considering the percentage of heavy vehicles, left shoulder width, and access point density. Highways were categorized into four-lanes and more-than-four-lanes configurations and further analyzed under different base free flow speeds (BFFS) ranging from 90 to 120 km/h. For four-lane highways, Model 1-2 (BFFS = 100 km/h) achieved the best predictive performance, with an R² of 0.9583, root mean squared error (RMSE) of 1.51, and mean absolute error (MAE) of 1.24. For more-than-four-lanes highways, Model 2-3 (BFFS = 110 km/h) performed best (R² = 0.9387, RMSE = 1.86, MAE = 1.46). All variables showed significant negative effects on FFS. The developed models provide practical tools for assessing roadway performance, thereby regulating speeds, and guiding infrastructure planning, enhancing safety and efficiency on Thai multilane highways.

Classification of Thailand’s industrial firms under global supply chain disruptions: Integrating resilience and sustainability in industrial performance

Woramol C. Watanabe — 2025-11-04

There has been significant disruption of global supply chains as a result of pandemics, geopolitical tensions, and climate-related events. Firms around the world, including those in Thailand, have been compelled to adapt, with increasing emphasis on resilience and sustainability. While many studies have addressed supply chain performance through these lenses, few have focused specifically on the industrial context in Thailand. Moreover, the direction of industrial transformation in response to global challenges remains insufficiently examined. This study addresses these gaps by proposing a comprehensive framework that integrates conventional supply chain performance indicators with resilience and sustainability dimensions. Data were collected from 98 publicly listed industrial firms in Thailand using their 2023 annual reports. Principal component analysis (PCA) was used to identify key performance patterns, and this was followed by K-means clustering to classify firms based on strategic orientation. The analysis revealed four distinct clusters. The largest group, referred to as resilient enterprises (54 firms), demonstrated a balanced performance across efficiency and resilience. Performance-oriented firms (23 firms) exhibited high customer satisfaction, product quality, and flexibility. Agile and lean operators (6 firms) prioritized operational speed and rapid delivery. Flexibility-centric firms (16 firms) focused on adaptability but faced constraints in financial and inventory performance. Overall, the findings indicate that economic factors such as revenue, profitability, and productivity, together with resilience attributes like agility and flexibility, are the primary variables that differentiate firms. This suggests that Thailand’s industrial firms currently place the most emphasis on operational efficiency and adaptability, while broader environmental and social sustainability concerns remain less prominent in strategic differentiation.

Design and development of an IoT-enabled smart pill box prototype with a smartphone app-based medication reminder

Muhammad Thesa Ghozali — 2026-06-12

Non-adherence to medication regimens remains a significant concern in healthcare, negatively impacting patient outcomes and increasing costs. This pilot study investigated using Internet of Things (IoT) and microcontroller technologies to enhance adherence through a smartphone-app-driven smart pill box. The prototype integrated an IoT-enabled microcontroller with a custom app designed to send real-time medication reminders and log pill box usage. Ten participants with chronic conditions requiring daily medication evaluated the device over four weeks. Adherence rates, self-reported before the trial, were compared to those monitored during the trial. Results indicated a marked improvement in adherence, increasing from 78% at baseline to 92% during the study. Additionally, 90% of participants rated the system as user-friendly and expressed willingness to continue its use. The study highlights the potential of this IoT-enabled smart pill box to address medication non-adherence effectively. However, further research with larger sample sizes is necessary to confirm its broader applicability across diverse populations and medical conditions. These findings suggest that integrating IoT and microcontroller technologies into adherence interventions could significantly benefit chronic disease management and healthcare outcomes. This innovative approach holds promise for addressing the widespread challenge of medication non-adherence, but larger and more demographically diverse studies are needed to validate its long-term effectiveness and generalizability.

Enhancing mechanical and thermal performance of EPS lightweight concrete using condensed silica fume for sustainable building applications

Thanongsak Nochaiya — 2026-01-12

Condensed silica fume (CSF), a highly reactive pozzolanic material, is produced as a by-product of silicon and ferrosilicon alloy manufacture. It is well known for its high silica content and ultra-fine particle size, which make it useful for improving the cement performance. The present study aims to investigate the impact of CSF on the properties of lightweight concrete composites (LWC). CSF was utilized to partially replace cement by up to 8 wt% in order to improve the strength properties of LWC. To produce LWC with a density of 800-900 kg/m³, recycled expanded polystyrene foam (re-EPS) was incorporated at 53 Vol% of the total LWC volume. Compressive strength, density, thermal conductivity, and time lag were investigated. The results indicated that the compressive strength of LWC blended with CSF increased significantly at early ages, while the density was notably lower than that of the control LWC. Although the thermal conductivity did not significantly change with increasing CSF content, all values remained within the acceptable range for insulating materials. In addition, the re-EPS lightweight concrete containing CSF demonstrated a longer time lag in heat transfer from the exterior to the interior of the building wall, which potentially improving energy efficiency for building applications.

Developing a predictive model for quantity estimation of tie columns and lintel beams in residential construction

Tanapat Namjan — 2025-10-17

Accurate construction cost estimation is at the root of any effective project planning, yet it often requires extensive expertise and time-consuming calculations. This paper discusses a predictive equation for estimating the quantity of tie columns and lintel beams in a two-story residential building. In this study, multiple linear regression analysis was employed to identify the significant variables that impact the quantity of those structural elements using 75 sets of residential drawings, all of which featured conventional two-story brick masonry construction with reinforced concrete frames. The formulated equation, where Y represents the total linear meters of tie columns and lintel beams combined, is expressed as Y = 1.834 + 1.243 (brick wall area in m²) - 0.639 (open space area in m²). The equation was checked against fifteen residential designs with detailed estimates. The percentage error was observed to be between -3.58% and 5.37%, which is considered within an acceptable limit for preliminary estimates. This equation could provide a useful tool for cost estimators, offering a much-simplified approach yet yielding reasonable accuracy for preliminary assessments of the structural quantities of buildings. This research highlights the equation's potential for improving efficiency in project planning and cost estimation within its defined scope, with further validation across a wider range of designs recommended to broaden its applicability.

Comparative adsorption study of Pb(II), Fe(II), and Zn(II) using non-chemically activated rubber seed shell biochar and commercial activated carbon

Chuthamat Chiamsathit — 2025-11-27

The widespread contamination of water sources by heavy metals such as Pb(II), Fe(II), and Zn(II) poses serious environmental and health risks. This study investigated the use of non-chemically activated biochar derived from rubber seed shells, an agricultural waste material, as a sustainable adsorbent for heavy metal removal. Biochars were produced by a two-step carbonisation process at temperatures of 850, 900, and 950 °C, and the physicochemical properties were systematically assessed. The sample carbonised at 850 °C (PRC850) exhibited the most favourable properties, including a high BET surface area (795 m²/g), mesoporous structure, and suitable surface functional groups, as confirmed by SEM, BET, XRD, and FTIR analyses. Initial screening was conducted for Pb(II), Fe(II), and Zn(II) adsorption, and PRC850 demonstrated superior performance, removing up to 98.64% of Pb(II), which was significantly higher than the 85.52% removal rate achieved by commercial-grade activated (CGA) carbon. The adsorption behaviour of Pb(II) was best described by the Langmuir isotherm model, and the pseudo-second-order kinetic model fitted the experimental data well, indicating chemisorption. These findings indicated that rubber seed shell biochar had the potential to serve as a cost-effective and ecologically friendly adsorbent, particularly for Pb(II) removal, while also performing effectively for Fe(II) and Zn(II).

Optimization of oxygen transfer coefficient and standard aeration efficiency of a Venturi-type aerator using response surface methodology

Aphirak Khadwilard — 2026-03-11

This research focuses on determining the optimal parameters for maximizing dissolved oxygen (DO) when using a Venturi-type aerator, considering two objectives: oxygen transfer coefficient corrected to 20°C (K_La₂₀) and standard aeration efficiency (SAE). The determination of the optimal parameters for the Venturi-type aerator was carried out under thirty experimental conditions of a face-centered central composite design (FCCD), involving four influencing variables: Venturi convergence angle (α), Venturi divergence angle (β), water flow rate (Q_w), and air flow rate (Q_a). Response surface methodology (RSM) was used to evaluate the experimentally collected data. The analysis of the experimental results showed that the most suitable conditions for were 45° convergence angles and 15° divergence angles, with a water flow rate of 40 L/min and an air flow rate of 0.9 L/min, resulting in a value of 4.278 h^-1. For optimal SAE values, the study found that the Venturi convergence angle of 45°, the Venturi divergence angle of 15°, the water flow rate of 20 L/min, and the air flow rate of 0.9 L/min should be set. These parameters gave an SAE value of 0.0343 kgO₂/kWh. Analysis of the regression equations developed in this study showed that the coefficients of determination (R²) of the K_La₂₀and SAE prediction equations were more than 90% for both equations. Therefore, the response can be accurately predicted, and these equations serve as guidelines for the design of the most appropriate Venturi-type aerator in practice.

Ceria-modified zeolite: A dual-function approach for effective removal of arsenite from polluted water sources

Suttikorn Suwannatrai — 2025-10-30

As a carcinogen, arsenic poses a significant threat when it contaminates water sources and agricultural products. In water-based environmental contamination, the most significant arsenic species are arsenate (As(V)) and arsenite (As(III)), with the latter presenting a greater challenge for removal. The development of more efficient adsorbents to successfully remove As(III) from contaminated water is still needed. A novel nanosorbent, ceria supported on Na-P zeolite (CeZ), was created in this study to perform the dual functions of oxidizing As(III) to As(V) and subsequently adsorbing the resulting As(V). CeZ was characterized by XRD, TEM, FTIR, pHpzc, and XANES analyses. Batch adsorption experiments indicated that As(III) removal in the pH range of 3-10 was highly efficient, with a maximum removal capacity of 31.746 mg g^-1, which was best explained by pseudo-second-order kinetics. XANES analysis confirmed that CeZ oxidized As(III) to As(V) on the surface during As(III) adsorption. The hydroxyl groups at the CeZ interface play a key role in As(III) sorption, forming inner-sphere monodentate and bidentate complexes. As(III) removal was effective because the sorption reaction was coupled with the oxidation process. Specifically, the CeO₂ on the Na-P zeolite surface was the main factor responsible for the oxidation of As(III) to As(V) and its sorption. The As(V) in the solution subsequently adsorbed onto the zeolite.

Superior mechanical and tribological properties of Al7075 metal matrix nanocomposites processed through a novel multi-stage casting route

Charinrat Potisawang — 2025-10-08

This study aims to improve the microstructural features and mechanical performance of Al7075 aluminium matrix composites reinforced with silicon carbide (SiC) nanoparticles and graphite (Gr) through a novel processing route. The proposed method integrates mechanical alloying-assisted semisolid stir casting with die casting, followed by a T6 heat treatment. The Al7075/SiC composite subjected to T6 treatment exhibited superior mechanical properties, including a microhardness of 218 HV, a 0.2% proof stress of 250 MPa, an ultimate tensile strength of 364 MPa, and an elongation of 16%. These enhancements are primarily attributed to synergistic strengthening mechanisms, including grain refinement, Orowan looping, and precipitation hardening. In contrast, the Al7075/SiC/Gr hybrid composite demonstrated a marginally reduced ultimate tensile strength of 254 MPa, representing a 12% decline compared to the Al7075/SiC composite, which was attributed to graphite agglomeration and inadequate interfacial bonding. Wear resistance testing revealed that the SiC-reinforced composite exhibited the lowest material loss, with scanning electron microscopy (SEM) analyses confirming reduced groove depth and plastic deformation. Conversely, the hybrid composite displayed increased surface roughness and porosity, primarily due to graphite-induced defects. These findings indicate that the incorporation of SiC nanoparticles, in conjunction with T6 heat treatment, constitutes an effective strategy for enhancing the structural integrity and mechanical performance of Al7075-based composites. However, further optimization of graphite morphology and dispersion is necessary to fully realize its potential as a solid lubricant in hybrid composite systems.

Combination of cassava starch and peel, using corn stalks and husks, and bentonite clay as fillers to enhance the mechanical properties of bioplastics

Donny Lesmana — 2026-06-17

This study investigates the effect of combining cassava starch gelatin and cassava peel gelatin and evaluates the influence of two different fillers, cellulose and bentonite clay, on the mechanical properties of bioplastics. It aims to obtain bioplastics with tensile strength and elongation properties comparable to low-density polyethylene (LDPE). The study employs different mass ratios of starch to gelatin (9:1, 8:2, and 7:3 g/g) and incorporates bentonite clay and cellulose fillers (corn stalks and husks), each at 0.4 g. The materials were processed with a 200-mesh sieve. Bioplastic synthesis involved a glycerol concentration of 25% (w/w), stirring at 375 rpm for 35 minutes at 90°C. Mechanical properties (tensile strength, elongation, and Young's modulus) were analyzed, along with SEM and FTIR characterization. The results indicate that the bioplastic formulation with a starch-to-gelatin ratio of 8:2 and bentonite clay as a filler exhibits the most promising mechanical properties, with a tensile strength of 1.837 MPa, elongation of 33.584%, and a Young's modulus of 5.47 MPa. Comparatively, bentonite clay enhanced tensile strength and rigidity, while cellulose fillers provided higher elongation and flexibility but lower reinforcement due to particle agglomeration. FTIR analysis identified key functional groups, including O-H, CH₂, C=O, N-H, and C-O. The combination of cassava starch, cassava peels, corn stalk-derived cellulose, fillers, and plasticizers significantly impacts the quality and mechanical properties of bioplastics.

Evaluation of missing value handling methods in machine learning for emergency department mortality prediction

Narawish Kophimai — 2025-09-16

Missing data remains a significant challenge in emergency medicine, particularly in mortality prediction models. This study investigates five distinct missing value handling methods applied to various machine learning algorithms using a dataset of 331,151 emergency department records from a Thai hospital (2016–2021). The study evaluates complete case analysis, zero imputation, mean imputation, k-Nearest Neighbors (kNN) imputation, and MissForest, combined with logistic regression, decision tree, random forest, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). The results indicate that XGBoost with zero imputation delivers the best performance, achieving an accuracy of 0.8659, precision of 0.8726, recall of 0.8659, F1-score of 0.8681, and an AUC ranging from 0.8848 to 0.9947 across eight prediction classes. Furthermore, tree-based models demonstrated greater stability across different missing value handling methods, whereas linear models were more sensitive to imputation techniques. These findings suggest that strategic selection of missing data handling approaches can significantly enhance the reliability of mortality predictions in emergency care settings.

Assessment of the water quality index and pesticide genotoxicity via the Single-Cell Gel Electrophoresis (SCGE) assay utilizing aquatic plants

Veerapas Na Roi-et — 2026-01-30

Pesticides pose significant threats to the integrity and functionality of aquatic ecosystems worldwide, necessitating thorough assessment and mitigation by all stakeholders. Chemical analysis alone cannot predict the synergistic effects of various contaminants found in aquatic ecosystems. This study assesses water quality indicators, including the Water Quality Index (WQI) for physicochemical and biological parameters, alongside the human health risks associated with pesticide exposure. The investigation focuses primarily on pesticide toxicity, persistence, and the impacts on water resources. Gas Chromatography-Mass Spectrometry (GC-MS/MS) is employed to quantify pesticides in aquatic ecosystems. Chemical analyses revealed detectable pesticide concentrations in water supplies, albeit at relatively low levels. Additionally, the single-cell gel electrophoresis (SCGE) assay was utilized to evaluate DNA damage in aquatic plants. Genotoxic effects were assessed using four species as bioindicators: Ceratophyllum demersum L., Eichhornia crassipes (Mart.) Solms, Ipomoea aquatica Forssk., and Salvinia cucullata Roxb. ex Bory, to evaluate aquatic ecosystem health. The results demonstrated that DNA fragmentation increased in proportion to pesticide exposure levels, with maximum damage reaching 17.49% and statistically significant differences from control specimens (p < 0.01). Complex species-specific responses were revealed during analysis, with trends suggesting potential phytoremediation mechanisms rather than simple bioaccumulation patterns. The SCGE assay proved effective for assessing DNA migration rates under conditions of low-level pesticide contamination, establishing aquatic plants as valuable biomonitoring tools for environmental risk assessment.

An overview of logistics network through bibliometric analysis using VOSviewer

Tran Trung Chuyen — 2026-01-07

Logistics networks are essential frameworks that facilitate the efficient movement and management of goods within supply chains, particularly in the context of globalization and technological advancements. The objectives include: (i) conducting a large-scale bibliometric analysis of logistics network literature from 1980 to 2024, (ii) identifying leading authors, institutions, and countries, (iii) mapping thematic clusters and keyword co-occurrences, and (iv) clarifying research gaps to guide future studies. Using 3,686 Scopus-indexed documents, performance analysis and science mapping techniques (VOSviewer, MS Excel) were applied to analyze publication growth, citation metrics, co-authorship, co-citation, and keyword networks. Key findings show exponential growth in publications since 2004, with “China”, “the United States”, and “Germany” leading in both output and collaboration strength; “logistics” and “logistics network” are the most frequent keywords but have lower average citations compared to emerging terms such as “sustainability” and “circular economy”; and high-impact thematic clusters are shifting towards sustainable and technology-driven logistics. The novelty of this study lies in providing the first comprehensive, quantitative mapping of logistics network research, integrating structural collaboration patterns with thematic evolution to uncover actionable insights for advancing both theory and practice.

A genetic-neural optimization approach for friction stir spot welding of semi-solid metal Aluminum Alloy 5083

Konkrai Nakowong — 2025-09-18

Friction Stir Spot Welding (FSSW) of Semi-Solid Metal (SSM) Aluminum Alloy 5083 poses challenges due to nonlinear interactions between process parameters and mechanical properties. Traditional optimization methods, such as Response Surface Methodology (RSM), provide statistical modeling but often fail to capture these complexities accurately. This study integrates Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) and Response Surface Methodology (RSM) to develop a hybrid optimization framework for FSSW parameter selection, aiming to enhance weld strength and hardness while minimizing the number of experimental trials. The ANN model, trained using a feed-forward backpropagation algorithm with the Levenberg-Marquardt learning rule, predicts tensile shear strength and weld hardness based on key parameters: rotational speed, travel speed, and dwell time. GA optimizes these parameters through an evolutionary search, while RSM validates the results and assesses parameter interactions. The optimized parameters 2143.93 RPM, 14.33 mm/min, and 6.58 s yield a shear strength of 5999.99 N. ANN exhibited lower mean absolute error (MAE) and root mean squared error (RMSE) than RSM, confirming superior predictive capability. However, RSM provided statistical validation, ensuring robust insights. The findings highlight the effectiveness of AI-driven optimization in welding applications, reducing experimental trials while ensuring optimal mechanical performance. Future research should explore the integration of deep learning and real-time sensor feedback for further enhancement.

Application of the HEC-HMS model for analysing land use change and hydrological responses across different return periods in tropical flood-prone areas

Suzani Mohamad — 2025-10-24

Land use change is a significant environmental concern worldwide today, as it has the potential to increase the frequency of natural disasters, such as floods. The Sg. Segamat Watershed, which is particularly vulnerable to flooding, highlights the importance of hydrological modelling as a crucial tool in disaster mitigation. In this study, the Hydrologic Engineering Center’s Hydrologic Modelling System (HEC-HMS) was utilised to assess the effects of land use change on hydrological responses across various return periods. The analysis examined both the pre- and post-calibration phases under varying land use conditions. Land use data from 2006 and 2011 were used to simulate future scenarios. The findings showed that the expansion of built-up areas and the conversion of forested land to mixed agriculture had a significant influence on flood patterns. Specifically, built-up areas expanded by 2.24%, resulting in increased flood volumes in sub-basins 4, 9, and 12 between 2006 and 2026. Concurrently, forest cover declined by approximately 4.40%, which led to heightened flood peak heights in sub-basins 1, 3, and 11 under all land use conditions. Sub-basin 3 recorded the highest flood peak height, estimated at 1,150.20 m³/s in the pre-calibration phase, 1,165.80 m³/s in the post-calibration, and 1,036.20 m³/s using the initial CN with calibrated parameters. Meanwhile, sub-basin 4 demonstrated the highest flood volume, with estimates reaching 342.10 mm pre-calibration, 366.09 mm post-calibration, and 341.04 mm using initial CN with calibrated parameters. These results clearly demonstrate how land use changes influence hydrological behaviour, emphasising the need for watershed planning and flood risk management. The study highlights the value of hydrological modelling as a tool for enhancing flood mitigation strategies and provides crucial insights for policymakers, planners, government agencies, and local communities.