Engineering and Applied Science Research

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

2026-01-28T13:57:40+07:00

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.

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

2026-01-27T18:27:08+07:00

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.

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

2026-01-27T18:43:50+07:00

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.

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

2026-01-27T11:00:00+07:00

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

2026-01-27T18:20:51+07:00

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

2025-07-15T15:58:42+07:00

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.

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

2026-01-28T10:57:20+07:00

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

2025-07-08T14:22:16+07:00

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

2026-01-27T10:43:49+07:00

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).

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

2025-07-15T16:59:46+07:00

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

2025-07-03T11:39:24+07:00

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.

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

2025-08-01T13:18:29+07:00

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

2026-01-30T09:22:30+07:00

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

2026-01-27T18:34:46+07:00

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

2025-06-29T16:16:24+07:00

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

2025-06-18T18:46:02+07:00

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.

Enhanced biogas production through co-digestion of tapioca starch wastewater and duckweed in a continuous stirred tank reactor

2025-06-04T17:01:37+07:00

This study investigates the biogas production performance of co-digesting duckweed with tapioca starch wastewater (TSW) in a laboratory-scale continuous stirred tank reactor (CSTR). Duckweed, with its rich nutrient composition, represents an underutilized biomass resource for renewable energy production in Thailand, where the tapioca starch industry constitutes a significant economic sector. The experimental setup utilized a 3-liter CSTR operated at mesophilic conditions (35°C) with a hydraulic retention time (HRT) of 28 days. Initial mono-digestion of TSW at an organic loading rate (OLR) of 0.31 gCOD/L-d resulted in a specific methane production of 0.28 NL-CH₄/g COD removed (NL = liter of gas at 273 K and 1 atm). Subsequent co-digestion with duckweed (1.0 g dry weight per liter of TSW) under identical operational conditions, enhanced methane production to 0.35 NL CH₄/g COD removed—corresponding to a 1.3-fold increase in specific methane production yield. These findings demonstrate that co-digestion of duckweed with TSW significantly enhances methane yield compared to mono-digestion of TSW, offering a promising approach for simultaneous wastewater treatment and renewable energy generation in Thailand's tapioca processing industry.

Chitosan beads with Calamondin essential oil: Antioxidant, antimicrobial, physical, and structural properties

2025-09-16T15:23:17+07:00

Chitosan, a biopolymer renowned for its biodegradability and antimicrobial properties, is frequently enhanced with essential oils (EOs) to improve functionality. This study investigated the impact of incorporating calamondin EO (CmEO) into chitosan beads on their physicochemical properties, antioxidant activity, antibacterial efficacy, and structural characteristics. The incorporation of CmEO improved the sphericity of the beads, ranging from 68.20% to 87.59%, and produced 177–263 beads per 10 mL of solution, depending on the formulation. The addition of CmEO to chitosan beads significantly boosted antioxidant activity, with the highest concentration exhibiting radical scavenging activity of 77.96%. Antibacterial assays revealed that the chitosan beads with CmEO could inhibit the growth of Escherichia coli, Bacillus cereus, Salmonella enteritidis, and Staphylococcus aureus. Structural analysis shows significant changes on the bead surface corresponding to changes in CmEO concentration. These findings highlight the potential of chitosan-based materials enriched with EOs for sustainable food preservation.

Assessment of surface water and groundwater potential under climate change in the Lam Phaniang River Basin

2025-04-18T11:31:21+07:00

The assessment of surface water and groundwater potential under climate change in the Lam Phaniang River Basin was based on SWAT model for evaluating streamflow, and MODFLOW model for evaluating groundwater flow. The SWAT model was well calibrated and validated with daily discharge measured at E.68A during 2010-2015 and 2016-2021, respectively, with R² and Nash-Sutcliffe Efficiency values more than 0.60. The MODFLOW model was also well calibrated and validated with observation data from 16 groundwater wells during May 2021, and 2 groundwater observation wells of Department of Groundwater Resources during 2013-2021, respectively, with r greater than 0.95 and Normalized Root Mean Square Error less than 10%. Future climate analysis (2022-2099) was based on Regional Climate Models (CNRM-CM5, CanESM2, and GFDL-ESM2M), under RCPs 4.5 and 8.5 scenarios. The maximum and minimum temperatures under RCP 4.5 were 34.54 °C and 21.07 °C, respectively, while under RCP 8.5, both temperatures were 35.30 °C and 20.36, respectively. The future mean temperature tended to be higher than present temperature (32.35 °C and 19.28, respectively). The future mean annual rainfall, which were 1,246.37 mm/year and 1,250.01 mm/year under RCPs 4.5 and 8.5, respectively, were lower than the mean annual rainfall recorded between 2002-2021 (1,257.00 mm/year). The surface water under RCPs 4.5 and 8.5 were lower than the present condition (2,175,988,582 m³/year), while the future groundwater supply was increased from the present (424,418,714 m³/year). When annual surface water supply was compared with water demand, no water shortage was expected under present condition, while low to moderate levels of water shortage were identified under RCPs 4.5 and 8.5. When compared annual surface and groundwater supply with the demand, no water shortage was detected under present and future conditions. Finally, the obtained results will be useful for surface and groundwater management, in which water-related problems can sustainably be solved.

Analysis of factors hindering labor productivity in large-scale high-rise building construction projects

2026-01-28T11:12:05+07:00

Thailand faces persistent challenges in its labor market, notably low and declining labor productivity, labor shortages, and skill mismatches in the construction sector. Over the past decade, national development has focused more on capital investment and labor quantity than on productivity improvement. This study aims to address this gap by developing and validating a Structural Equation Model (SEM) to examine relationships among key labor productivity indicators in Thailand’s construction industry, particularly large-scale high-rise projects. Data were collected from 600 construction workers employed in residential projects across Bangkok and surrounding provinces, representing a population of 1,125,400 workers (2013–2019). The SEM incorporated nine latent constructs: Materials, Equipment/Tools, Labor, Safety, Construction Methods, Rework, Weather, Motivation, and Productivity that capture both resource-related and human-factor dimensions. The validated model demonstrated a good fit with empirical data, with all observed variable correlations significant at the 0.05 level. Motivation was identified as the most influential factor on labor productivity (total effect = 0.680), followed by Equipment/Tool performance (0.483), Labor Management (−0.049), and Resource Management/Working Conditions (−0.066). Collectively, these factors explained 92.7% of the variance in productivity. Indirect effects through Motivation accounted for 51.7% of its variation. Findings underscore the crucial role of worker motivation in improving productivity. Housing support had the most substantial positive influence on Motivation, explaining 75.7% of its variance. Construction managers should prioritize motivational strategies, particularly housing support, project-end bonuses, and social insurance, to enhance workforce satisfaction and productivity in Thailand’s construction sector.

Predictive maintenance of industrial milling machine using machine learning techniques

2025-08-19T15:56:04+07:00

Condition-based predictive maintenance of industrial machinery is a key area of research in the present world looking towards Industry 4.0. Machine learning (ML) techniques can have tremendous impact in this aspect because of their robust predictive modeling capabilities. The present paper aims to determine the optimized machine learning technique for the predictive maintenance of an industrial milling machine. The data pertaining to the operating parameters and the failure types of the machine is obtained from a public dataset with 10,000 data points. Five of the most popular classification ML algorithms namely, Artificial Neural Network (ANN), Discriminant Analysis (DA), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) techniques are implemented for the dataset to determine their optimized hyperparameters for an effective prediction of the machine failure type. DT and ANN were found to be the two best techniques with overall accuracy of 99.15% and 98.8%, respectively, and superior performance metrics of Precision, Recall and F-Measure compared to the other models. The results obtained from the present study may be enriched in the future by incorporating deep learning-based models and hybrid ML and intelligent optimization techniques for effective predictive maintenance of various industrial systems. The present approach can thus be employed in real-time factory settings to realize the targets of Smart Manufacturing and Industry 4.0.

Performance evaluation of travel demand forecasting models for transportation network analysis

2025-06-08T18:31:50+07:00

This paper presented a performance evaluation of travel demand forecasting techniques on transportation networks in Upper Northern Provincial Cluster 2, Thailand. The study compared multiple regression analysis and four-step sequential decision models. The findings revealed that the four-step sequential decision model forecasted person-trip generation in the study area to be 346,506, 373,422, 404,356, and 440,132 person-trips/day for the years 2029, 2034, 2039, and 2044, respectively. In comparison, the multiple regression model predicted approximately 320,245, 328,678, 338,123, and 349,567 person-trips/day for the same years, showing differences of 8.20%, 13.61%, 19.59%, and 25.91%, respectively. This variation can be attributed to the four-step sequential decision model's superior capability in comprehensively considering the impacts of future infrastructure development projects in the area compared to the multiple regression model. While both models forecast total person-trip generation, the four-step model additionally provides spatial distribution, modal allocation, and network assignment of these trips, enabling detailed analysis of traffic volumes on specific corridors. However, when evaluating model development convenience and time requirements, the multiple regression analysis approach offers faster problem-solving capabilities due to its more straightforward development process, while providing reasonably accurate forecasts of total person-trips.

Evaluation of the impact of tool geometry on thrust force and bearing strength of ramie woven composite

2025-06-08T18:29:33+07:00

This research evaluates the impact of tool geometry on thrust force, bearing strength, and failure modes in ramie woven composite materials. Four types of tool geometries were tested: Brad and Spur Drill (BSD), Twist Drill (TWD), End Mill Centre Cut (ECC), and End Mill Centre Hole (ECH). The thrust force measurement results show that the ECC device generated the highest thrust force of 148.17 N with the lowest bearing strength of 78.00 MPa. Conversely, the lowest thrust force was observed in the TWD device at 60.52 N, which coincided with the highest bearing strength of 98.54 MPa. This condition indicates that materials with higher bearing strength tend to be stiffer, reducing the amount of thrust force that can be effectively transferred. The failure modes observed after the bearing tests indicated that all specimens experienced net tension, with damage variations depending on the tool geometry used. These results directly benefit industries utilizing ramie composites, such as the automotive, aerospace, and environmentally friendly manufacturing sectors. By reducing material damage during the machining process and improving mechanical efficiency, this research can help enhance final product quality, extend components' service life, and reduce production costs.

Enhancing the energy efficiency of traditional brick kilns through sustainable insulation with rice husk ash and wood ash

2025-07-29T11:32:30+07:00

This study investigates the use of rice husk ash (RHA) and wood ash (WA) as sustainable thermal insulators to enhance the energy efficiency of traditional brick kilns. As readily available byproducts of agricultural and biomass combustion processes, RHA and WA are low-cost materials that support circular economy practices. Their favorable physical properties, including low thermal conductivity, high porosity, and reactive silica content, make them suitable for use as insulation for kiln walls. Experiments were carried out using a scaled-down downdraft open-top kiln to evaluate the thermal performance across various wall thicknesses and compaction levels. The results indicated that WA, particularly in a 15 cm loose-fill configuration, achieved the lowest heat loss (23.24 MJ) and the highest thermal efficiency (54.42%), representing a 15.09% improvement compared to the control. This approach to insulation also reduced the unit energy cost per brick from 0.00487 to 0.00414 USD, yielding an estimated annual fuel saving of 588 USD and a payback period of 1.47 years (~20 batches). In addition to energy savings, the reuse of RHA and WA reduces landfill waste, mitigates reliance on virgin insulation materials, and contributes to emission reductions, potentially lowering the CO₂ output by up to 750 kg per year for small-scale kilns. These findings confirm that incorporating RHA and WA into kiln construction is a viable, cost-effective strategy for improving sustainability in artisanal and semi-industrial brick production. The results are scalable and adaptable to various geographical and climatic contexts, thereby supporting broader adoption in developing regions.

Characterisation and quality improvement of binder free bio-pellets from the sugar industry residues and grass jelly food wastes for energy purposes

2025-05-01T11:03:07+07:00

Torrefied pelletised biomass wastes can be a sustainable and efficient solid fuel; however, the addition of binders is frequently required to improve the consistency, durability and overall quality of the pellets. Such additional processes can increase production costs, reduce stability on exposure to moisture, increase ash content and may reduce the heating value of the fuel. Therefore, the production of durable binder free bio-pellets would create a sustainable and economically viable route for producing these solid fuels. Herein, the binder free pelletisation of waste sugarcane leaves and/or grass jelly leaves/stalks from the agricultural industry was investigated for use as bio-based solid fuels. Importantly, the resulting pellets had a diameter, length, moisture content, unit density, bulk density, calorific value and durability that met the international standards (DIN 51731, SS 187120 and CEN/TS 14961) and Thailand standard (TIS 2772–2560). However, the ash content for grass jelly leaves/stalks pellets was 16%, while the blended pellets of 50:50 sugarcane:glass jelly was 7%, both of which were higher than that required by the Thailand standard. The calorific value of the pellets was improved via a torrefaction process at 200 – 300^oC for between 15 – 60 min. Yields of pellets dropped with increasing temperature and residence time, while the calorific value increases as the torrefaction severity increases from 16,630 – 26,334 kJ/kg. Crucially, this is the first reported pelletisation and torrefaction of sugarcane leave, grass jelly leaves/stalks or a 50:50 blend of these wastes to yield bio-based fuels with calorific values comparable to coal-like fuel pellets. Moreover, the mechanical strength of obtained pellets was still maintained without the use of an additional binder, thus reducing additional processing steps and potential cost. The optimal operating conditions for torrefaction were 250°C for 30 min, resulting in the greatest integrity, calorific value, enhancement factor and energy yield.

Development of water hyacinth reinforced jackfruit-seed-starch bi-layerd composites for sustainable thermal insulation

2025-06-20T13:28:06+07:00

This study investigates the development of biodegradable composite materials using water hyacinth pulp and jackfruit-seed-starch as a binder, aimed at providing an eco-friendly solution for thermal insulation applications. The composites were fabricated using a compression molding process with varying starch contents (10%, 20%, 30%, and 40%) under controlled temperature and pressure. Mechanical properties such as tensile strength, flexural strength, impact strength, hardness, and thermal conductivity were evaluated. The results indicated that composites with 30% jackfruit seed starch exhibited the best mechanical performance, including tensile and flexural strength, along with favorable thermal conductivity. However, water absorption remained a challenge, with higher starch content leading to increased moisture uptake. The findings highlight the potential of these composites for thermal insulation, particularly in extremely cold countries, where they could serve as a sustainable alternative to conventional materials. Further studies are needed to reduce water absorption and enhance the durability of the composites for long-term use.

Effect of curing methods on compressive strength of pervious concrete containing silica fume and calcium carbonate

2025-03-31T16:41:39+07:00

Concrete curing plays a critical role in the development of compressive strength, particularly in pervious concrete, which is highly susceptible to moisture loss due to its porous structure. This study investigates the effects of different curing methods on the compressive strength of pervious concrete and examines how the incorporation of silica fume (SF) and calcium carbonate powder (CC) influences the curing sensitivity index (CSI). Experimental results indicate that water curing consistently yields the highest compressive strength across all pervious concrete mixes at 7 and 28 days, followed by plastic and air curing. The presence of silica fume increases CSI, making pervious concrete more dependent on curing conditions, particularly under air curing. In contrast, calcium carbonate powder reduces CSI, enhancing curing efficiency and mitigating sensitivity to curing variations. Notably, a ternary blend of silica fume and calcium carbonate significantly lowers CSI at early ages, indicating improved curing resilience. However, at 28 days, the effect of CC in mitigating curing sensitivity diminishes slightly, while SF continues to increase curing dependency. These findings suggest that optimal curing strategies should be tailored to the binder composition, with calcium carbonate powder proving effective in stabilizing curing sensitivity. The results contribute to developing more durable and sustainable pervious concrete mixes with enhanced performance under variable curing conditions.

Optimizing the pork supply chain: A model integrating feed production and pig farming with outsourcing and subcontracting costs

2025-02-24T14:31:18+07:00

This study introduces a mathematical model designed to optimize the vertically integrated pork supply chain by addressing key cost factors, including pig farming, feed production, and outsourcing. The model integrates pig fattening and feed production stages, incorporating essential cost components to synchronize farming schedules with feed production plans while minimizing total costs. Computational experiments, using data from an empirical study of Thailand's vertically integrated pork supply chain, were conducted to evaluate the model's efficiency and sensitivity under varying farm sizes and planning horizons. The results demonstrate that the model effectively identifies optimal solutions for shorter planning periods (up to 14 months). However, extended planning horizons and larger farm sizes significantly affect solution times and the quality of feasible solutions. These findings provide valuable insights and practical strategies for pork production companies seeking to enhance cost efficiency and improve supply chain sustainability. Future research should focus on developing advanced heuristics and exploring additional supply chain dynamics within integrated environments.

Ensemble machine learning-based PM2.5 modeling using hotspot counts (0-1000 km) reflecting Chiang Mai, Thailand’s extreme pollution

2025-05-07T13:25:58+07:00

Persistent local and transboundary smog has critically elevated PM2.5 levels in Northern Thailand over the past decade, resulting in significant health risks. The spatial distribution of hotspot counts, indicative of biomass burning and smoke dispersion, demonstrates a strong correlation with PM2.5 concentration patterns, underscoring the importance of incorporating such data into air quality analyses. This study integrates hotspot data to capture both temporal dynamics and external influences in PM2.5 prediction models. The importance of lagged hotspot counts within 100–1000 km of Chiang Mai—ranked as the world’s most polluted city during the study period—and lagged ground-level PM2.5 is assessed using Lasso regularization. The analysis reveals that the cumulative effects of hotspots extend their influence on air quality in Chiang Mai up to approximately 700 km. Advanced tree-based ensemble machine learning methods, including Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost), are implemented alongside the Long Short-Term Memory (LSTM) deep learning model to evaluate their predictive performance. This approach provides a novel framework for PM2.5 modeling in Northern Thailand. Five key features with specific day lags were identified for modeling. These include PM2.5 at lag 1, short-range hotspots within 100 km at lags 1 to 3, mid-range hotspots at 200 and 400 km at lags 2 to 4, and long-range hotspots beyond 700 km at lag 5. Incorporating hotspot data improved model performance by approximately 20%, as evidenced by error metrics and residual analysis. Among the models tested, GB outperformed XGBoost, RF, and LSTM, achieving the highest R² (0.97), lowest RMSE (5.49), MAE (2.08), and MAPE (5.8%), along with near-zero MBE and minimal MdAE (0.48). Statistical validation confirmed the model’s reliability with no significant bias.

Banana quality classification using lightweight CNN model with microservice integration system

2025-04-08T14:40:55+07:00

Banana sorting has been performed manually, which often leads to human error due to the high volume and diverse characteristics involved. This paper presents a banana quality classification system using ConsolutechMobileNetV2 (CST-MobileNetV2) to classify banana ripeness into four categories unripe, ripe, overripe, and rotten. A lightweight deep learning model is proposed and integrated with a uniquely designed microservice system to optimize performance while minimizing computational demands. A publicly available dataset containing 13,478 images was used, and the data split into 56% for training, 14% for validation, and 30% for testing. Image normalization and augmentation techniques were applied to enhance the model's robustness. The model's performance was evaluated using a confusion matrix, achieving 98% precision, recall, and F1-score. The proposed model was compared with other deep learning models to benchmark its performance and deployed in different operating systems to evaluate its flexibility and capabilities. The LINE platform was employed as the user interface, enabling practical interaction with users. The system also demonstrated an average response time of 9.25 seconds per image, ensuring efficient processing, delivers high accuracy and scalability making it a practical and efficient solution for automated banana quality classification.