Engineering and Applied Science Research

Mesoporous carbon-silica composite from low-cost vinasse for the effective adsorption of methyl orange and methylene blue dyes from aqueous systems

2026-08-17T16:07:33+07:00

The adsorption behaviour of anionic and cationic dyes, namely methyl orange (MO) and methylene blue (MB), respectively, has been studied on carbon-silica composites. These composites were produced through the valorisation of waste vinasse from industrial sugar production as the carbon feedstock, while the silica production was achieved via a simple sol-gel method using low-cost sodium and potassium silicate solutions (Na₂SiO₃ and K₂SiO₃) or higher-cost tetraethyl orthosilicate (TEOS). Use of Na₂SiO₃, K₂SiO₃ and TEOS, led to composites with specific surface areas of 375, 343 and 656 m²/g and % mesoporosity of 79.45, 94.35 and 82.93%, respectively. Adsorption of MO is reduced at higher pH, but MB removal was enhanced under basic pH. Kinetic results demonstrated a strong correlation with the pseudo-second order model, and it was noted that intraparticle diffusion had a key role to play in the mechanism of mass transport. Adsorption data fitted the Sips isotherm model, with excellent adsorption capacity of MO (844.63 mg/g for Na₂SiO₃, 979.06 mg/g for K₂SiO₃, 1161.65 mg/g for TEOS) and MB (514.30 mg/g for Na₂SiO₃, 385.34 mg/g for K₂SiO₃, 294.88 mg/g for TEOS). Elevated temperatures led to preferential removal of the acidic and basic dyes, suggesting an endothermic adsorption process. Carbon-silica composites derived from vinasse are promising materials for the remediation of MB and MO from aqueous systems, exhibiting comparable or higher surface area and adsorption capacities for both dyes relative to other reported materials, while being synthesized through simple preparation processes from low-cost agricultural by-products.

Software selection and requirement specification for technology licensing fee management system using AHP and QFD approach

2026-08-18T17:09:09+07:00

Technology licensing fee management in university science and technology parks faces critical challenges including data loss, inefficient royalty tracking, and revenue leakage from spreadsheet-based systems lacking automated notifications and centralized data management. This study develops an integrated framework combining Analytic Hierarchy Process (AHP) with Quality Function Deployment (QFD) for systematic software selection and requirement specification in technology licensing contexts. Conducted at a science and technology park, Chiang Mai University in Thailand, five technology transfer experts validated fourteen initial selection criteria through Index of Item-Objective Congruence (IOC), retaining nine criteria for evaluating three alternatives through AHP pairwise comparisons: customized Booked ME solution, commercial CRM system, and Google Workspace integration. Subsequently, seven end-users and three technical consultants conducted QFD analysis to translate fourteen validated user requirements into twelve technical specifications via House of Quality framework. AHP analysis prioritized security (0.21) and initial cost (0.20) as dominant factors, identifying the customized Booked ME solution as optimal (0.43) over commercial CRM (0.28) and Google-based solutions (0.29). QFD analysis revealed validated calculation algorithms (14.38%), web-based architecture (13.92%), and workflow simplification (11.88%) as highest-priority technical characteristics. The research contributes by: (1) extending AHP-QFD to technology licensing fee management, a novel domain combining financial, legal, and IP management; (2) providing empirical evidence from an emerging economy that platform customization outperforms commercial solutions when security and cost dominate; and (3) developing a replicable integrated framework bridging strategic platform selection with tactical requirement specification.

Hybrid RSM-CCD and ANN-GA optimization of mechanical properties in permanent-backing-plate-assisted GMAW dissimilar welding of AISI 304L and SS400 steels

2026-08-13T17:04:00+07:00

Dissimilar welding between AISI 304L stainless steel and SS400 carbon steel is widely applied in structural and industrial systems, yet differences in thermal and metallurgical properties often lead to heterogeneous microstructures and unstable mechanical performance. This study investigates the mechanical behavior and fracture characteristics of dissimilar AISI 304L–SS400 joints produced by Gas Metal Arc Welding (GMAW) with a permanent backing plate using a hybrid RSM–CCD + ANN–GA modeling and optimization framework. Welding current, arc voltage, and travel speed were selected as the key process parameters and optimized with respect to tensile strength, yield strength, and elongation. Statistical analysis revealed that welding current is the dominant factor controlling mechanical responses through its influence on heat input and weld penetration. The permanent SS400 backing plate introduces a tri-material heat conduction path (304L–SS400–SS400), modifying heat flow and thermal gradients within the weld region. Although the RSM–CCD quadratic models captured the general response trends, the Artificial Neural Network (ANN) provided superior predictive accuracy with lower RMSE, MAE, and MAPE values. Integration of ANN with a Genetic Algorithm enabled efficient multi-response optimization, identifying an optimal welding condition of approximately 170 A, 22.5 V, and 170 mm/min, which was experimentally validated. SEM fractography confirmed ductile micro-void coalescence under the optimized condition and mixed-mode fracture under detrimental parameters. The proposed hybrid framework provides a reliable approach for predicting and optimizing welding parameters in dissimilar GMAW systems with permanent backing plates.

Geotechnical and environmental evaluation of cement-stabilized lateritic soil with wood bottom ash for road subbase

2026-08-19T16:53:07+07:00

This article investigates the characteristics of the marginal lateritic soil stabilized with cement and wood bottom ash (WBA) as subbase materials. A comprehensive methodology involved modified Proctor compaction and unconfined compression tests, supplemented by microstructural analyses, as well as pilot-scale field construction and leachate monitoring. Results revealed that compressive strength increased with WBA content up to 60%, and was further enhanced by higher cement dosages. Compressive strength was inversely correlated with porosity and voids-to-cement ratio. Field monitoring showed that Cr was the only metal consistently detected in leachate, decreasing over time but remaining above permissible limits, indicating insufficient immobilization due to weaker incorporation into cementitious phases. This study highlights the synergistic effects of WBA and cement in improving soil subbase performance while supporting sustainable reuse of industrial by-products.

Banana peel-derived carbon for supercapacitor electrodes: Carbonization temperature-dependent structural and electrochemical properties

2026-08-13T10:22:04+07:00

Banana peels (Musa ABB, Namwa cultivar) were utilized as sustainable precursors for the production of carbonaceous materials for supercapacitor electrodes. Carbonization was performed at temperatures ranging from 500 to 1000 °C. During carbonization, CO₂ and H₂O were released, promoting physical self-activation and pore development. X-ray diffraction confirmed the presence of KCl and K₂CO₃ phases, which were removed after washing with HCl and deionized water, leaving only amorphous carbon. Iodine adsorption analysis revealed that increasing carbonization temperature enhanced porosity, with iodine adsorption values of -10.42, 194.20, 150.04, 485.80, 471.44, and 325.09 mg·g^-1 at 500, 600, 700, 800, 900, and 1000 °C, respectively. This trend is in agreement with the change in surface area with carbonization temperature, which increased from 500 to 800 °C (382.403–800.846 m²·g^-1) and decreased at 900 and 1000 °C (684.595 and 156.881 m²·g^-1, respectively). The carbon obtained at 800 °C (CC800) delivered the highest specific capacitance, 169 F·g^-1 at 0.5 A·g^-1 in a 1 M H₂SO₄ electrolyte, owing to its highest specific surface area (800.846 m²·g^-1). Additionally, the redox peaks associated with surface functional groups formed during carbonization would further enhance the capacitance of all carbons. These results demonstrate that banana peel-derived carbon, synthesized via carbonization, exhibits a high surface area and excellent charge storage capability.

Impact of co-existing ions on arsenic remediation using green nano zero-valent iron supported on nanocellulose: A fractional factorial design study

2026-06-30T09:36:17+07:00

This study focused on developing a novel adsorbent material of green nano zero-valent iron supported by nanocellulose (G-NZVI/NCC) from agricultural waste for the effective removal of arsenic. The adsorption isotherm for arsenite (As³⁺) and arsenate (As⁵⁺) on G-NZVI/NCC showed maximum capacities of 4.2123 and 4.9579 mg·g^‒1, respectively. The model indicated that As³⁺ forms a multilayer on a heterogeneous surface, while As⁵⁺ forms a monolayer on a homogeneous surface. The Gibbs free energy for the adsorption of As³⁺ and As⁵⁺ on G-NZVI/NCC from water demonstrates a non-spontaneous process at higher temperatures for As³⁺, but it occurs spontaneously for As⁵⁺. Furthermore, most previous studies have largely overlooked the influence of co-existing ions in water, or have investigated them individually. Consequently, this study aimed to simulate real-world conditions by examining the simultaneous effects of all relevant ions. This comprehensive approach is critical for the practical implementation of the system, and statistical principles are employed for data analysis and interpretation. However, surface water contains competing ions such as calcium (Ca²⁺), phosphate (PO₄^3‒), bicarbonate (HCO₃^‒), chloride (Cl^‒), and sulfate (SO₄^2‒) that can interfere with arsenic adsorption. A 2^5‒2 fractional factorial design was used to systematically evaluate individual factors, significant parameters, and their interactions on arsenic removal efficiency. The results revealed that Ca²⁺, PO₄^3‒, and HCO₃^‒ play crucial roles in arsenic removal, with Ca²⁺ and PO₄^3‒ enhancing removal efficiency, while HCO₃^‒ exhibits inhibitory effects. Notably, high PO₄^3‒ concentrations unexpectedly enhanced arsenic removal compared to previous studies, attributed to the synergistic physical and chemical adsorption properties of iron oxide and hydroxide phases. Mechanistic analysis revealed that As⁵⁺ can substitute PO₄^3‒ in vivianite formation, creating a vivianite-symplesite solid solution (Fe₃(PO₄)_2.7(AsO₄)_0.3·8H₂O), providing new insights into arsenic immobilization mechanisms under realistic water chemistry conditions.

Mechanical and hydraulic performance of porous asphalt concrete containing recycled PET plastic granule for permeable pavements

2026-08-21T10:34:35+07:00

This research examines the mechanical and hydraulic properties of porous asphalt concrete (PAC) modified with recycled polyethylene terephthalate granules (G-PET) to enhance the performance of permeable pavement materials. Marshall specimens of the mixture containing various percentages of G-PET of 0%, 5%, 10%, 15%, 20%, and 40% (by weight of asphalt binder) were subjected to Marshall stability, indirect tensile strength, ultrasonic pulse velocity, Cantabro loss, draindown, moisture damage, and permeability tests. The experimental results show that adding G-PET improves pore uniformity, thereby increasing permeability. However, excessive G-PET addition led to higher porosity and reduced stiffness due to particle agglomeration and weak interfacial bonding of the mixture. Quantitative results showed that the 10% G-PET mixture provided the most favorable balance, meeting volumetric, mechanical, and permeability criteria within functional specifications for permeable pavements. This finding fills a gap in sustainable pavement design by developing an experimentally validated method to incorporate recycled plastics into porous asphalt mixes, thereby supporting circular-economy goals for infrastructure materials.

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

2026-06-23T10:13:50+07:00

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.

AI-assisted power quality improvement in PV systems using RNN-based DSTATCOM and HiClampCasBoost converter

2026-07-31T09:59:26+07:00

The rapid deployment of Photovoltaic (PV) systems in modern power distribution networks has caused major issues related to voltage fluctuations, reactive power unbalance, harmonics, and less efficient systems. It is critical to protect Power Quality (PQ) under these conditions to ensure safe and consistent operation of grid. Flexible AC Transmission System (FACTS) devices are used to mitigate those problems encountered in the network. Hence, this work proposed a grid-tied PV system integrated with Distribution Static Compensator (D-STATCOM) device that help accurate voltage sag, swell, and harmonic distortion at the distribution level. In this work, the interaction of D-STATCOM with the grid is controlled by a Recurrent Neural Network (RNN) based controller with PWM signals adjusted for voltage stability, sag, swell and load disturbances compensation. Further, a high-gain HiClampCasBoost Converter (HiClampCasBoost) converts low PV output voltage and regulates the DC-link, assuring constant output of the D-STATCOM while under voltage conditions prevail. Additionally, a Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) controller, optimized using the Hummingbird Optimization Algorithm (HOA) for tracking Maximum Power Point (MPP) of PV array under dynamic solar conditions to produce maximum energy from PV array whilst ensuring D-STATCOM DC-link stability. The MATLAB simulations show that the developed system enhances PQ and grid stability in comparison to conventional methods. The proposed system has several benefits, such as superior voltage regulation, reduced THD of 1.63%, superior converter efficiency of 96%, tracking efficiency of 99% superior dynamic operation performance, etc.

Optimizing layering configurations for enhanced flexural and impact performance in perforated Al foil/glass fiber/epoxy hybrid laminates

2026-06-30T09:55:38+07:00

Lightweight panels for aircraft, vehicles, and protective structures need to absorb damage without a large weight penalty. In practice, many fiber metal laminates (FMLs) still struggle with shaping or curvature and can delaminate early, largely because the metal plies are relatively thick and the metal/polymer interface is prone to separation. An alternative hybrid laminate was developed using thin perforated Al foil (A) (~0.1 mm; perforations ~150 μm) combined with woven glass fiber (G)/epoxy, and the influence of stacking sequence on flexural and impact behavior was examined. Five configurations were manufactured by hand lay-up and evaluated using three-point bending (ASTM D790) and unnotched Charpy impact (ASTM D6110). Fracture surfaces and interfacial features were examined by optical microscopy and SEM. The laminates showed a clear, design-driven performance balance between bending strength and impact resistance. The symmetric, staggered lay-up V4 (GGGAAAGAAAGGG) delivered the highest flexural strength (229.74 MPa), whereas the metal-rich core V3 (GGGAAAAAAGGGG) produced the highest impact resistance (123 kJ/m²). Microstructural characterization indicates that resin flow through the perforations forms mechanical “anchors” at the Al foil/epoxy interface, limiting adhesive-type separation and promoting more progressive damage through cohesive cracking and controlled delamination. The results suggest that perforated Al foil/glass/epoxy hybrids offer a practical route to tune stiffness and toughness for lightweight skins/panels and protective components requiring high bending performance with moderate-to-high impact resistance.

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

2026-03-24T10:11:35+07:00

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

2026-04-29T17:31:28+07:00

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.

A structured maturity model for evaluating smart logistics parks transformation: Case study in western China

2026-06-30T22:05:47+07:00

The transformation of traditional into smart logistics parks (SLPs) requires a structured framework for assessing maturity and guiding development. Existing maturity models, often adapted from smart city or industrial park frameworks, lack specificity and empirical validation for the logistics park context. To address this gap, this study constructs and validates a data-driven maturity model for SLPs by integrating a literature review, expert consultation, and case studies of two logistics parks in western China. The model comprises five dimensions—Smart Economy, Public Services and Smart Governance, Infrastructure and Intelligent Technology Application, Skilled Human Capital, and Environmental Sustainability—and defines 20 measurable factors. These factors are evaluated through a four-level maturity framework progressing from Initial to Optimization stages, corresponding to the conventional levels of Initial, Defined, Managed, and Leading. The application revealed that both parks were positioned between the Initial and Growth stages. Logistics Park 2 excelled in digital infrastructure and technology adoption, as well as workforce digital literacy, whereas Logistics Park 1 enjoyed advantages in operational ROI and stakeholder integration. While for both parks, Public Services and Smart Governance reached the Growth stage, Smart Economy and Environmental Sustainability lagged, reflecting reliance on volume-based growth and underdeveloped sustainability outcomes. The model differentiated between transitional and emerging SLPs, proving its value as a diagnostic and strategic tool. One limitation, however, is the model's initial validation within the two case studies in western China, and future research should expand the application to diverse contexts to enhance generalizability and robustness.

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

2026-06-17T15:11:52+07:00

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.

Modification of sulfur spring bitumen with Gilsonite: A step towards exploiting natural bitumen in flexible pavement production

2026-07-16T13:49:42+07:00

In recent years, there has been increasing interest in employing Natural bitumen (NB) as a modifier or bitumen in road construction due to its exceptional properties. This study investigates the ability to produce new asphalt binders by mixing two types of NB. In this work, NB from deposits in the western region of Iraq is selected. It consisted of two types, including the sulfur spring bitumen (NB-SS) and Gilsonite bitumen (NB-G). NB-SS modified with NB-G in percentages of 40, 50, and 60% by the weight of NB-SS. Conventional asphalt tests were conducted to examine the physical properties of bitumen. Furthermore, chemical tests, including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX), and Fourier Transform Infrared spectroscopy (FTIR), were performed to examine the morphology and the chemical composition of bitumen. To examine the behavior of the asphalt mixture, the Marshall test was conducted to evaluate the mechanical and volumetric properties. In addition, the Cantabro test was performed in dry and wet conditions to assess the durability of PA and modified NB mixtures. The results reveal that NB-G can effectively harden NB-SS and improve its overall properties. Based on chemical tests, the microstructure of NB-SS and refinery asphalt is mostly flat, but it is more complex for NB-G. Moreover, NB-G has a high content of trace elements, and new functional chemical groups are produced in modified NB samples. Additionally, 50% of NB-G is the optimal blend content. Compared to a conventional mixture, modified NB-50% has a stiffness 34% higher with lower resistance to raveling, especially in dry conditions. However, the modified NB-50% mixture is more resistant to raveling in a moisture condition by approximately 52%. In summary, using NB-G as a modifier is an effective way to enhance the properties of NB-SS and make it more appropriate to use in asphalt pavement.

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

2026-04-23T13:34:33+07:00

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

2026-06-30T09:44:58+07:00

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.

Life cycle assessment and management of carbon emission from reinforce concrete construction projects in Thailand

2026-08-14T09:23:53+07:00

This study presented the comprehensive analysis and management to reduce carbon emissions in Thailand's reinforced concrete building construction projects using Structural Equation Modelling (SEM). Data were collected via questionnaires from experts certified by the Thailand Green Building Institute. The SEM analysis, which demonstrated good model fit, identified three main components: Material Manufacturing, Transportation, and Construction Processes. Structural work exhibited the highest factor loading, whilst electricity uses in construction showed the lowest. Notably, the study revealed the negative relationship between material manufacturing and transportation processes, challenging conventional assumptions. These findings provide crucial insights for designers and industry professionals in developing targeted strategies to reduce carbon emissions. The study's limitations include its focus on the Thai context and a specific set of variables. Future research should explore additional factors, such as renewable energy integration, and conduct longitudinal studies to refine our understanding of emission drivers in the construction sector.

The innovative simple water filter for producing utility water using activated carbon from rubber tree branches

2026-08-13T15:49:17+07:00

This study presents the development of a cost effective and an innovative household water filtration system utilizing activated carbon derived from locally sourced rubber tree branches. Aimed at improving tap water quality in the Ban Kut Saeng Community, Sakon Nakhon Province, Thailand, the research promotes sustainable material use and simple technology to enhance the availability of clean water in underdeveloped and developing regions. Activated carbon was produced through a dry activation process using NaOH, yielding high adsorption capacity 687.24 mg I₂/g for iodine and complete (100%) methylene blue removal at 100 ppm. BET surface area analysis indicated a value of 833.06 m²/g, with the pore structure exhibiting both microporous and mesoporous characteristics. The filtration performance was evaluated by comparing raw and treated water quality based on the standards of the American Public Health Association (APHA), American Water Works Association (AWWA), and Water Environment Federation (WEF), covering physical, chemical, heavy metal, and microbiological parameters. The raw water has shown high turbidity and bacterial contamination. Following two-stage filtration, Sample D exhibited the tap water quality standards, with turbidity of 9 NTU, apparent color of 2.1 Pt-Co, total dissolved solids (TDS) of 151 ppm, pH of 6.8, total hardness of 30 ppm as CaCO₃, and chloride ion concentration of 9.8 ppm. Total coliforms and E. coli counts were observed below the detectable limits (<1.1 CFU/100 mL), and heavy metals or other harmful ions found to be absent. The results have confirmed the system’s high efficiency in contaminant removal and water quality improvement, underscoring its potential as a low-cost, community-driven solution for household water treatment in the resource-limited areas.

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.

Current outlook and future potential of no-core fiber sensor technology based on plasmonic effect

2026-08-07T13:39:55+07:00

This paper reviews presents a review of the current status and future prospects of plasmonic-effect-based No-Core Fiber (NCF) sensor technology. The discussion covers the basic principles of plasmonic effects, namely Surface Plasmon Resonance (SPR) and Localized Surface Plasmon Resonance (LSPR), the sensing mechanism of NCF, and the current status of plasmonic-based NCF sensor development. Plasmon-based NCF sensors have been widely applied in various measurement applications, with the majority of research focusing on refractive index sensors. Most studies still utilize SPR rather than LSPR, considering the technical challenges in controlling the size and shape of metal nanoparticles in LSPR. Nevertheless, LSPR provides design flexibility that enables precise tuning of nanostructure parameters to improve sensor sensitivity. Some potential development directions include optimizing sensor design to improve sensitivity and selectivity, developing more controllable and reproducible fabrication methods, and exploring new plasmonic materials. With the advancement of fabrication technology and characterization methods, it is expected that the technical challenges in developing plasmonic-based NCF sensors can be overcome. This advancement will facilitate broader applications across diverse fields, including chemistry, biosensing, and environmental monitoring.

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

2026-04-29T17:16:39+07:00

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

2026-04-17T16:10:05+07:00

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

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.

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

2026-04-10T15:22:57+07:00

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

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.

Engineering visual–textual fusion for automatic item categorization in Indonesian e-commerce platforms

2026-06-30T09:36:16+07:00

In the modern digital era, e-commerce remains a fundamental pillar of the global economy. The rapid growth of online ventures has fundamentally reshaped how products are bought and sold. However, as the number of products on e-commerce platforms in Indonesia increases, the main challenge is accurately matching products to consumer preferences. While previous studies have primarily used unimodal approaches to product matching, relying on a single type of information, little research has examined how to recognize new products on e-commerce platforms. Utilizing a multimodal approach that integrates information from diverse data types, such as text and images, is increasingly appealing for improving product-matching quality. This study's main contribution is to create vector representations of image and text features using deep learning techniques, including Convolutional Neural Networks and Doc2Vec. It also involves merging features through a cross-modality approach using FeedForward Neural Networks and determining the appropriate parameters for clustering new products into existing clusters using Hierarchical Clustering. The research demonstrates that employing a multimodal approach that leverages text and image information can enhance product matching quality on e-commerce platforms in Indonesia, achieving a Normalized Mutual Information of 0.96.

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

2026-06-19T15:56:06+07:00

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

2026-04-29T10:17:10+07:00

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

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.