Suranaree Journal of Science and Technology

LONG-TERM MAINTENANCE COST FORECASTING AND WARRANTY BUDGET PLANNING FOR RESIDENTIAL BUILDINGS IN BANGKOK, THAILAND

2026-04-02T10:19:26+07:00

This study evaluates the adequacy of current warranty budget allocations in residential developments and proposes differentiated, data-driven percentages based on long-term maintenance cost projections and project characteristics. Empirical defect data from five condominium projects in Bangkok, varying in unit type, scale, and market positioning, were collected between 2019 and 2023 and used to model 30-year cost projections through linear regression. The findings show that maintenance costs consistently increase over time, with annual cost slopes ranging from USD 10,162 to USD 30,857 and R² values between 0.90 and 0.99, while projected lifetime maintenance costs represent 5.56% to 11.26% of total project values. The widely adopted 2% warranty allocation was found to be inadequate, depleting as early as year 11 in high-density or lower-cost projects. Based on cumulative cost behavior and project conditions, differentiated warranty budgets of 6% to 11% are recommended to ensure financial sustainability. While the analysis is limited to five projects in Bangkok and linear forecasting methods, the results underscore the importance of empirical, context-sensitive approaches to warranty planning. By integrating real defect data with long-term cost modeling, this research provides a practical framework for improving financial foresight, construction quality, and asset management in rapidly urbanizing regions.

SPRAY-PYROLYSIS ZnSe THIN FILMS WITH TUNABLE OPTOELECTRONIC PROPERTIES THROUGH BORON DOPING AND TEMPERATURE CONTROL

2026-04-01T14:47:08+07:00

Zinc selenide (ZnSe) is a wide-bandgap II – VI semiconductor with promising applications in photovoltaics, optoelectronics, and photonics. In this study, undoped and boron-doped ZnSe thin films were deposited via spray pyrolysis at substrate temperatures ranging from 250 to 450°C to evaluate the synergistic effects of temperature and dopant incorporation on structural, optical, morphological, and electrical properties. X-ray diffraction confirmed the cubic ZnSe phase with nanocrystalline features, and optimal crystallinity and preferred orientation were obtained at 0.3% B-doping. Optical analyses revealed strong ultraviolet absorption (<400 nm), with transmittance increasing up to 54% at 450°C and a systematic bandgap widening from 3.64 to 3.75 eV, attributed to improved crystallinity and reduced defect density at elevated growth temperatures. Reflectance values remained below 4% across all films, with an exceptionally low value of 0.1% at 350°C, underscoring their potential for antireflection coatings. SEM analysis showed distinct morphology transitions from flower-like structures in undoped films to compact agglomerates upon boron incorporation, enhancing film density. Hall effect measurements indicated that mobility increased with temperature and was further modulated by boron doping, with a balance achieved between conductivity and transparency.

A HYBRID DEEP LEARNING APPROACH FOR GROUNDWATER LEVEL FORECASTING USING MULTIVARIATE GEOSPATIAL AND METEOROLOGICAL DATA

2026-05-28T13:45:56+07:00

Groundwater level prediction is a major challenge due to the complex and nonlinear behavior of hydrological systems, particularly in regions such as Chhattisgarh, where groundwater is a primary source of irrigation and domestic supply. In this study, a hybrid Particle Swarm Optimization-Long Short-Term Memory (PSO-LSTM) model was developed for groundwater level forecasting and compared with traditional Artificial Neural Network (ANN) and standard LSTM models. The multivariate dataset included geographical coordinates, elevation, soil type, meteorological variables (precipitation, temperature, diurnal temperature range, and vapour pressure), and historical groundwater level observations collected from five major divisions of Chhattisgarh. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R²), and Cosine Similarity (CS). The hybrid PSO-LSTM model achieved superior performance with testing values of MAE = 0.676, MSE = 0.678, RMSE = 0.824, R² = 0.876, and CS = 0.989, outperforming both ANN and standard LSTM models. The proposed model reduced RMSE by approximately 31% compared to ANN and 16% compared to standard LSTM, demonstrating its effectiveness in capturing nonlinear relationships and long-term temporal dependencies in groundwater systems. The empirical results of the proposed framework can be extended to other regions using relevant multivariate datasets, making it a reliable tool for groundwater management and planning. Accurate groundwater level prediction can support water harvesting, agriculture, industrialization, and sustainable water resource management. Therefore, these findings can significantly contribute to predicting the groundwater level effectively.

A HYBRID VAE-HUFFMAN GENERATIVE COMPRESSION FRAMEWORK FOR IMAGE TRANSMISSION IN WIRELESS SENSOR NETWORKS

2026-04-23T14:29:27+07:00

This paper proposes a hybrid image compression framework that integrates a tunable β-Variational Autoencoder (β-VAE) with Huffman entropy coding to achieve efficient, low-complexity compression suitable for wireless sensor networks (WSNs) and resource-constrained embedded systems. The model jointly optimizes latent representations and entropy-aware symbol distributions, improving both rate-distortion performance and theoretical coding efficiency. Trained on CIFAR-100 and ImageNet and evaluated on the Kodak dataset, the approach attains over 32 dB PSNR and SSIM above 0.90, and an average compression ratio of 18.22:1. When compared with the JPEG standard, the proposed method provides comparable perceptual quality while enabling deterministic, lightweight decoding that substantially reduces computational and energy demands. The results demonstrate that combining generative latent representation modeling with classical entropy coding offers a scalable, hardware-friendly alternative for next-generation learned image compression systems.

EFFECTS ON PROPERTIES OF EFUHPC MADE BY SUGARCANE BAGASSE ASH

2026-04-21T13:12:45+07:00

In this work we examine about sugarcane bagasse ash which satisfactory for producing Eco-friendly Ultra-High Performance Concrete (EFUHPC) and improving mechanical properties of EFUHPC. In UHPC more amount cement and other materials used which produce hug amount of CO₂, there are major problem for environment. Very less study found on use of SBA in EFUHPC hence we studied for effect of sugarcane bagasse ash on UHPC and futuristic use for EFUHPC. In this work Sugarcane Bagasse Ash by weight of cement used to 0%, 5%, 10%, 15% and 20% and workability, flexural strength, compressive strength and water absorption are investigated. The result show that mixture using 10% amount of SBA has optimum mechanical strength and water absorption decreased when SBA increased.

APPLICATION OF QUALITY FUNCTION DEPLOYMENT (QFD) TO DETERMINE THE REQUIREMENTS FOR PM2.5 DUST MANAGEMENT EQUIPMENT IN SAKON NAKHON PROVINCE

2026-04-17T16:23:56+07:00

This research aims to design for PM2.5 dust management innovation in Sakon Nakhon Province to meet the needs of the target group by applying the Quality Function Deployment (QFD) technique, which was applied through a needs assessment of 384 individuals across 18 districts of Sakon Nakhon Province, which has 2 steps as follows: Part 1: Collecting the demands of the target group by interviewing and organizing the target group’s demand factors using a linkage diagram to design a questionnaire and find the weight score of the importance of each factor of the product’s demand. Part 2: Building a quality house involves identifying the technical requirements, analyzing the relationships between these technical requirements and the target group’s demand factors, scoring the relationship values, and ranking the importance of the technical requirements within the House of Quality (HOQ). From the analysis, it was found that in Phase 1, the product planning matrix, the ranking of the importance weight value by comparing the highest score of the technical requirements is PM2.5 dust filter, followed by durable, strong structure, and finally easy to clean, with customer demand characteristics at 16.40, 13.96, and 3.07 percent, respectively. In Phase 2, the design transformation matrix The ranking of importance weights of the subcomponent requirements, with the most important value, is with the PM2.5 dust filter, followed by the machine being suitable for use, and finally the timer for operation, with the importance values of 19.82, 12.47, and 1.25 percent, respectively.

MODELING SUSTAINABLE CONCRETE BEHAVIOR USING FOUNDRY SAND, GENE EXPRESSION PROGRAMMING, AND MACHINE LEARNING METHODS

2026-06-16T09:53:32+07:00

Sustainable materials are increasingly used in the construction industry to reduce the environmental impact. One promising approach is the use of residual foundry sand (RFS), an industrial byproduct, as a partial replacement of natural sand in concrete. This helps to divert industrial waste away from landfills, conserve natural resources and encourage environmentally sustainable construction. The mechanical properties of RFS based concrete were predicted using advanced computational techniques like Genetic Programming (GP) and Machine Learning (ML). Predictive models for compressive strength (f’c), elastic modulus (Ec) and split tensile strength (fst) have been developed based on material properties, mix proportions and environmental factors. Four mathematical models were established on the basis of experimental data. Linear Model #1 gave an excellent prediction of the compressive strength, R2 = 0.99. Exponential Model #3 yielded R2 = 0.985. The linear model #2 was found to fit the elastic modulus very well (R² = 1.00) and the nonlinear model #4 was the most accurate (R² = 0.998). The predicted ranges were 20-50 MPa, 15-30 GPa and 2-5 MPa for the compressive strength, elastic modulus and split tensile strength respectively. The results indicate that GP and ML can successfully describe the complex behavior of RFS concrete, allowing for reliable property prediction and supporting sustainable concrete design and increased use of recycled industrial materials.

A STUDY ON NON DESTRUCTIVE TESTS AND MECHANICAL PROPERTIES OF CONCRETE USING MARINE SAND

2026-04-27T10:41:48+07:00

Developing countries like India are investing heavily in infrastructure, highway projects, metro projects, and smart cities as part of their plans to boost their economy. This growing building sector requires a substitute for river sand. This study advocates for the use of washed marine sand (MS) as a fine aggregate to partially or fully substitute M-Sand, aiming to minimize the exploitation of natural aggregates. This approach contributes to river conservation efforts and helps mitigate erosion and flooding. The objective of this experimental research is to analyze the mechanical characteristics of concrete and conduct Non-Destructive Tests (NDT) on concrete that incorporates washed marine sand as a partial substitute for M-Sand. The study evaluates concrete strength characteristics in line with the guidelines of IS 516-1959, focusing on crushing strength, split tensile strength and bending strength. Six concrete mixtures are tested, with washed marine sand replacing M-Sand at varying levels of 20% to 100%, labeled as M100, MS20, MS40, MS60, MS80, and MS100. From this experimental investigation, 60% of partial replacement of marine sand meets the required strength properties. Also, from this study a comparison is made between theoretical predictions and experimental findings.

OPTIMIZATION OF PERFORMANCE PARAMETER OF IC ENGINE POWERED BY BLENDED KARANJA BIODIESELS WITH DISTINCT INJECTORS USING TAGUCHI-BASED HYBRID APPROACHES

2026-04-08T20:52:24+07:00

Performance characteristics and emissions parameters of a Kirloskar diesel engine having single-cylinder compression ignition (CI) with direct injection (DI), water-cooled, running at 1500 rpm with variable load were tested in the blending percentage of Karanja methyl esters (KME) B10, B20, and B30 with diesel, the biodiesel made from Karanja oil, along with pure diesel. An experimental Taguchi L16 matrix is prepared for the analysis of performance, combustion, and emissions characteristics of the engine mentioned above with 3-hole, 4-hole, 5-hole, and 6-hole injection systems. Two hybrid methods are used for the optimization of engine parameters first one is the combination of Principal Component Analysis (PCA) and Utility method, and the other one is the combination of Criteria Importance through Criteria Inter-Correlation (CRITIC) and Utility method. The optimal factor level for higher brake thermal efficiency (BTE), lower brake-specific fuel consumption, and lower engine emission in blending conditions is achieved by having a fuel injector with a 6-hole nozzle in combination with a B20 blending condition at 60% loading condition. This optimal level condition provides higher BTE, lower SFC, and lower engine emissions compared to other conditions.

PHOTOLUMINESCENCE AND SCINTILLATION PROPERTIES OF CE-DOPED Lu0.5Gd0.5AlO3 SINGLE CRYSTALS GROWTH BY FLOATING ZONE METHOD

2026-05-19T15:20:47+07:00

Undoped and Ce-doped Lu_0.5Gd_0.5AlO₃ (LuGdAP:Ce) single crystals were grown by the floating zone method. The coloration of the samples faded by annealing under a reducing atmosphere, and the characterizations of the samples after annealing were evaluated. The characterizations included X-ray diffraction, photoluminescence (PL), and scintillation properties. In PL properties, the 0.3% Ce-doped sample exhibited a broad emission at 350–500 nm under 290 nm excitation, and the emission would be ascribed to 5d-4f transitions of Ce³⁺ ions. The undoped sample showed emission at 650-750 nm under excitation at 320 nm, and the emission would be due to charged oxygen vacancies. In scintillation properties, all the Ce-doped samples showed a broad emission at 360-500 nm, which came from 5d-4f transitions of Ce³⁺ ions. According to pulse height spectra under γ-ray irradiation from ¹³⁷Cs, the scintillation light yield (LY) of the 0.3% Ce-doped was 1,610 ph/MeV, and the energy resolution was 54.4%. The 0.3% Ce-doped samples showed the lowest afterglow level at 20 ms after X-ray irradiation for 2 ms among all the Ce-doped samples. Therefore, the optimal Ce-concentration was 0.3% in terms of the LY and afterglow.

PARTIAL PURIFICATION AND CHARACTERIZATION OF A NEWLY IDENTIFIED LECTIN-LIKE PROTEIN FROM ELEUSINE CORACANA (FINGER MILLET) SEEDS

2026-03-15T17:45:54+07:00

Lectins are carbohydrate-binding proteins with significant biological activities, including antimicrobial, anticancer, and antioxidant properties. Eleusine coracana (finger millet), a nutritionally rich cereal traditionally valued in India, has not been extensively explored for lectin properties. This study aimed to partially purify and characterize a lectin-like protein from Eleusine coracana seeds, with a focus on its hemagglutination activity, pH - temperature stability, sugar specificity, effects of metal ions and EDTA. Lectin partial purification was performed using ammonium sulphate precipitation followed by dialysis, yielding a protein concentration of 1 mg/mL. Hemagglutination assays revealed highest activity toward human blood group A+. The lectin was active within a pH range of 5-9, with maximum stability at pH 6-7, and retained high activity between 20-50 °C, with thermal stability up to 70°C. Sugar specificity assays showed mild hemagglutination inhibition by galactose. EDTA completely abolished hemagglutination activity, while Ca²⁺, Zn²⁺, and Mg²⁺ enhanced its activity, suggesting lectin’s metal dependence. These results demonstrate presence of lectin with distinct stability and characteristics in Eleusine coracana seeds, providing a foundation for further purification, characterization and exploration of its potential biomedical and nutraceutical applications.

OPTIMIZING IOMT BASED SMART HEALTHCARE WITH DEEP EHR-TANFLOW: A HYBRID ATTENTION DRIVEN MODEL

2026-01-19T13:06:39+07:00

The increasing demand for efficient and accurate disease diagnosis in Internet of Medical Things (IoMT)-based smart healthcare systems necessitates advanced predictive models that capture complex temporal dependencies in Electronic Health Records (EHR) data. However, existing approaches often struggle with long-term dependency modelling, feature redundancy, and computational inefficiency, which limits their applicability in real-world clinical settings. To address these challenges, this research introduces the Deep Learning EHR-Based Temporal Attention Flow (Deep EHR-TANFlow) framework. This novel architecture processes structured EHR data without requiring direct physical intervention. The framework begins with a robust preprocessing pipeline that incorporates KNN imputation for missing values, Min–Max scaling for normalization, and a sliding-window strategy to generate refined temporal segments. This ensures high-quality, structured input suitable for effective temporal predictive modelling. At its core, the framework employs the CNN–BiGRU Temporal Attention Network (CG-TAN), developed specifically for IoMT-based healthcare monitoring. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Bidirectional Gated Recurrent Units (Bi-GRU) for modelling forward and backward temporal dynamics, and a Self-Attention (SA) mechanism to enhance global contextual representation. This hybrid architecture effectively captures both local and global dependencies within patient data, significantly improving disease prediction and risk assessment. Experimental results demonstrate that the proposed system achieves superior diagnostic performance, attaining 98% accuracy, 98.12% precision, and 98.08% F1-score. These findings highlight the resilience, efficiency, and low computational complexity of the Deep EHR-TANFlow framework, making it a dependable solution for IoMT-based continuous health monitoring and automated risk assessment while ensuring reliable information extraction and enhanced patient safety.

INVESTIGATION OF CRYSTALLOGRAPHIC STRUCTURE AND FUNCTIONAL GROUP OF SILAR-DEPOSITED THALLIUM CUPROUS SULPHIDE THIN FILMS

2026-04-10T07:01:00+07:00

This study synthesized and characterized thallium cuprous sulphide (TCS) thin films. TCS thin films were prepared using successive ionic layer adsorption and reaction (SILAR) technique from the aqueous solution of thallium chloride (TlCl), copper (II) chloride dihydrate (CuCl₂.2H₂O), thiourea, and trisodiumcitrate (TSC), while ammonia (NH₃), triethanolamine (TEA) and ethylenediaminetetraacetate (EDTA) were used as pH adjuster and complexing agents respectively. A detailed characterization of the TCS thin film was investigated by X-ray diffractometer (XRD) and Fourier transform infrared spectroscopy (FTIR). XRD results showed prominent peaks around (130), (110), (200), (211) orientation planes corresponding to 23.18⁰, 26.72⁰, 37.92⁰ and 51.64⁰. Other lesser peaks appeared around (101), (310), (301) for 33.88⁰, 61.82⁰, 65.84⁰ lattice planes respectively which revealed the growth of a crystalline (carlinite) thallium sulphide (Tl₂S) in the fabricated films. FTIR results revealed further that the deposited TCS films can be attached to the Ti-OH lattice vibrations mainly by their OH groups and that C-OH group is undominant, which implies the presence of impurity in the deposited thin film.

A NOVEL CROSS-CONNECTED T-TYPE ASYMMETRICAL MLI WITH REDUCED COMPONENTS FOR PV APPLICATIONS

2026-03-26T13:44:05+07:00

We provide a new seventeen-level cross-connected T-type (CT-type) inverter with a lower device count (RDC) in this study. Just eight power switches, four self-balancing capacitors, and two DC sources are needed for the suggested design. It reduces the voltage stress on the switches by avoiding the need for an H-bridge to generate the negative half cycle, in contrast to traditional multilevel inverters. The distinctive feature of this topology is its asymmetrical cross‑connected structure, enabling seventeen voltage levels with far fewer components while maintaining natural capacitor voltage balancing. By cascading capacitors and DC sources with the switches, the inverter achieves the desired output levels efficiently. Thanks to its strong self‑balancing and high‑voltage handling capability, the design is particularly well-suited for renewable energy integration into the grid. The gate driver signals are generated using level‑shifted pulse width modulation. A comparative study demonstrates the advantages of the proposed topology over existing designs in terms of component count, driver requirements, and total standing voltage. Its practicality is confirmed through both simulation studies and a laboratory prototype.

CRYOPRESERVATION-INDUCED METABOLITE CHANGES IN TERAP (Artocarpus elasticus Reinw. ex Blume) SEEDS: IMPLICATIONS FOR SEED VIABILITY

2026-01-21T14:18:19+07:00

Terap (Artocarpus elasticus Reinw ex. Blume) seeds are recalcitrant, making cryopreservation the most effective method for long-term preservation. However, the cryopreservation process can alter metabolite composition, potentially affecting seed viability and germination. This study aimed to identify metabolite changes in cryopreserved terap seeds that may influence viability (germination) using chemometric analysis. A factorial, completely randomized design was applied with two factors: loading time (0 and 20 minutes) and vitrification time (0, 30, and 60 minutes). Metabolite profiling using GC-MS identified 247 compounds across all samples, classified into 44 distinct compound classes. Based on their effect on germination, compounds were categorized as promoting (15.11%), inhibiting (44.63%), either promoting or inhibiting (38.75%), and unidentified (0.05%). Chemometric analysis, including Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA), confirmed metabolite changes in terap seed post-cryopreservation. The variable Importance in Projection (VIP > 1.6) score from PLS-DA identified eleven key metabolites as potential biomarkers of viability loss: Benzeneethanol; Hexanal; Supraene; Octane, 2,4,6-trimethyl-; Nonanal; Elemadienonic acid, methyl ester; 2,4-Decadienal; Hexanoic acid; Butylamine, 3-methyl-1-[5-tetrazolyl]-; and Linoelaidic acid. These findings provide new insights into the biochemical mechanisms underlying seed viability decline post-cryopreservation and highlight key metabolites as indicators of seed deterioration.

A ROBUST PSO-LSTM FRAMEWORK FOR PREDICTING ACADEMIC PERFORMANCE WITH EXPERIMENTAL VALIDATION

2026-04-07T15:13:20+07:00

Accurately predicting students' academic performance is crucial for identifying students with learning difficulties and tailoring educational interventions effectively. This study proposed a PSO-LSTM framework that combines Particle Swarm Optimization (PSO) with Long Short-Term Memory (LSTM) to improve prediction accuracy by optimizing LSTM hyperparameters. The model was evaluated on a dataset of 722 students from the Computer Science program at Wenzhou Business College, covering six academic years (2018–2024). Two experimental scenarios were designed: semester-based GPA prediction and year-based CGPA prediction. The results indicated that PSO-LSTM outperformed traditional machine learning models (Decision Tree, Random Forest, SVM, KNN) and standard deep learning models (ANN, LSTM) in both experimental settings. In predicting the cumulative grade point average (CGPA), the model's accuracy increased from 87.59% in DSTU5 to 95.17% in DSTU7, and further to 96.55% after applying SMOTE. This method effectively alleviates bias caused by class imbalance, particularly by improving recall for identifying the minority (at-risk) class, thereby significantly enhancing fairness and robustness (e.g., DSTU5 increased by 8.0%). These findings highlight PSO-LSTM as a highly effective framework for predicting academic performance, with strong potential for early intervention strategies and personalized learning recommendations.

OSCILLATION CRITERIA OF CERTAIN FOURTH ORDER DIFFERENCE EQUATIONS WITH DAMPING TERM

2025-12-04T14:10:38+07:00

This paper deals with the oscillation condition for fourth order delay difference equation. The fundamental idea is to obtain the oscillation result and Property B via comparison techniques. Our results extend and enhance certain earlier findings. Examples are demonstrated to highlight the main results.

MICROMECHANICS STUDY AND COMPARATIVE ANALYSIS OF CARBON FIBER REINFORCED EPOXY COMPOSITES FABRICATED BY HAND LAY-UP AND VACUUM ASSISTED RESIN INFUSION PROCESSES

2026-05-12T22:03:01+07:00

The micromechanical behaviour and mechanical performance of carbon fabric reinforced epoxy (CFRP) produced by two different manufacturing routes are studied in this research work. Hand lay-up and vacuum-assisted resin infusion (VARI) are used to manufacture the CFRP specimens. Water immersion and acid digestion techniques are employed to extract several key material properties. The average density of the VARI specimens after water immersion test is found to be 1.4525 g/cm³. In contrast, the average density of hand lay-up specimens is 1.191 g/cm³. The average fibre volume fraction for VARI specimens is approximately 45%. On the other hand, the average fibre volume fraction of hand lay-up specimens is around 23%. The corresponding average void content for VARI specimens varies between 1.2% and 1.53%. On the other hand, that of hand lay-up varies between 9.58% and 10.56%. Changes in microstructure caused by processing enhances the material's mechanical properties. The tensile testing results indicate that the tensile strength and the elastic modulus of the tensile tested VARI-fabricated specimen are 268.3 MPa and 35.63 GPa respectively. A micromechanical model also shows higher elastic modulus for the VARI-fabricated composite with lower sensitivity to voids. The results suggest that VARI processing offers a cost-effective method for the fabrication of high-performance composite parts and assemblies that have potential applications in the aerospace field.

INTEGRATED WATER ACCOUNTING AND WEAP MODELING FOR SUSTAINABLE WATER MANAGEMENT IN RAINFED AREAS: CASE STUDY IN NAKHON RATCHASIMA, THAILAND

2026-09-16T16:33:12+07:00

Agricultural production in Thailand remains highly vulnerable to climatic variability, particularly drought, which recurrently disrupts rainfed systems in non-irrigated basins. This study integratein water accounting and the Water Evaluation and Planning (WEAP) model to assess water availability, agricultural demand, and drought vulnerability in four sub-basins of Nakhon Ratchasima Province in northeastern Thailand: Lam Takhong, Lam Phra Phloeng, Lam Chiang Krai, and Upper Mun. Monthly water accounts were constructed at subdistrict and basin scales, while WEAP simulations evaluated 24 scenarios that varied dry-season rice intensity and tested a crop-substitution strategy with sweet corn. Based on the results, dry-season rice cultivation was the principal driver of water deficits in Lam Chiang Krai and Upper Mun, with shortages concentrated during February-April. Substituting rice with sweet corn reduced deficits by 37-38%, highlighting the potential of crop diversification to strengthen drought resilience. Structured interviews with 879 farmers and a survey of parameters for 79 small reservoirs confirmed late dry-season water stress, limited groundwater potential, and strong community demand for small-scale storage and drought-tolerant crops. Methodologically, the integration of water accounting with dynamic modeling provided complementary insights into seasonal imbalances, spatial drought hotspots, and adaptation options. These findings underscore the need for adaptive, demand-focused water management strategies to enhance the resilience of rainfed agriculture in Southeast Asia.

EXPERIMENTAL INVESTIGATION ON LIGHTWEIGHT PAPERCRETE BRICKS

2026-05-29T09:46:40+07:00

Construction materials have been in demand for now days on account of rapidly increasing population and urbanisation, which causes the prices of materials to rise. Depletion of natural resources for construction materials necessitates the use of recycled products, which not only reduces waste and environmental impact but also conserves resources. In this study, the utilisation of waste paper, fly ash, quarry dust, rice husk ash, and cement for the production of light weight bricks was examined. The SEM and EDAX have been analysed to find the chemical constituents of those materials. Trial mix proportions of paper sludge, cement, fly ash, quarry dust, and rice husk ash in various proportions made lightweight bricks. In addition to these, Dr.Fixit powder has been included by weight of cement. The mix's optimisation was identified based on the compressive strength, water absorption test, and bricks' unit weight. According to the trial mix results, these bricks can achieve the highest compressive strength and the lowest water absorption during production. As a result of the study, the self weight on the structure will be decreased, allowing it to be used in the non-load-bearing walls of the building.

STRUCTURAL AND ECONOMIC PERFORMANCE OF SBS POLYMER-MODIFIED ASPHALT UNDER HEAVY TRAFFIC LOAD

2026-04-03T15:46:01+07:00

This study investigates the structural and economic performance of SBS polymer-modified asphalt under heavy traffic loading, emphasizing polymer interaction, rutting resistance, and moisture durability. XRD analysis revealed enhanced polymer–bitumen interaction, indicating effective integration of SBS into the asphalt matrix. Laboratory evaluations of Dense Bituminous Macadam (DBM) and Bituminous Concrete (BC) demonstrated substantial improvements compared with conventional mixes. At the optimum SBS content of 4%, the Marshall Stability increased by 22.49% for DBM and 30.31% for BC, while the Marshall Quotient (MQ) rose by 31.96% and 10.32% respectively. Percentage improvement was calculated with respect to the control mix values, indicating superior resistance to permanent deformation under traffic loading. Moisture susceptibility assessed using Retained Marshall Stability (RMS), showed significant improvement, indicating enhanced durability under wet conditions. Furthermore, life cycle cost analysis (LCCA) highlighted the economic benefits of SBS modification, including potential reductions in maintenance costs over the pavement lifespan. Overall, the findings demonstrate that SBS-modified asphalt provides enhanced structural performance, durability, and cost efficiency, making it highly suitable for high-traffic highways.

PHYTOCHEMICAL TREASURES AND PHARMACOLOGICAL INSIGHTS: A REVIEW OF ERYTHRINA INDICA

2025-12-16T15:28:27+07:00

Erythrina indica (syn. Erythrina variegata) is a medicinal plant widely used in Ayurveda and other traditional systems, attracting growing scientific interest for its therapeutic potential. This review synthesizes existing research on its phytochemical composition and pharmacological activities, highlighting its promise as a source of natural therapeutics. Phytochemical investigations reveal that Erythrina indica is rich in alkaloids, flavonoids, and pterocarpans, which are responsible for its broad spectrum of bioactivity. Scientific studies validate its potent antioxidant capacity and demonstrate significant anti-inflammatory effects. Notably, extracts and isolated compounds exhibit dose-dependent anticancer activity against breast, lung, and liver cancer cell lines by inducing apoptosis and showing cytotoxicity with defined IC₅₀ values. Antimicrobial efficacy has been observed against pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa, with specific compounds demonstrating the ability to disrupt biofilm formation. Erythrina indica also possesses marked antidiabetic potential, significantly reducing blood glucose and glycosylated hemoglobin levels in diabetic models while preserving pancreatic β-cell integrity. Collectively, these findings strongly corroborate the plant’s traditional uses and underscore its promise for the development of novel treatments for oxidative stress, inflammation, cancer, microbial infections, and diabetes. Future research should prioritize clinical trials, mechanistic studies, and the development of standardized formulations.

OPTIMIZATION OF MANURE LEVELS AND APPLICATION DEPTH FOR ENHANCING SOIL PHYSICOCHEMICAL PROPERTIES UNDER SALINE CONDITIONS

2026-04-08T15:22:07+07:00

A field experiment was conducted in 2015 at the Al-Hartha Research Station, Iraq, to evaluate the effects of animal manure application rate, placement depth, and application method on soil salinity and physico-chemical properties in a silty loam soil under drip irrigation. The field was plowed perpendicularly, and the experiment was arranged in a split–split plot design within a Randomized Complete Block Design (RCBD) with three replications. Treatments included three manure application rates (0, 20, and 40 t ha⁻¹), three application depths (10, 20, and 30 cm), and two application methods (mixing and manure placement depth ). Corn (Zea mays L.) was used as the test crop, and all plots received equal amounts of NPK fertilizers. At the end of the growing season, soil samples from the 0-30 cm depth were collected to determine EC, soil pH, BD, and soil water content. The application of 40 t ha⁻¹ manure combined with manure placement depth from 10-30 cm reduced soil EC 4.63 to 5.87 dS m⁻¹ and pH 7.97 and 8.08 , respectively, compared with the control (6.81 dS m⁻¹ and 8.12) and soil bulk density was found to reduce from 1.23 to 1.20 Mg m^-3 while soil water content was increased from 26.33 to 30.23% when depth of addition was raised from 10 to 30 cm. The manure application 40 t ha⁻¹ combined with manure placement depth from 10-30 cm was more effective than mixing in reducing soil pH, soil salinity, and EC, BD and improved soil water content, particularly at deeper soil layers, resulting in improved soil physical conditions.