Indonesian Journal of Science and Technology
The Indonesian Journal of Science and Technology (IJoST) (ISSN: e.2527-8045 p.2528-1410) is an open access and peer-reviewed journal, published by Universitas Pendidikan Indonesia, which is a dissemination from research results from scientists and engineers in many fields of science and technology. In 2016-2020, IJoST is issued on April and September. Since 2020, IJoST issues 3 times a year (April, September, and December). The editors welcome submissions of papers describing recent theoretical and experimental research related to: (1) Research Paper and (2) Review Paper. IJoST is an open-access, peer-reviewed premier journal in its field, in which it has been engaged with universities and institutions in more than 49 countries across the world, including Algeria; Australia; Bahrain; Bangladesh; Belgium; Brazil; Brunei Darussalam; Canada; China; Colombia; Egypt; Estonia; France; Germany; Hungary; India; Indonesia; Iran; Iraq; Italy; Japan; Jordan; Kazakhstan; Malaysia; Mauritania; Mexico; Morocco; Namibia; Netherlands; Nigeria; Pakistan; Palestine; Philippines; Russia; Saudi Arabia; Singapore; South Africa; South Korea; Spain; Thailand; Tunisia; Turkey; Uganda; United Arab Emirates; United Kingdom; United States; Uzbekistan; and Vietnam.
Integrating Ground Magnetic and Satellite Gravity Data for Delineating Geothermal System-Controlling Structures
Geothermal energy is a key renewable resource in Indonesia, including non-volcanic regions where subsurface conditions remain poorly understood. The Karangrejo–Tinatar area in Pacitan, East Java, displays hot spring manifestations that suggest a structurally controlled geothermal system. This study characterises the system using integrated primary ground magnetic data and GGMPlus satellite gravity data over a 25 km² area. Magnetic data were processed using Reduction to the Pole and Tilt Derivative, while gravity data were analysed through Bouguer correction, upward continuation, and 3D inversion modelling with ZondGM3D and Grablox 1.6. Results show magnetic susceptibility values ranging from −0.01759 to 0.0402 SI and rock density values from 1.88 to 3.11 g/cm³. The inversion model identified five major fault structures—Kebonsari, X, Karangrejo, Sedayu, and Tinatar Faults—that control geothermal fluid circulation as recharge and discharge pathways. This study provides a new framework for understanding non-volcanic geothermal systems in Indonesia.
Iskara Hadijah Aqila Nasywa Rastramata Athorhab
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
Deep Learning Based Framework for Automated Road Crack Detection and Severity Assessment under Different Weather Conditions
Crack detection and assessment of roads and concrete are vital for infrastructure and safety management. This paper proposed an end-to-end deep learning based framework for automated crack detection, classification, and severity analysis under different surface conditions. The simulation implemented and tested four architectures, namely CNN, BiGRU, BiLSTM, and a hybrid BiGRU–BiLSTM–CNN model. Using a large dataset of 5,200 crack and non-crack photos collected from the METU campus and road photos taken using a smartphone, we trained and assessed our approach. Regression and classification metrics such as RMSE, MAE, R2, Cohen's Kappa, F1-score, and AUC were also used in the study to evaluate model performance. The CNN outperformed recurrent and hybrid models in capturing complex spatial crack information, according to the experimental data (R² = 0.92, F1-score = 0.97, AUC = 0.99). The suggested framework included geometric crack analysis and 3D heat map visualization in addition to probabilistic crack classification to measure relative depth and crack shape. The result offers an approach to real-time road condition evaluation using conventional cameras that is clear, dependable, and reasonably priced.
Monte Carlo‐Based Assessment of Human Health and Ecological Risks from Chromium and Iron Contamination in Coastal South Sulawesi, Indonesia
This study aimed to assess exposure levels and health risks associated with iron (Fe) and chromium (Cr) among coastal communities in South Sulawesi, Indonesia. A cross-sectional design was employed, involving field sampling and laboratory analysis of seawater and marine biota, combined with a health risk assessment approach. Intake analysis indicated that Fe was the dominant contributor to daily exposure, while Cr levels were relatively lower. Risk characterization showed that the average Risk Ratio (RQ) for Fe exceeded the safe threshold (RQ > 1), while the RQ for Cr remained below the safe limit. Monte Carlo simulations (MCS) confirmed that both Cr and Fe exhibited Target Hazard Quotient (THQ) values above safe levels. Sensitivity analysis revealed that exposure duration, frequency, consumption level, and metal concentration in seafood were the most influential variables. This study demonstrated that Fe is the primary risk factor contributing to non-carcinogenic health hazards in coastal areas of South Sulawesi, while Cr presents a potential long-term carcinogenic risk. These findings underscore the urgent need for integrated mitigation strategies to protect public health and ensure the sustainability of coastal ecosystems.
A Multi-Analytical Study of Ultrasound-Assisted Extraction of Annona muricata Leaves with Bioactive Quality
This study compared ultrasonic-assisted extraction (UAE) with conventional Soxhlet extraction to evaluate the effect of solvent composition (ethanol–water 70:30–100:0), ultrasonic power rate (10–90%), and extraction time (2.5–15 min) on bioactive release and extraction kinetics. Extraction performance was assessed using a hierarchical framework consisting of extraction yield, total phenolic content normalized to yield (yield × TPC), and total flavonoid content normalized to yield (yield × TFC), followed by functional and chemical characterization through antioxidant activity (IC50), phytochemical profiling, colorimetry, FTIR spectroscopy, GC–MS, and HPLC analysis. The results showed that solvent polarity, ultrasonic power rate, and extraction time significantly affected extraction efficiency (p < 0.001). Under optimal UAE conditions (70:30–85:15 ethanol–water, moderate ultrasonic power, and 5–7.5 min extraction), yields reached 3.6–4.1%, which equates to a 17–368% increase compared to Soxhlet extraction. Overall, UAE produced chemically richer, more stable, and more bioactive extracts than Soxhlet extraction, while significantly reducing extraction time, demonstrating its potential as a rapid and energy-efficient method for producing high-quality A. muricata leaf extracts for natural product applications.
MRD-UNet: Detail-Preserving Low-Light Image Enhancement with Multiscale Residual Dense Networks and Edge-Texture Guided Loss
Low-light image enhancement is an important task in computer vision, particularly for night photography, autonomous driving, and video surveillance. Although recent deep learning methods perform well in reducing global noise, many models still degrade micro-structural details and texture fidelity because of excessive smoothing under extreme low-light conditions. This study proposes MRD-UNet, an encoder–decoder model designed to enhance low-light images while preserving structural integrity and textural sharpness. The model introduces two main components. First, the MRD-Block combines residual connections, dense connections, and multiscale convolutions to capture both global contextual features and fine local details. Second, an Edge-Texture Guided Loss based on Sobel and Laplacian operators is used to guide the learning process, improving edge consistency and high-frequency detail preservation. Experiments on the LOL-V1 benchmark show that MRD-UNet outperforms several state-of-the-art methods, achieving an SSIM of 0.911 and PSNR of 26.09 dB.
Assessment of Tolerance, Biosorption, Adsorption Isotherm, and Kinetics of Heavy Metals from Liquid Media using Live Mycelia of Perenniporia subtephropora
This study assessed the tolerance of Perenniporia subtephropora and evaluated the potential of its living mycelia as a biosorbent for liquid media contaminated with heavy metals. Isolates were identified using morphological and molecular techniques. Biosorption was performed based on initial metal concentrations (0, 50, 150, and 450 mg/L), pH changes (4.5, 7.0, and 8.0), and contact duration (0, 24, 48, 72, 96, and 120 hours). P. subtephropora was classified as highly tolerant to Cr, Ni, As, and Fe; moderately tolerant to Cu, Zn, and Pb; and less tolerant to Mn. Biosorption increased with increasing metal concentration, and no biosorption occurred at a metal concentration of 0 mg/L. The percentage removal efficiencies varied significantly with respect to the lowest efficiency, with some metals (Pb, Fe, Ni, Mn, and As) having the lowest removal efficiencies at the minimum pH (pH 4.5), while others (Cu, Zn, and Cr) having the lowest values at neutral pH (pH 7.0). The maximum removal percentages for all metals were at the highest pH (pH 8.0). P. subtephropora showed a time-dependent increase in biosorption ability. This study concluded that fungal mycelia proved to be a low-cost biosorbent with high potential for the removal of various metals from aqueous solutions.
Examining Thermal and Salinity Impact on Flow of Crude Oil through Porous Media for Enhancing Recovery
The objective of this study is to examine the influence of reservoir temperature and injected formation brine salinity on oil recovery enhancement in brown oil fields. The study employed core flooding experiments using actual crude oil-producing porous media represented by core plugs, with recovery efficiency used as the main evaluation parameter. Flooding tests were conducted at 65°C, 75°C, 85°C, 95°C, and 105°C, representing reservoir temperatures of selected oil wells in the Upper Assam Basin. Initial water flooding was performed using low-salinity injection water of 897 ppm, followed by subsequent flooding using higher-salinity brine of 8000 ppm under the same temperature conditions. SARA analysis was also conducted to characterize the crude oil composition in terms of saturates, aromatics, resins, and asphaltenes. The results show that low-salinity flooding at 897 ppm produced oil recovery efficiencies of 51.47%, 44.21%, 89.89%, 36.33%, and 16.22% at 65°C, 75°C, 85°C, 95°C, and 105°C, respectively. When salinity was increased to 8000 ppm, the corresponding recovery efficiencies were 22.22%, 38.73%, 59.48%, 43.33%, and 4.98%. The highest oil recovery was achieved at 85°C for both salinity levels, with 89.89% recovery at 897 ppm and 59.48% at 8000 ppm. These findings indicate that reservoir temperature strongly affects crude oil recovery, while injection brine salinity also plays an important role. The study concludes that thermal and salinity-induced wettability alteration occurs during low-salinity water flooding, contributing to improved oil recovery in brown oilfields.
Transformation of Bioactive Compounds in Coffee Ground Kombucha: The Effect of Sugar Sources on Chemical and Sensory Properties
Spent coffee grounds still contain valuable bioactive compounds that can be valorized through kombucha fermentation to develop functional beverages and support the sustainable utilization of coffee processing by-products. This study aimed to evaluate the effect of different sugar sources on the transformation of bioactive compounds and the chemical and sensory properties of coffee ground kombucha. Kombucha was prepared using spent coffee grounds as the fermentation substrate with three sugar sources (sucrose, glucose, and fructose) and fermented for 15 days at 28 °C using a standard SCOBY culture. Microbial growth, reducing sugar, total acidity, and pH were monitored, while total phenolic content, antioxidant activity, and sensory properties were analyzed. The results showed that the sugar source significantly influenced fermentation and product characteristics. Glucose promoted the most intensive fermentation, resulting in the highest acidity (13.94 g/L) and lowest pH (2.20). In contrast, fructose produced the highest total phenolic content (1341.81 µg/mL) and antioxidant activity (83.22%). Sensory evaluation indicated that fructose-based kombucha was the most preferred due to its balanced aroma, flavor, sweetness, and acidity. Overall, fructose was identified as the most suitable sugar source for producing coffee ground kombucha with enhanced bioactive and sensory qualities.
GC-MS Analysis of Suaeda maritima and its Application as a Salt Substitute in Fish Marinade Powder
This study aims to identify bioactive compounds in seablite (Suaeda maritima) and develop seablite-based fish powder seasoning and marinade as an alternative low-sodium product. The chemical composition of edible seablite extract was analyzed using gas chromatography-mass spectrometry (GC-MS). Production process: powder preparation from dried seablite leaves and dried herbal powder including 5% lemongrass, 5% galangal, 5% garlic, and 5% kaffir lime leaves. Sensory evaluation and consumer acceptance test were conducted on 30 participants. GC-MS results showed that seablite extract contains phytochemicals, with the main components including phytol (43.19%), Myo-Inositol (7.06%), 9,12,15-Octadecatrienoic acid, (Z,Z,Z)- (6.32%), 9-Octadecenamide, (Z)- (5.36%) and Palmitoleamide (5.21%). The final product contained 320.87 kcal, 8.65 g protein, 68.62 g carbohydrate, 1.31 g fat, 15.19 g ash, 6.23 g water per 100 grams, and 46,431.25 mg/kg sodium. This formulation offers a healthier alternative with reduced sodium while maintaining flavor and nutritional value. The optimal formulation for seasoning and marinating fish powder consisted of 50% dried seablite powder. Sensory evaluation scores indicated consumer preferences ranging from “somewhat liked” to “quite liked.” These findings indicate that the developed seasoning powder is a promising, tasty, and healthier alternative for low-sodium fish products, while maintaining acceptable sensory qualities.
Digital Technology in Differentiated English Language Teaching: A Systematic Review with Bibliometric Insights
This study aimed to systematically review research on digital technology in differentiated English language teaching by integrating a systematic literature review with bibliometric mapping. The data were collected from Scopus and analyzed using PRISMA guidelines and VOSviewer. Results indicated that digital platforms, learning management systems, and adaptive technologies were central to personalization. Advanced technologies such as artificial intelligence, big data, and the Internet of Things are emerging but underutilized. These technologies are significant because they enable real-time adaptation and enhance learner-centered instruction, although institutional readiness, teacher competence, and infrastructure mediate their effectiveness. Bibliometric insights showed three clusters: pedagogical integration, institutional innovation, and data-driven personalization. This review contributes by linking pedagogy, science, and technology, offering a framework for scaling and sustaining digital differentiation in English language teaching. The findings are expected to guide educators, policymakers, and researchers toward effective and equitable technology-enhanced instruction.
Reducing Early Failure through a Six-Week Integrated Approach in Foundational Engineering Mathematics
Foundational Engineering Mathematics often shows uneven engagement and high early failure in diverse cohorts. This study evaluated a six-week integrated framework combining a placement diagnostic, weekly pre- and post-lecture online quizzes, peer-learning tutorials, and one invigilated written quiz. Data from eight online quizzes, one written quiz, and the placement test were analysed for participation, performance, and progression. Results showed sustained engagement (mean participation 89%) and average quiz performance of 6.9/10; failure peaked at 33% in an early pre-quiz but declined to 15-18% in later weeks. Placement was predictive: 79% who passed the diagnostic later passed quizzes, whereas failing or absent groups were less consistent. The approach worked because frequent low-stakes assessments provided retrieval practice and timely feedback, heterogeneous peer groups offered scaffolding, and the early diagnostic identified at-risk students who needed support. This framework offers a scalable path to enhance engagement, reduce early failure, and strengthen accountability in foundational engineering mathematics.
Structural Characterization, Hirshfield Surface Analysis, and Molecular Docking of Novel Diquinoline Derivatives as Anti-Tumor Agents
Quinoline is a versatile scaffold in anticancer drug design due to its structural flexibility and diverse biological activity. This study reports the synthesis, characterization, Hirshfeld surface analysis, molecular docking, and cytotoxic evaluation of three novel diphenyldiquinoline derivatives: DPDQ-3a, DNDPDQ-3b, and DCDPDQ-3c. The compounds were synthesized via Friedländer condensation and tested against lung (A549), colorectal (DLD1), and breast (MCF-7) cancer cells, with human embryonic kidney (HEK293) as controls. DPDQ-3a showed the strongest potency and selectivity, with IC₅₀ values lower than cisplatin and reduced toxicity toward normal cells. Hirshfeld analysis revealed stabilizing intermolecular interactions, while docking studies confirmed high binding affinities to cancer-related proteins. In silico toxicity assessment indicated favorable safety, highlighting DPDQ-3a as a promising anticancer candidate.
Heritage Preservation in Flood-Prone Urban Areas: Bibliometric, Risk Index, and Spatial Analysis of Historic Buildings in Semarang
Historic buildings in coastal and low-lying cities are increasingly threatened by tidal flooding, land subsidence, and climate-driven sea-level rise. Preserving these assets requires a framework that integrates cultural heritage values with disaster risk assessment. This study applies three approaches: bibliometric analysis to examine research trends on historic buildings and disasters; weighting analysis of cultural values based on Burra Charter parameters, including historical, aesthetic, social, and authenticity aspects; and spatial analysis using GIS and Digital Elevation Model data to map flood-prone zones. The results show that landmark buildings with strong historical and architectural significance are consistently prioritized for preservation. In contrast, structures with limited cultural importance or advanced deterioration rank lower in feasibility for conservation. Spatial mapping highlights that heritage sites in flat and low-lying areas face the greatest exposure to flooding and sea-level rise. This interdisciplinary framework provides a scientific basis for prioritizing heritage preservation in flood-vulnerable urban environments. It also supports adaptive conservation strategies that align cultural continuity with resilience to environmental hazards.
Structural and Physicochemical Tuning of Binary and Ternary Mn-Based Layered Double Hydroxides via Alkali-Free Synthesis for Advanced Material Applications
This study explores the alkali-free synthesis of binary (MnCo) and ternary (MnCoCr) layered double hydroxides (LDHs) to enhance structural and physicochemical properties relevant to advanced catalytic systems. The materials were synthesized using an ammonium nitrate buffer, followed by controlled ageing and calcination. Characterization included PXRD, BET, TGA, FESEM, and FTIR techniques. The ternary MnCoCr LDH demonstrated improved crystallinity, larger surface area, and more uniform morphology compared to the binary MnCo LDH. These enhancements occurred because chromium altered interlayer spacing and cation interactions, promoting better nucleation and structural integrity. The findings suggest that careful control of metal composition and synthesis conditions can effectively tailor LDH properties. This approach supports sustainable materials engineering and opens new possibilities in catalysis, adsorption, and energy storage applications.
Sustainable Goat Skin Gelatin-Based Edible Coatings Incorporated with Konjac Glucomannan: Physicochemical Properties and Preservation Efficacy on Strawberries
This study evaluated the effectiveness of edible coatings made from goat skin gelatin, with and without konjac glucomannan, for preserving strawberries at room temperature. Three groups were analyzed: a control group (EKG1), a gelatin coating group (EKG2), and a gelatin with konjac glucomannan group (EKG3). The addition of konjac glucomannan increased coating viscosity and thickness. While EKG3 initially raised total soluble solids, this difference disappeared by day 3. Both gelatin-based coatings maintained pH levels and showed no significant firmness difference between EKG2 and EKG3. The coatings effectively slowed color changes and pigment oxidation because they limited oxygen exposure. Weight loss was similar for EKG2 and EKG3, but EKG3 better preserved ascorbic acid on day 12. These results suggest that natural coatings can effectively reduce fruit spoilage, supporting sustainable food packaging and circular economy principles because they extend shelf life while being environmentally friendly.
Classification of Companion Animal Clinic Medical Service Quality Factors Using a Multidimensional Evaluation Model
The rapid growth of the companion animal healthcare sector has heightened the need for high-quality veterinary services. This study aimed to identify and prioritize medical service quality attributes in animal clinics using a multidimensional evaluation model integrating science and technology concepts. A structured online survey was conducted with pet owners to classify service elements based on the Kano model, followed by statistical analyses using Timko’s coefficients. Results showed that veterinary expertise and interpersonal communication skills were consistently valued more than physical infrastructure. These attributes enhance satisfaction because pet owners trust professionals who demonstrate technical skill and empathy. The findings suggest that focusing on human-centered competencies and diagnostic precision—reflecting advances in clinical technology—is critical for improving customer loyalty. The model can be adopted by animal hospitals to optimize their services through science-based prioritization, supporting sustainable competitiveness in the veterinary field.
Optimization by Artificial Neural Network (ANN) of Malachite Green Adsorption on Clays: Contribution to the Achievement of the Sustainable Development Goals (SDGs)
This study focused on the adsorption of malachite green onto clays in aqueous solutions. The results were simulated using an artificial neural network (ANN). Materials were characterized using X-ray fluorescence spectrometry, Fourier transform infrared spectroscopy, X-ray diffraction, and nitrogen adsorption at 77 K. Sorption experiments were carried out in the discontinuous mode, examining the influences of contact time, adsorbent dose, solution pH, initial concentration, and temperature. The neural network topology was 4–10-1. The results predicted by this model show a good agreement with experimental data. The mathematical modelling of the obtained isotherms revealed that the Freundlich isotherm model is perfectly consistent with the experimental data. The thermodynamic parameters, such as the changes in Gibbs free energy, enthalpy, and entropy, are determined. The MG adsorption is physical, spontaneous, and exothermic for both adsorbents. This method, therefore, appears as an effective means to achieve the objectives of sustainable development of the United Nations.
Sustainable Development Goals (SDGs) in Engineering Education: Definitions, Research Trends, Bibliometric Insights, and Strategic Approaches
This study investigates the integration of Sustainable Development Goals (SDGs) within engineering education by providing conceptual definitions, identifying research trends, and analyzing strategic approaches. Using a systematic literature review enriched with bibliometric analysis, the study explores six key aspects: the role of SDGs in engineering education, the intersection of sustainability and technical learning, the integration of Education for Sustainable Development (ESD), core competencies required for sustainability, systems thinking as a foundational skill, and institutional challenges and opportunities. Bibliometric mapping reveals increasing global attention to SDGs in engineering, particularly in 2022 and 2024. The findings show a paradigm shift from purely technical training to holistic, interdisciplinary education that combines ethics, ecology, and social impact. Despite structural barriers and curricular gaps, strategic opportunities (such as faculty readiness, innovative pedagogies, and technological tools) support transformative learning. This review provides a comprehensive framework for aligning engineering education with the SDGs and contributes to the development of sustainability-literate future engineers.
Green Extraction of Anthocyanins from Red Dragon Fruit Peel Using Choline Chloride-Urea Deep Eutectic Solvents: A Sustainable Valorization Strategy Aligned with the Sustainable Development Goals (SDGs)
This study proposes a sustainable extraction strategy for anthocyanins from red dragon fruit peel using a choline chloride:urea (1:2) deep eutectic solvent (DES) system, contributing to the Sustainable Development Goals (SDGs), particularly SDGs 3, 9, and 12. Extraction conditions were optimized through Box-Behnken design and response surface methodology, targeting temperature (25-45 °C), phase ratio (0.5-2), and solid-liquid ratio (1:10-50 g/mL). The optimal parameters (33°C, 1:1.7 phase ratio, and 48:1 mg/mL solid–liquid ratio) achieved a 99.5% yield (29.15 mg/100 g) of total anthocyanins, outperforming conventional techniques. The FTIR confirmed the structural integrity of both DES and anthocyanins, with hydrogen bonding facilitating efficient solubilization under mild, non-degradative conditions. This environmentally friendly method transforms agricultural waste into high-value antioxidants, advancing circular bioeconomy principles. The approach offers scalable potential for application in the food, pharmaceutical, and cosmetic industries, reinforcing responsible production practices while promoting innovation in green extraction technologies.
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Toward Carbon-Neutral Power Generation in Indonesia: A Techno-Economic Assessment of Renewable Ammonia Co-Firing in Combined Cycle Power Plants
The urgency of climate change mitigation demands transformative shifts in energy systems, especially in fossil fuel-dependent regions like Southeast Asia. This study evaluates the techno-economic feasibility and environmental impact of green ammonia co-firing in existing combined cycle power plants. Using advanced simulations, including alkaline electrolysis, cryogenic air separation, and the Haber-Bosch process followed by GateCycle modelling, the integration of green ammonia was assessed. The results show that a 50:50 blend of natural gas and green ammonia delivers an 80 MW output, 11.3 MJ/kWh heat rate, and 31.8% thermal efficiency. While this reduces carbon emissions significantly, economic analysis reveals a negative net present value and a high levelized cost of electricity of 0.63 USD/kWh, indicating financial infeasibility under current market conditions. These findings highlight the need for policy support and strategic incentives. Green ammonia presents a scalable pathway for decarbonized power generation, contributing to sustainable energy transitions in Southeast Asia.
Fire Performance of Cross-Laminated Timber Enhanced by Borax Treatment and Densification for Structural Wood Composites
This study investigates the fire performance of Cross-Laminated Timber (CLT) using a combination of densification and borax treatment to enhance its safety for structural applications. Laminated Batai wood underwent borax treatment either before or after densification, followed by tests on absorption capacity and combustion resistance. The results revealed variations in fire resistance and density profiles depending on the treatment sequence. Densification increased density, and borax improved flame retardancy. The treatment order influenced absorption rates and charring behavior because the densification process modified internal porosity, affecting chemical penetration. Image analysis and thermal evaluation confirmed that specific combinations improved fire performance. This approach offers a scientific and technological basis for optimizing fire-resistant engineered wood products with lightweight species.
Unveiling Soil Salinity and Heavy Metal Patterns in the Khorat Plateau, Thailand: Implications for Sustainable Development Goals (SDGs) Monitoring
Strongly saline soils cover approximately 2,400 km² of the Khorat Plateau, Thailand, posing challenges for agriculture and environmental sustainability. This study aimed to characterize soil salinity and heavy metal patterns across five soil layers in both badlands and paddy fields. Soil samples were analyzed for pH, electrical conductivity (EC), salt content, and concentrations of As, Cd, Cu, Fe, Mn, Pb, and Zn. Results showed higher EC and salt percentages in badlands, while paddy fields exhibited greater levels of micronutrient metals such as Cu, Fe, Mn, and Zn, likely due to agricultural practices. Heavy metal concentrations remained below national safety thresholds, suggesting low contamination risk. Statistical analyses revealed significant correlations among salinity indicators, soil properties, and metal contents. These findings provide baseline data critical for environmental monitoring and support Sustainable Development Goals (SDGs) 2 and 15 by informing strategies for food security, sustainable land use, and soil resource conservation in salinity-affected regions.
A Hybrid Artificial Intelligence-Driven and Genetic Algorithm-Based Optimization Framework for Enhancing Wind Turbine Performance with Structurally Defective Blades in Support of the Sustainable Development Goals (SDGs)
This study aims to enhance the performance of horizontal-axis wind turbines with structurally defective blades through a hybrid Artificial Intelligence-driven and Genetic Algorithm-based optimization framework. The research developed Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System models to predict power output and vibration levels. The models were integrated into a Genetic Algorithm to determine optimal pitch angles and rotational speeds. The framework resulted in maximized power generation and minimized vibration. The findings demonstrate that the combined models outperform traditional methods because they capture complex nonlinear interactions and support real-time control. This integration of science and technology concepts contributes to improving the operational efficiency and reliability of wind turbines while supporting the Sustainable Development Goals through renewable energy advancement.
Antibiotic Removal from Wastewater via Ceramic Membrane Distillation: Toward Clean Water and Antimicrobial Resistance Mitigation Aligned with Sustainable Development Goals (SDGs)
Antibiotic residues in wastewater represent a growing concern for environmental and public health due to their role in promoting antimicrobial resistance. This study investigates the use of ceramic membrane distillation for removing amoxicillin, ciprofloxacin, and levofloxacin under various operational conditions. Two ceramic membranes, fabricated from red clay with nanocellulose and silica additives, were tested at different pH levels, temperatures, and antibiotic concentrations. Experimental results showed consistently high rejection rates, attributed to electrostatic repulsion and membrane-liquid interaction. Zeta potential measurements supported the correlation between membrane surface charge and separation performance. The study contributes to the body of knowledge on membrane-based water purification technologies and supports goals related to clean water access and reduced pharmaceutical contamination, as reflected in the United Nations Sustainable Development Goals.
Modernization of Submersible Pump Designs for Sustainable Irrigation: A Bibliometric and Experimental Contribution to Sustainable Development Goals (SDGs)
The modernization of submersible pump designs is essential for enhancing the sustainability and operational reliability of irrigation systems. This study integrated bibliometric analysis and experimental evaluation to address performance degradation caused by cavitation, sedimentation, and mechanical wear. The bibliometric analysis identifies increasing global research attention toward energy-efficient and reliable irrigation technologies. Experimental investigations assess pump efficiency, hydraulic performance, and component wear under controlled laboratory conditions. Physical inspections reveal that impeller erosion and casing degradation significantly reduce hydraulic stability because sediment-laden flows and cavitation accelerate material loss. To predict pump longevity, a vibration-based reliability model was developed, allowing early detection of failure progression and enabling condition-based maintenance. These design innovations improve irrigation efficiency and energy utilization while extending pump service life because they mitigate wear progression and operational instability. The outcomes contribute directly to multiple Sustainable Development Goals (SDGs), particularly in water resource management, clean energy utilization, food security, and climate resilience. This integrated approach provides a practical framework for sustaining irrigation infrastructures under growing agricultural and environmental demands.
Innovative Nanofluid Encapsulation in Solar Stills: Boosting Water Yield and Efficiency under Extreme Climate Supporting Sustainable Development Goals (SDGs)
Water scarcity in extreme climates presents a global challenge directly linked to Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation). This study introduces an innovative solar still design using nanofluid encapsulation to enhance freshwater production because conventional systems often struggle with limited efficiency. The modified solar still employed sealed glass tubes containing CuO nanofluid, improving solar energy absorption and heat retention because the nanofluid could enhance thermal conductivity. The system maintained higher operational temperatures, leading to increased evaporation and freshwater yield. The encapsulation prevented nanoparticle contamination, ensuring water safety and long-term system stability. This approach demonstrates significant potential to support sustainable freshwater solutions aligned with global SDG targets under harsh environmental conditions.
Computational Engineering of Malonate and Tetrazole Derivatives Targeting SARS-CoV-2 Main Protease: Pharmacokinetics, Docking, and Molecular Dynamics Insights to Support the Sustainable Development Goals (SDGs), with a Bibliometric Analysis
This research applies computational engineering to explore malonate and tetrazole derivatives as potential inhibitors of the SARS-CoV-2 Main Protease. A comprehensive in silico approach, including pharmacokinetics prediction, molecular docking, and molecular dynamics simulations, was utilized to evaluate the drug-likeness, binding affinity, and stability of the designed compounds. The malonate derivatives demonstrated strong interaction stability with the target protease and exhibited favorable pharmacokinetic profiles with minimal predicted toxicity, supporting their potential as therapeutic candidates. A bibliometric analysis was also performed to position this study within the broader scientific landscape, showing increasing global interest in SARS-CoV-2 protease inhibitors and antiviral drug development. This work aligns with the Sustainable Development Goals by contributing to global health improvement and fostering innovation in pharmaceutical engineering. The promising computational outcomes underscore the need for further experimental validation to confirm therapeutic efficacy and safety, potentially contributing to future antiviral treatment strategies.
Integrating Multi-Stakeholder Governance, Engineering Approaches, and Bibliometric Literature Review Insights for Sustainable Regional Road Maintenance: Contribution to Sustainable Development Goals (SDGs) 9, 11, and 16
This study analyzes the Provincial Road Improvement and Maintenance (PRIM) program in Lombok Barat, Indonesia, which integrates technical engineering frameworks with multi-stakeholder governance mechanisms to improve road maintenance performance. Using a case study approach, data were collected through official government records, complaint management systems, multi-stakeholder forum documentation, and bibliometric analysis of global research trends. Life Cycle Cost Analysis (LCCA) demonstrated that preventive maintenance under PRIM reduced total life cycle costs by 28-35% compared to reactive maintenance models. The establishment of the Road Traffic and Transportation Forum (FLLAJ) and implementation of the Anti-Corruption Action Plan (ACAP) strengthened transparency, accelerated complaint resolution within seven working days, and enhanced public trust. The PRIM model successfully aligned technical efficiency with governance innovation while directly contributing to multiple Sustainable Development Goals (SDGs) 9, 11, and 16. The findings offer practical lessons for other developing regions seeking to institutionalize integrated infrastructure governance frameworks that simultaneously optimize service quality, fiscal sustainability, and institutional accountability.
Adaptive Robust Confidence Bands on Local Polynomial Regression Using Residual Bootstrap Percentiles
Ensuring reliable inference in local polynomial regression requires robust methods that can manage data irregularities, particularly outliers. This study introduces an adaptive robust approach for constructing confidence bands using residual bootstrap percentiles. Two robust weighting techniques (Huber and Tukey) were applied to address different levels of data contamination. The method was evaluated using both simulated datasets and real-world observations involving fluctuating patterns. Huber weighting produced more stable and narrower confidence bands under moderate anomalies, while Tukey weighting was more effective in handling extreme deviations. These differences arise because Huber downweights moderate residuals proportionally, whereas Tukey aggressively suppresses extreme outliers. Smoothing parameters were optimized through cross-validation to balance bias and variance effectively. This approach enhances the robustness of nonparametric regression because it maintains consistent confidence coverage despite data imperfections, offering a reliable tool for statistical inference in complex datasets.
Numerical Simulation of Deep Mixed Columns Beneath the Heavy-Haul Railway Embankment
One approach used to modify loose sand settlement under railway embankment involves deep mixed columns (DMCs). The geometrical properties of DMCs, such as the length, diameter, and spacing between adjacent columns, affect the distribution of load, failure mechanism, and ultimate resistance of a foundation constructed above soil reinforcement by this method. Numerical analysis is used as an alternative to laboratory modelling to minimize the cost and simplify the creation of complex DMC geometries in the lab. In this research, 3D Plaxis models are used to examine the influence of DMCs constructed under the embankment of a heavy-haul railway. The model used in this study has been calibrated and adjusted based on historical data from experimental tests on silty sand with low relative density. The study results offer details on how the bearing capacity of loose soil below train embankments is affected by the length and spacing of columns under static and dynamic loads.