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Research of Scientia Naturalis
Published by Yayasan Adra Karima Hubbi
ISSN : 30479932     EISSN : 30479940     DOI : 10.70177/scientia
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Physics
Research of Scientia Naturalis is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the research fields of Mathematics and Natural Sciences. All publications provide breadth of coverage appropriate to a wide readership in Mathematics and Natural Sciences research depth to inform specialists in that area. We feel that the rapidly growing Research of Scientia Naturalis community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 7 Documents
 1 
Search results for , issue "vol. 3 no. 3 (2026)" : 7 Documents clear
INTEGRATION OF REMOTE SENSING DATA AND GEOGRAPHIC INFORMATION SYSTEM FOR PRECISE MAPPING OF CARBON SEQUESTRATION POTENTIAL IN FOREST Obed Patiung; Nilam Atsirina Krisnaputri; Li Wei
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4015

Abstract

Accurate assessment of forest carbon sequestration potential has become increasingly important for climate change mitigation, sustainable forest management, and environmental policy development. Advances in Remote Sensing and Geographic Information System (GIS) technologies offer new opportunities to improve the precision and efficiency of carbon mapping across large and heterogeneous forest landscapes. This study aimed to examine the effectiveness of integrating remote sensing data and GIS techniques for precise mapping of carbon sequestration potential in forest ecosystems and to identify the environmental factors influencing carbon distribution patterns. A quantitative geospatial approach was employed using multisource satellite imagery, vegetation indices, biomass estimates, topographic variables, land-cover data, and field validation measurements. Spatial modeling, statistical analysis, and GIS-based overlay techniques were applied to evaluate carbon sequestration potential across the study area. Results revealed substantial spatial variation in carbon storage capacity, with high-carbon zones concentrated in dense and ecologically intact forests. Vegetation density, biomass accumulation, forest cover percentage, and topographic characteristics showed significant positive relationships with carbon sequestration estimates. Integrated modeling achieved high predictive accuracy and demonstrated strong agreement with field observations. Findings indicate that combining remote sensing and GIS technologies provides a reliable framework for identifying carbon-rich forest areas, supporting evidence-based conservation planning, improving carbon accounting practices, and strengthening climate change mitigation strategies through more accurate spatial assessment of forest carbon resources.
ARTIFICIAL INTELLIGENCE DRIVEN PREDICTIVE ANALYTICS FOR SUSTAINABLE WATER RESOURCE MANAGEMENT IN RAPIDLY URBANIZING REGIONS ACROSS SOUTHEAST ASIA Luis Santos; Muhammad Firdaus Abduh; Liam Wilson
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4115

Abstract

Rapid urbanization in Southeast Asia has exerted unprecedented pressure on water resources, leading to inefficiencies, resource depletion, and challenges in maintaining water quality. Conventional water management approaches often struggle to meet dynamic demand patterns and respond to infrastructure constraints, limiting sustainable urban water governance. This study aims to evaluate the role of artificial intelligence-driven predictive analytics in enhancing water resource management by forecasting demand, detecting system vulnerabilities, and optimizing allocation strategies in rapidly growing urban regions. A mixed-methods research design was employed, integrating quantitative hydrological and consumption datasets with real-time sensor data, machine learning-based predictive modeling, and qualitative expert insights. Data were analyzed through scenario-based simulations, regression analysis, and cross-validation to assess predictive performance and operational effectiveness. Results indicate that AI-enabled predictive analytics significantly reduces non-revenue water from 32% to 19%, improves reservoir stability from 68% to 81%, enhances water quality indices from 74 to 88, and increases leakage detection from 45% to 78%. Case studies demonstrate the practical applicability of predictive alerts in proactive infrastructure management and resource optimization. The study concludes that AI-driven predictive analytics provides a transformative tool for sustainable urban water governance, enabling proactive, efficient, and adaptive management strategies in complex urban environments.
MULTISCALE MODELING OF FLUID-STRUCTURE INTERACTION IN WIND TURBINE BLADES TO IMPROVE ENERGY CONVERSION EFFICIENCY IN RENEWABLE SYSTEMS Kaito Tanaka; Sulaiman Sulaiman; Lars Jensen
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4211

Abstract

The increasing demand for renewable electricity drives the need to improve wind turbine efficiency under complex aerodynamic and structural conditions. Numerical approaches often separate airflow analysis and structural response, thus reducing the accuracy of predicting aeroelastic behavior. This study aims to analyze the effectiveness of multiscale fluid-structure interaction (FSI) modeling in improving wind turbine blade performance through integrated aerodynamic, structural, and material analysis. Using a mixed-method sequential explanatory design, the study tested 2,800 simulation scenarios on 48 blade configurations under varying wind speeds, turbulence intensities, and composite material properties. Quantitative analysis included multivariate statistics and hierarchical regression, while qualitative thematic analysis was drawn from expert interviews and engineering workshops. Results show that a fully coupled multiscale FSI model consistently outperforms conventional approaches in improving aerodynamic efficiency, computational convergence, structural stability, vibration damping, and fatigue life prediction. Composite material optimization enhanced structural robustness under dynamic conditions. This framework provides practical guidance for designers and engineers to optimize renewable energy conversion through advanced multiphysics simulations.
INTEGRATED BIOREMEDIATION STRATEGIES FOR HEAVY METAL CONTAMINATION IN INDUSTRIAL WASTEWATER USING GENETICALLY ENGINEERED MICROBIAL CONSORTIA Achmad Agus Salim; Han Jiwon; Seo Jiwon
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4214

Abstract

Industrial activities generate heavy metal waste that challenges conventional methods due to their high cost and low efficiency. This study aims to evaluate the effectiveness of an integrated bioremediation strategy using a genetically engineered microbial consortium in removing heavy metal contaminants while simultaneously improving reactor stability, biosafety, and environmental sustainability. Using a mixed-methods sequential explanatory design, this study tested 420 industrial wastewater samples and 180 laboratory- and pilot-scale bioreactors. The bioreactors represented three treatments: the conventional method, the natural microbial consortium, and the genetically engineered microbial consortium. Quantitative analysis involved descriptive statistics, structural equation modeling (SEM), hierarchical regression, and mediation-moderation analysis. Qualitative data from expert interviews, operational observations, biosafety assessments, and regulatory documents were analyzed thematically. The results demonstrated that the genetically engineered microbial consortium significantly improved heavy metal removal efficiency, microbial viability, metabolic stability, biosorption capacity, reactor performance, and environmental sustainability. Microbial viability was found to partially mediate the relationship between genetic engineering and treatment efficiency, while reactor optimization strengthened the influence of metabolic stability on remediation performance. In conclusion, the sustainability of industrial wastewater treatment depends on the coordinated integration of microbial genetic engineering, ecological cooperation, reactor optimization, biosafety management, and environmental governance to provide a scalable bioremediation framework.
QUANTUM COMPUTING APPLICATIONS IN SOLVING COMPLEX NONLINEAR EQUATIONS FOR ADVANCING COMPUTATIONAL FLUID DYNAMICS IN AEROSPACE ENGINEERING Joni Wilson Sitopu; Darwan Edyanto Saragih; Li Wei
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4237

Abstract

Increasing complexity in computational fluid dynamics (CFD) simulations demands faster, more accurate, and scalable approaches for solving highly nonlinear equations. Conventional high-performance computing remains effective in aerospace engineering but faces limitations in large-scale turbulence modeling, compressible flows, multiphysics interactions, and optimization requiring intensive iterations. This study evaluated the effectiveness of hybrid quantum-classical computing for solving complex nonlinear CFD equations and improving aerospace simulation performance. A mixed-methods sequential explanatory design involved 540 computational benchmark simulations and 240 experimental scenarios covering conventional solvers, hybrid quantum-classical optimization, and quantum-enhanced nonlinear solvers. Quantitative analyses used descriptive statistics, structural equation modeling, hierarchical regression, mediation, and moderation analysis, while qualitative evidence from expert interviews, computational observations, software evaluations, and document reviews underwent thematic analysis. Results showed that hybrid quantum-classical computing significantly improved convergence efficiency, numerical accuracy, turbulence prediction, scalability, residual error reduction, and simulation reliability. Hybrid optimization partially mediated the effect of quantum algorithms on computational efficiency, while mesh optimization strengthened convergence and engineering accuracy. These findings support integrating quantum computing with established CFD methods to enable faster optimization, stronger prediction, and scalable next-generation aerospace simulations.
ADVANCING SUSTAINABLE AGRICULTURE THROUGH NANO-FERTILIZERS ANALYZING SOIL HEALTH AND CROP PRODUCTIVITY IN TROPICAL ECOSYSTEMS Rahmawati Rahmawati; Siri Lek; Ton Kiat
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4248

Abstract

Sustainable rice production in tropical ecosystems is constrained by low nitrogen-use efficiency, nutrient losses, declining soil quality, and increasing dependence on conventional fertilizers. This study aimed to evaluate the effects of nano-nitrogen fertilizer on soil health, nitrogen efficiency, and rice productivity under tropical field conditions. A randomized complete block design was implemented with six treatments: no nitrogen, 100% conventional urea, and nano-nitrogen fertilizer applied at 25%, 50%, 75%, and 100% of the recommended nitrogen rate, each replicated four times. Crop growth, chlorophyll content, nitrogen uptake, yield components, grain yield, agronomic efficiency, and selected physical, chemical, and biological soil indicators were analyzed using analysis of variance, post hoc comparisons, correlation, and regression. Results showed that the 75% nano-nitrogen treatment produced the highest grain yield, reaching 7.24 t ha?1, and exceeded full-rate conventional urea while improving productive tillers, chlorophyll status, nitrogen uptake, microbial biomass, urease activity, organic carbon, and aggregate stability. The 100% nano-nitrogen rate increased nitrogen uptake but provided no additional yield advantage, indicating a nonlinear response. The study concludes that optimized nano-nitrogen application can reduce fertilizer demand while improving rice productivity and soil functioning, although multi-season environmental and safety assessments remain necessary before widespread adoption across diverse tropical production environments.
EXPLORING SUPERCONDUCTIVITY PHENOMENA IN HIGH-TEMPERATURE CERAMIC MATERIALS FOR POTENTIAL APPLICATIONS IN FUTURE ENERGY STORAGE SYSTEMS Saripuddin M; Miku Fujita; Daiki Nishida
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4399

Abstract

High-temperature ceramic superconductors offer promising pathways for future energy storage because their near-zero electrical resistance and strong magnetic behavior may support efficient high-current applications. This study aimed to compare superconducting phenomena in YBCO, Bi-2212, and Bi-2223 ceramics and evaluate their material-level relevance to prospective superconducting magnetic energy storage systems. A comparative laboratory-based experimental design was employed using structural characterization, scanning electron microscopy, four-probe resistivity measurements, magnetic analysis, critical current evaluation, thermal cycling, and field-dependent testing. The results showed that Bi-2223 achieved the highest superconducting onset temperature, whereas YBCO exhibited the strongest overall functional profile through a narrower transition width, higher critical current density, stronger magnetic shielding, and more stable current transport. Bi-2212 demonstrated reproducible superconductivity but showed comparatively weaker grain connectivity and lower current-carrying performance. Correlation analysis further indicated that phase purity, grain connectivity, and flux-pinning behavior were strongly associated with superconducting performance. The study concludes that critical temperature alone is insufficient for assessing energy-storage suitability. A multidimensional evaluation integrating thermal, electrical, magnetic, and microstructural properties provides a more reliable basis for identifying promising superconducting materials for future storage technologies while highlighting the need for conductor-scale and device-level validation under realistic cryogenic, mechanical, and high-field operating conditions over extended cycles.

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