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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 85 Documents
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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.
MAPPING THE EFFICACY OF BIO-BASED CARBON CAPTURE TECHNOLOGIES IN MITIGATING GREENHOUSE GAS EMISSIONS FOR INDUSTRIAL SUSTAINABILITY Achmad Agus Salim; Muhammad Taswin; Muhammad Yassir; Daiki Nishida
Research of Scientia Naturalis Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i4.4324

Abstract

Industrial decarbonization requires carbon-management technologies that can reduce residual emissions without creating excessive energy, resource, or land burdens. This study aimed to map and compare the efficacy of bio-based carbon capture technologies for mitigating greenhouse gas emissions and supporting industrial sustainability. A systematic review, evidence mapping, and quantitative synthesis were conducted on 84 studies published between 2010 and 2026, covering microalgae, microbial fixation, carbonic-anhydrase-assisted capture, biomass-derived sorbents, biochar, and bioenergy with carbon capture and storage. Data were evaluated across technical performance, environmental effectiveness, economic feasibility, industrial scalability, and storage permanence. The findings showed that carbonic-anhydrase-assisted systems and bioenergy with carbon capture and storage achieved the strongest gross capture performance, while biochar and bioenergy with carbon capture and storage provided greater long-term storage potential. Microalgae and microbial systems offered valuable co-benefits through wastewater treatment, nutrient recovery, and biomass production, although energy-intensive cultivation and downstream processing reduced their net climate benefits. The study concludes that capture efficiency alone is insufficient for evaluating technological efficacy. Credible industrial deployment requires life-cycle greenhouse gas assessment, durable carbon storage, sustainable feedstock sourcing, realistic scale-up evidence, and integration with broader decarbonization strategies across diverse industrial sectors and operating conditions under transparent monitoring, reporting, and verification frameworks worldwide.
DEEP LEARNING ARCHITECTURES FOR AUTOMATED CLASSIFICATION OF SATELLITE IMAGERY TO MONITOR GLOBAL DEFORESTATION TRENDS AND LAND USE CHANGES Sa’dianoor Sa’dianoor; Chen Mei; Yang Xiang
Research of Scientia Naturalis Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i4.4327

Abstract

Global deforestation and land-use change require timely, accurate, and transferable monitoring systems capable of processing extensive satellite archives. This study aimed to evaluate deep learning architectures for automated classification of satellite imagery, detection of forest change, and identification of post-deforestation land uses across diverse ecological regions. A comparative experimental design was applied to 120,000 labelled image patches derived from Landsat, Sentinel-1, and Sentinel-2 data representing South America, Southeast Asia, Central Africa, North America, Europe, and Oceania. Nine architectures, including convolutional, recurrent, Siamese, Transformer-based, and hybrid models, were assessed using spatial, temporal, and cross-regional validation. The hybrid CNN–Transformer achieved the strongest overall accuracy, calibration, and transfer performance, while radar–optical fusion improved classification in persistently cloudy tropical environments. Multitemporal models reduced confusion between permanent deforestation, seasonal disturbance, harvesting, and regeneration. Geographic holdout testing produced substantial performance declines across all architectures, demonstrating that random partitioning overestimated operational reliability. The study concludes that effective global deforestation monitoring requires multimodal data, temporal context, uncertainty estimation, active learning, and geographically independent evaluation. Automated classification can strengthen forest surveillance, yet expert verification and statistically adjusted area estimation remain essential for responsible environmental decision-making and policy implementation under rapidly changing climatic, technological, and land-management conditions worldwide over time.

Page 9 of 9 | Total Record : 85

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