Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Moeslim Research Technik Research of Scientia Naturalis
Wilson, Amanda
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A METAGENOMIC ANALYSIS OF THE GUT MICROBIOTA IN THE KOMODO DRAGON (VARANUS KOMODOENSIS) AND ITS ROLE IN DIGESTION AND IMMUNITY Thai, Aom; Yamamoto, Sota; Wilson, Amanda
Research of Scientia Naturalis Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i6.2921

Abstract

The Komodo dragon (Varanus komodoensis), the largest living lizard, plays a crucial role in its ecosystem. Understanding its gut microbiota is essential for assessing its digestive efficiency and immune function, yet little is known about the microbial communities within its gastrointestinal system. This study aimed to analyze the gut microbiota of wild and captive Komodo dragons using metagenomic sequencing and to explore its role in digestion and immunity. Fecal and gut content samples were collected from 12 wild and 10 captive Komodo dragons. High-throughput sequencing of the 16S rRNA gene was used to characterize the microbial diversity. The results revealed significant differences in microbiota composition between wild and captive individuals, with wild dragons displaying higher microbial diversity. Dominant phyla in wild Komodo dragons included Firmicutes and Bacteroidetes, while Escherichia and Klebsiella were more prevalent in captive individuals. Additionally, microbial diversity was positively correlated with immune-related gene expression, suggesting that the microbiota plays a role in immune modulation. These findings highlight the importance of diet and environmental factors in shaping the gut microbiota, with implications for conservation and breeding programs. Further research should focus on functional profiling and exploring other microbial groups to fully understand the microbiome's impact on health
ALGORITHMIC INTELLIGENCE IN ENGINEERING DESIGN: INTEGRATING MACHINE LEARNING WITH PHYSICAL MODELING Erwis, Fauzi; Fujita, Miku; Suarnatha, I Putu Dody; Wilson, Amanda
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3467

Abstract

Increasing complexity in engineering systems demands design methodologies that balance computational efficiency, predictive accuracy, and physical reliability. Traditional physics-based simulations ensure mechanistic consistency but are computationally expensive, while purely data-driven machine learning models offer speed yet often lack interpretability and physical compliance. Integrating algorithmic intelligence with physical modeling has therefore emerged as a promising paradigm in advanced engineering design. This study aims to develop and evaluate a hybrid framework that integrates machine learning algorithms with governing physical equations to enhance design performance, robustness, and computational efficiency. A mixed-methods computational design was employed using 15,000 high-fidelity simulation datasets across structural, aerodynamic, and thermal engineering cases. Three modeling configurations—physics-based models, data-driven models, and hybrid physics-informed machine learning models—were comparatively analyzed using performance metrics including mean squared error, R², runtime efficiency, robustness testing, and constraint violation indices. Statistical analyses were conducted to determine significance of performance differences. Hybrid models achieved superior balance, reaching R² = 0.97 with significantly reduced runtime compared to physics-based simulations (p < 0.001), while maintaining substantially lower physical constraint violations than purely data-driven models. Sensitivity and uncertainty analyses confirmed enhanced robustness under parameter perturbation. Algorithmic intelligence integrated with physical modeling represents an epistemologically coherent and practically effective approach, advancing engineering design toward trustworthy, efficient, and physically consistent computational frameworks.
Co-Authors Erwis, Fauzi Fujita, Miku I Putu Dody Suarnatha Thai, Aom Yamamoto, Sota