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CLIMATE CHANGE AND FOREST ECOSYSTEMS: IMPACTS AND ADAPTATION STRATEGIES Seojin, Choi; Minho, Kim; Jiwon, Lee
Journal of Selvicoltura Asean Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i2.2036

Abstract

Climate change is one of the most critical challenges facing global ecosystems, with forest ecosystems being particularly vulnerable to its impacts. The alteration of climate patterns, such as increased temperatures, altered precipitation patterns, and extreme weather events, has led to significant disruptions in forest biodiversity, carbon storage, and ecosystem services. This study investigates the effects of climate change on forest ecosystems and explores potential adaptation strategies to mitigate these impacts. The primary objective of this research is to assess the impacts of climate change on forest ecosystems and identify viable adaptation measures to ensure ecosystem resilience. This research employs a combination of qualitative and quantitative methods, including field observations, data analysis from climate models, and review of existing literature on forest ecology and climate adaptation strategies. The findings indicate that climate change has led to shifts in species distribution, changes in forest composition, and increased susceptibility to pests and diseases. Additionally, forest degradation and loss of biodiversity have been observed in several regions. Adaptation strategies, such as assisted migration, improved forest management practices, and conservation efforts, have shown potential to enhance the resilience of forest ecosystems. In conclusion, while climate change poses significant threats to forest ecosystems, proactive adaptation strategies can mitigate some of the adverse effects. It is essential to integrate climate change considerations into forest management policies to promote long-term ecosystem sustainability.    
ARTIFICIAL INTELLIGENCE IN BIOMEDICAL NANOTECHNOLOGY: FROM DIAGNOSIS TO THERAPY OPTIMIZATION Seojin, Choi; Doudou, Sabrina; Muntasir, Muntasir
Journal of Biomedical and Techno Nanomaterials Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v3i1.3210

Abstract

The integration of Artificial Intelligence (AI) with biomedical nanotechnology is revolutionizing medical diagnostics and therapy optimization. The combination of AI’s computational power with the unique properties of nanomaterials enables more accurate disease detection, personalized treatment plans, and optimized therapeutic outcomes. Traditional diagnostic techniques often suffer from limitations in sensitivity, specificity, and the ability to offer personalized treatments. Nanotechnology, particularly through the development of nanoparticle-based systems, offers significant improvements in targeting, drug delivery, and imaging. AI can further enhance these capabilities by enabling real-time data analysis, predictive modeling, and personalized medicine approaches. This research explores the applications of AI in biomedical nanotechnology, focusing on its role in diagnosis, therapy optimization, and the potential for improving patient outcomes. The study employs a comprehensive review of existing literature, case studies, and computational models to assess the impact of AI-driven nanotechnologies in clinical settings. The results highlight the promising outcomes in disease diagnosis, particularly in oncology, and the potential for AI to optimize therapeutic strategies by analyzing large-scale patient data. In conclusion, the integration of AI and biomedical nanotechnology offers substantial advancements in precision medicine, facilitating more accurate, efficient, and personalized healthcare solutions.