Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Green Health: Journal of Health Sciences, Nursing and Nutrition Global Science: Journal of Information Technology and Computer Science
G Thippanna
Unknown Affiliation
Computer Science & IT Nursing

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

Found 2 Documents
Search

 1 
Life-Cycle and Health Impact Analysis of Biodegradable Versus Conventional Medical Supplies: A Novel Comparative Study on Environmental Footprint and Worker Exposure Harlis Setiyowati; Irfan Maulana; G Thippanna
Green Health International Journal of Health Sciences Nursing and Nutrition Vol. 1 No. 3 (2024): July: Green Health: International Journal of Health Sciences, Nursing and Nutri
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenhealth.v1i3.263

Abstract

The increasing use of conventional medical supplies has led to significant environmental challenges due to waste accumulation and the chemical exposure risks faced by healthcare workers. Despite ongoing efforts to adopt environmentally friendly materials, there is a lack of comprehensive studies that combine Life-Cycle Assessment (LCA) with direct monitoring of worker exposure. This study aims to compare the environmental and health impacts of biodegradable versus conventional medical supplies by assessing their life-cycle stages, energy consumption, carbon footprint, and hazardous exposure risks. A novel comparative approach was adopted, integrating LCA to evaluate raw material extraction, production, usage, and disposal processes, alongside monitoring the occupational exposure to chemical residues from medical supplies. The results indicate that biodegradable medical supplies reduce environmental footprint by approximately 40%, lower energy consumption, and generate less waste compared to conventional plastics. Additionally, the study shows that biodegradable materials pose a significantly reduced risk of chemical exposure to healthcare workers, offering a safer alternative. However, biodegradable materials present limitations, such as availability, durability, and higher initial costs, which need to be addressed for widespread implementation. The findings emphasize the importance of integrating sustainable practices in healthcare settings, offering actionable insights for hospital management and regulatory bodies. Future research is recommended to further explore the cost-effectiveness of biodegradable materials, conduct large-scale trials, and investigate alternative material types. This study contributes to the growing body of knowledge on environmental sustainability and occupational safety in healthcare, providing a valuable framework for future policy and operational decisions.
Explainable Deep-Reinforcement Learning Framework for Autonomous Traffic Signal Control Integrating V2X Data and Smart Infrastructure Jarot Dian Susatyono; Sofiansyah Fadli; G Thippanna
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.172

Abstract

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.
Co-Authors Harlis Setiyowati Irfan Maulana Jarot Dian Susatyono Sofiansyah Fadli