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All Journal Journal of World Future Medicine, Health and Nursing Journal of Computer Science Advancements Journal of Tecnologia Quantica
Green, Jessica
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Computer Science & IT Health Professions Medicine & Pharmacology Nursing Physics Public Health Veterinary

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Adaptation to Social Change and Urbanization: Community-Based Health Strategies for Public Health Andarmoyo, Sulistyo; Davis, Olivia; Green, Jessica
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i2.1912

Abstract

Social change and rapid urbanization have profoundly impacted public health systems, presenting challenges such as rising health disparities, increased prevalence of non-communicable diseases, and the strain on healthcare infrastructure. In response to these challenges, community-based health strategies have gained traction as effective approaches to improving public health outcomes. This research explores the role of community-based health strategies in adapting to the demands of social change and urbanization, with a focus on their impact on health equity, accessibility, and health system resilience. A mixed-methods approach was employed, combining quantitative data from health surveys and qualitative interviews with community leaders and healthcare providers to assess the effectiveness of community-driven health initiatives. The findings indicate that community-based programs significantly improved health outcomes, especially in urban areas facing overcrowding and limited access to healthcare. These programs enhanced health literacy, preventive care, and collaborative efforts between communities and healthcare providers. The study concludes that community-driven health models offer a sustainable solution to public health challenges in rapidly urbanizing regions. The research highlights the importance of integrating these strategies into urban health policy to ensure a more resilient and equitable healthcare system.
Quantum Cryptography to Secure Financial Data Williams, Sarah; Martin, David; Green, Jessica
Journal of Tecnologia Quantica Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i6.1702

Abstract

The background of this research focuses on the security challenges of financial data in the era of quantum computing, which can threaten traditional encryption systems. With the advancement of quantum computing technology, quantum cryptography is considered a potential solution to protect sensitive data from more sophisticated eavesdropping threats. The purpose of this study is to evaluate the effectiveness of the quantum key distribution protocol (QKD) in securing financial data and analyze its advantages and disadvantages in this context. The method used is a performance simulation of the three main QKD protocols (BB84, E91, and B92) to measure key delivery time, security level, and computing resource usage. The results show that the E91 protocol offers a higher level of security than BB84 and B92, although it requires longer delivery times and more resources. The conclusion of this study emphasizes that although quantum cryptography has great potential for securing financial data, its practical application still faces various challenges, especially in terms of efficiency and necessary resources. Further research is needed to optimize these protocols and overcome technical and cost barriers to implementation on a financial industry scale.
THE AI ENERGY DILEMMA: FINDING THE MIDDLE GROUND BETWEEN HIGH PERFORMANCE AND ECO-FRIENDLINESS Scott, James; Davis, Olivia; Green, Jessica
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.3337

Abstract

The exponential escalation of computational requirements for training and deploying Deep Learning models has precipitated an energy crisis, necessitating a critical reevaluation of the trade-off between algorithmic performance and environmental sustainability. This study aims to reconcile these conflicting demands by developing and validating a novel Dynamic Energy-Aware Pruning (DEAP) framework designed to maximize inference efficiency without compromising predictive accuracy. Employing a rigorous quantitative experimental design, we benchmarked state-of-the-art neural architectures, including ResNet-50 and Large Language Models (LLMs), across diverse hardware environments. The research utilized real-time telemetry to measure total energy consumption (Joules), thermal output, and carbon intensity () against standard accuracy metrics. Empirical results demonstrate that the proposed framework achieved a 42% reduction in energy consumption and stabilized hardware thermals, while maintaining predictive performance within a strict 1.5% non-inferiority margin compared to dense baselines. We definitively conclude that algorithmic sparsity effectively decouples high-level intelligence from excessive power usage, establishing a viable engineering paradigm for “Green AI” that aligns the trajectory of artificial intelligence with global decarbonization targets.
Co-Authors Andarmoyo, Sulistyo Davis, Olivia Martin, David Scott, James Williams, Sarah