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All Journal World Psychology Journal of World Future Medicine, Health and Nursing Journal of Computer Science Advancements Journal of Tecnologia Quantica Techno Agriculturae Studium of Research
Green, Jessica
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Health Professions Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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 1 
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.
THE PSYCHOLOGY OF FALSE CONFESSIONS: INVESTIGATING THE COGNITIVE AND EMOTIONAL FACTORS BEHIND INVOLUNTARY ADMISSIONS Satioso, Lucy Lidiawati; Williams, Sarah; Green, Jessica
World Psychology Vol. 5 No. 2 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/wp.v5i2.1258

Abstract

False confessions have long been a concern in the criminal justice system, yet the cognitive and emotional factors that drive individuals to confess to crimes they did not commit remain insufficiently explored. This research investigates the psychological underpinnings of false confessions, focusing on the cognitive overload and emotional stress experienced by suspects during interrogations. The study aims to examine how these psychological factors contribute to involuntary admissions and how they can be mitigated to prevent wrongful convictions. A mixed-methods approach was employed, combining qualitative interviews with legal professionals, psychologists, and law enforcement officers, along with a case study analysis of documented false confession cases. The findings reveal that emotional stress, particularly fear and anxiety, combined with cognitive overload during prolonged interrogations, significantly increases the likelihood of false confessions. The study concludes that false confessions are not solely the result of coercive interrogation techniques but are also deeply influenced by emotional and cognitive vulnerabilities. The research suggests the need for reform in interrogation practices, including better psychological safeguards and more effective legal protections for suspects.
PRECISION LIVESTOCK FARMING: INNOVATIONS IN FEED MANAGEMENT AND ANIMAL HEALTH FOR OPTIMIZED PRODUCTION EFFICIENCY Williams, Sarah; Green, Jessica; Turner, Michael
Techno Agriculturae Studium of Research Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v3i1.3611

Abstract

Precision Livestock Farming (PLF) has emerged as a strategic approach to address efficiency, sustainability, and animal welfare challenges in modern livestock systems. Advances in sensor technologies, data analytics, and automated decision-support tools have enabled real-time monitoring of feed intake, animal behavior, and health status, yet empirical evidence on their integrated impacts remains fragmented. This study aims to evaluate how innovations in precision feed management and animal health monitoring contribute to optimized production efficiency in intensive livestock systems. The research employed a quantitative experimental design combined with farm-level monitoring, involving sensor-based feed delivery systems, wearable health sensors, and automated data analytics across selected commercial livestock farms. Performance indicators included feed conversion ratio, growth or productivity rates, health incidence, and resource-use efficiency. The results demonstrate that precision-managed feeding significantly reduced feed waste while improving feed conversion efficiency, whereas continuous health monitoring enabled early disease detection and reduced morbidity rates. Integrated PLF systems produced measurable gains in overall productivity and operational efficiency compared to conventional management practices. The study concludes that the synergistic application of precision feed management and animal health technologies enhances production efficiency while supporting animal welfare and resource sustainability. These findings highlight the potential of PLF as a transformative pathway for resilient and data-driven livestock production systems.
Co-Authors Andarmoyo, Sulistyo Davis, Olivia Martin, David Satioso, Lucy Lidiawati Scott, James Turner, Michael Williams, Sarah