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All Journal Journal Emerging Technologies in Education Journal of World Future Medicine, Health and Nursing Journal of Computer Science Advancements Journal of Selvicoltura Asean
Davis, Olivia
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Health Professions Medicine & Pharmacology Nursing Public Health Veterinary Other

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Utilizing Technology in Multicultural Education: Experiences from Canadian Schools Clark, Emma; Scott, James; Davis, Olivia
Journal Emerging Technologies in Education Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jete.v3i1.2128

Abstract

Background. In the context of increasingly diverse classrooms, Canadian schools face both the challenge and opportunity of fostering inclusive learning environments through multicultural education. As technology becomes more integrated into pedagogy, its role in addressing linguistic, cultural, and social differences gains prominence.Purpose. This study explores how educators in Canadian schools utilize digital tools to support multicultural education and promote equity among students from diverse backgrounds. The research aims to identify effective strategies, tools, and practices that enhance cultural inclusivity and student engagement through technology-enhanced instruction.Method. A qualitative multiple-case study approach was employed, involving interviews with 28 teachers, classroom observations across six schools, and analysis of institutional technology integration plans. Results. The findings indicate that technology, when used intentionally, facilitates culturally responsive teaching through language support apps, collaborative platforms, and digital storytelling tools. However, the study also reveals disparities in access, digital literacy, and institutional readiness, which hinder equitable outcomes.Conclusion. The study concludes that leveraging technology for multicultural education requires not only pedagogical innovation but also systemic support, teacher training, and inclusive design principles. These insights offer practical implications for educators and policymakers seeking to enhance diversity and inclusion in digital learning environments.
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.
The Impact of Selective Logging on Forest Structure and Function Davis, Olivia; Thompson, Ethan; Clark, Emma
Journal of Selvicoltura Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i6.1674

Abstract

Selective logging is a prevalent forest management practice aimed at balancing timber production and conservation. However, its effects on forest structure and function remain a topic of significant concern. This study aims to evaluate the impact of selective logging on the biodiversity, biomass, and ecological functions of forest ecosystems. We employed a comparative analysis method, where forest plots subjected to selective logging were compared to undisturbed control plots. Data were collected on tree species diversity, density, and biomass, alongside assessments of soil health and microclimate conditions. Our findings indicate that selective logging significantly alters forest structure by reducing tree density and species diversity, leading to an overall decline in biomass. Additionally, changes in soil composition and moisture levels were observed, negatively affecting the forest's ecological functions. The results underscore the importance of adopting sustainable logging practices that mitigate adverse effects on forest ecosystems. In conclusion, while selective logging can provide economic benefits, its detrimental impacts on forest structure and function necessitate careful management and monitoring to preserve biodiversity and ecosystem health.
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 Clark, Emma Green, Jessica Scott, James Thompson, Ethan