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All Journal Journal Emerging Technologies in Education Journal of Computer Science Advancements Techno Agriculturae Studium of Research
Scott, James
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education 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.
CRISPR/CAS9-MEDIATED GENETIC ENGINEERING FOR DEVELOPING SALINITY-TOLERANT RICE VARIETIES FOR INDONESIAN COASTAL AGRICULTURE Scott, James; Williams, Sarah; Martin, David
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2964

Abstract

Salinity intrusion in coastal agricultural areas has become a major constraint to rice production in Indonesia, driven by climate change, sea-level rise, and unsustainable land management practices. High soil salinity adversely affects rice growth, yield stability, and food security, particularly in coastal regions that depend heavily on rice cultivation. Conventional breeding approaches for developing salinity-tolerant rice varieties are often time-consuming and limited by genetic complexity. Advances in genome editing technologies, particularly CRISPR/Cas9, offer a precise and efficient alternative for accelerating crop improvement. The objective of this study is to develop salinity-tolerant rice varieties suitable for Indonesian coastal agriculture through CRISPR/Cas9-mediated genetic engineering targeting key genes associated with salt stress tolerance. This research employed an experimental laboratory-based design combined with controlled greenhouse evaluation. Specific salinity-responsive genes were identified and edited using the CRISPR/Cas9 system. Transgenic rice lines were generated and screened for successful gene edits using molecular analysis techniques. Edited lines were subsequently evaluated under saline and non-saline conditions to assess physiological responses, growth performance, and yield-related traits. The results demonstrate that CRISPR/Cas9-edited rice lines exhibited enhanced tolerance to saline stress, indicated by improved germination rates, higher chlorophyll content, better ion homeostasis, and increased biomass compared to non-edited controls. Several edited lines maintained stable growth and yield under moderate to high salinity levels, confirming the effectiveness of targeted gene modification. In conclusion, CRISPR/Cas9-mediated genetic engineering shows strong potential for developing salinity-tolerant rice varieties tailored to Indonesian coastal environments. This approach provides a rapid and precise strategy to enhance rice resilience, support sustainable coastal agriculture, and strengthen national food security under changing climatic conditions.
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 Clark, Emma Davis, Olivia Green, Jessica Martin, David Williams, Sarah