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All Journal International Journal of Educational Narratives Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials
Oliver Harris
University of Queensland
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Education Physics Social Sciences Other

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OPTIMIZATION OF RENEWABLE ENERGY GRIDS USING COMPUTATIONAL INTELLIGENCE AND HEURISTIC ALGORITHMS FOR RESILIENT AND SUSTAINABLE POWER DISTRIBUTION Sulaiman Sulaiman; Johannes Muller; Oliver Harris
Research of Scientia Naturalis Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i2.4169

Abstract

Rapid expansion of renewable energy resources has increased the operational complexity of modern electricity grids as intermittent generation, distributed energy resources, and dynamic demand challenge conventional optimization methods. This study evaluated the effectiveness of computational intelligence and heuristic algorithms in optimizing renewable energy grids by improving power system resilience, renewable energy utilization, operational efficiency, and adaptive grid management. A mixed-methods sequential explanatory design was applied using 2,400 simulation scenarios across forty benchmark renewable energy distribution systems with varying renewable penetration, battery storage capacities, distributed generation configurations, and demand response conditions. Quantitative analyses included structural equation modeling, hierarchical regression, multivariate analysis, mediation, and moderation analyses, while qualitative evidence from expert interviews, engineering discussions, and technical document reviews was examined through thematic analysis. The findings showed that hybrid computational intelligence algorithms consistently outperformed conventional optimization approaches by enhancing renewable energy utilization, reducing power losses, improving voltage stability, accelerating computational convergence, and strengthening grid resilience. Distributed energy coordination and renewable forecasting further improved optimization performance under uncertain operating conditions. Overall, intelligent optimization represents an adaptive cyber-physical framework integrating renewable generation, energy storage, and demand response into resilient and sustainable electricity distribution systems. The proposed framework provides practical guidance for utility operators, system planners, and policymakers to accelerate reliable renewable energy integration while supporting long-term decarbonization and sustainable power system transformation.
Telling the Land: Aboriginal Educational Narratives and Curriculum Integration in Australian Schools Oliver Harris; Sarah Taylor; Thomas Mitchell
International Journal of Educational Narratives Vol. 3 No. 3 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i3.2211

Abstract

Background. Efforts to meaningfully integrate Aboriginal perspectives into Australian school curricula remain uneven and contested, often constrained by systemic limitations and a lack of culturally informed pedagogical frameworks. Aboriginal narratives, particularly those tied to Country, embody holistic systems of knowledge that challenge Western linear constructions of curriculum and offer alternative modes of understanding land, identity, and education. Purpose. This study explores how Aboriginal educational narratives are interpreted and integrated into curriculum practice by both Indigenous and non-Indigenous educators across diverse Australian school settings. Method. Employing a qualitative, multi-site case study approach, the research involved interviews with 22 educators and curriculum leaders, alongside analysis of classroom materials and reflective teaching journals. Results. The findings reveal that successful integration depends on deep, relational engagement with community knowledge holders, an ethic of cultural humility, and a willingness to reconfigure disciplinary boundaries. Educators who engaged in collaborative curriculum-making reported greater confidence in embedding Indigenous perspectives in ways that respect narrative sovereignty and pedagogical integrity. Conclusion. The study concludes that Aboriginal storytelling offers not only content but a method—transforming curriculum into a site of shared responsibility, ethical dialogue, and place-based learning.  
AGRICULTURAL SUSTAINABILITY UNDER CLIMATE VARIABILITY: COUPLING CROP PHYSIOLOGY WITH PREDICTIVE STATISTICAL MODELS Ren Suzuki; Samantha Gonzales; Oliver Harris
Research of Scientia Naturalis Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i1.3547

Abstract

Agricultural systems are increasingly challenged by climate variability, which disrupts crop productivity and threatens long-term sustainability. Existing approaches often separate physiological understanding from predictive modeling, limiting their ability to capture the complexity of crop responses to environmental stress. This study aims to develop an integrative framework that couples crop physiological processes with predictive statistical models to improve the accuracy and interpretability of agricultural sustainability assessments. A mixed-methods design was employed, combining field-based physiological measurements with advanced statistical and machine learning modeling. Data were collected across multiple agricultural sites, including climatic variables, soil conditions, and key physiological indicators such as photosynthetic rate, stomatal conductance, and water-use efficiency. Predictive models were developed and evaluated using regression analysis and machine learning techniques with cross-validation procedures. Results indicate that models incorporating physiological variables significantly outperform those based solely on climatic data in predicting crop yield. Physiological indicators function as critical mediators between environmental stress and productivity, enhancing both predictive accuracy and explanatory depth. Nonlinear modeling approaches further improve performance by capturing complex interactions among variables. Findings demonstrate that integrating crop physiology with predictive modeling provides a robust framework for understanding and managing agricultural systems under climate variability. This approach supports more adaptive and sustainable agricultural strategies.
ARTIFICIAL INTELLIGENCE IN MEDICINE: A DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK FOR PATHOLOGICAL IMAGE ANALYSIS AND CANCER GRADING James Smith; Oliver Harris; Dito Anurogo
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2480

Abstract

The histopathological analysis of tissue slides is the gold standard for cancer diagnosis and grading. However, this process is labor-intensive, time-consuming, and prone to inter-observer variability, which can affect clinical outcomes. The advent of artificial intelligence (AI), particularly deep learning, presents a transformative opportunity to enhance diagnostic precision and efficiency in pathology. This study aimed to develop, train, and validate a deep learning convolutional neural network (CNN) for the automated analysis of pathological images to accurately classify malignancies and provide reliable cancer grading. A robust CNN model was trained on a comprehensive, curated dataset of thousands of annotated digital histopathology slides from multiple cancer types. The model’s performance was rigorously evaluated against the consensus diagnoses of expert pathologists using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Our developed CNN model demonstrated exceptional performance, achieving an overall accuracy of 98.7% in distinguishing malignant from benign tissues. For cancer grading, the model yielded a Cohen’s Kappa score of 0.92, indicating almost perfect agreement with expert pathologists. The model also showed high robustness to variations in staining and image acquisition protocols. This research confirms that a deep learning CNN can function as a highly accurate and reliable tool for automated pathological image analysis and cancer grading. Integrating such AI systems into clinical workflows could significantly augment the capabilities of pathologists, leading to improved diagnostic consistency, reduced workload, and ultimately, better patient care.
Co-Authors Dito Anurogo James Smith Johannes Muller Ren Suzuki Samantha Gonzales Sarah Taylor Sulaiman Sulaiman Thomas Mitchell