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All Journal World Psychology Journal of Moeslim Research Technik Journal of Biomedical and Techno Nanomaterials
James Smith
University of Sydney
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences Other

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ENERGY-EFFICIENT POWER ELECTRONICS: DESIGN STRATEGIES FOR SUSTAINABLE ELECTRICAL ENGINEERING Muhammad Firdaus Abduh; Anna Schneider; James Smith
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3985

Abstract

Increasing global energy demand, rapid electrification, and growing environmental concerns have intensified the need for energy-efficient technologies capable of supporting sustainable development. Power electronics plays a crucial role in modern electrical engineering by enabling efficient energy conversion, transmission, and utilization across renewable energy systems, electric vehicles, smart grids, and industrial applications. Persistent challenges related to switching losses, thermal dissipation, and converter inefficiencies continue to limit overall system performance and sustainability outcomes. This study aims to examine design strategies that enhance energy efficiency in power electronic systems and to evaluate their contribution to sustainable electrical engineering. A qualitative literature-based research design employing a systematic review approach was adopted. Relevant peer-reviewed publications published between 2015 and 2025 were analyzed to identify emerging technological trends, efficiency-enhancing mechanisms, and sustainability-oriented design principles. Findings indicate that advanced semiconductor technologies, particularly silicon carbide (SiC) and gallium nitride (GaN), significantly reduce power losses and improve conversion efficiency. Optimized converter topologies, intelligent control algorithms, and advanced thermal management systems further enhance system reliability and operational performance. Integrated implementation of these strategies produces greater efficiency gains than isolated technological improvements. The study concludes that sustainable electrical engineering requires a holistic design framework that combines technological innovation, system optimization, and environmental considerations. Such an approach can accelerate the development of highly efficient, reliable, and environmentally responsible electrical energy systems.
DIGITAL TRANSFORMATION AND EMPLOYEE ADAPTABILITY: A STUDY OF PSYCHOLOGICAL FACTORS INFLUENCING WORKPLACE INNOVATION Dodi Setiawan; James Smith; Jack Davis
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.v5i2.1245

Abstract

Digital transformation has become a critical factor for organizational success, yet the ability of employees to adapt to technological changes remains a significant challenge. Employee adaptability is influenced by various psychological factors, which have not been sufficiently explored in the context of digital transformation. This study aims to investigate the role of emotional intelligence, cognitive flexibility, self-efficacy, and stress management in shaping employee adaptability during digital transformation processes. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews with 300 employees and 30 managers from various industries undergoing digital transformation. Descriptive and inferential statistical analysis revealed that emotional intelligence, cognitive flexibility, and self-efficacy significantly correlate with employee adaptability, while stress management showed a weaker relationship. Qualitative interviews supported these findings, highlighting the importance of emotional intelligence in reducing resistance to change and fostering collaboration. The study concludes that organizations should prioritize developing these psychological traits to enhance employee adaptability and improve the success of digital transformation initiatives. The findings contribute to a deeper understanding of the psychological mechanisms involved in employee adaptation, offering practical implications for organizations aiming to optimize workforce performance during technological transitions.
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 Anna Schneider Dito Anurogo Dodi Setiawan Jack Davis Muhammad Firdaus Abduh Oliver Harris