http://hdl.handle.net/123456789/6 Thu, 16 Jul 2026 07:49:03 GMT 2026-07-16T07:49:03Z http://hdl.handle.net/123456789/764 Title: AI In Leukemia Diagnostics: Complementing the Pathologist's Role Authors: Das, Sayandeep K. ; Das, Kusal K.  Abstract: Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context specific integration, and clinical decision-making. We present an illustrative “human-in-the-loop” workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator–integrator role across morphology, flow cytometry, and genomic/ multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on in ternational competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pair ing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/764 2026-06-01T00:00:00Z http://hdl.handle.net/123456789/763 Title: A Review on Influence of Process Parameters, Preheating and Cooling on Friction Stir Welded Joint Performance Authors: Dashwant, Prashant ; Kulkarni, Sameer  Abstract: The investigation of dissimilar materials joining using Friction Stir Welding process has become a prominent research trend due to its growing use in aerospace, automo tive, and shipbuilding industries. Over the past two decades, it has emerged as one of the most significant advancements in metal joining technology. This review brings together the key insights of research developments in the field, with an emphasis on the fundamental concepts behind Friction Stir Welding, tool design and materials, and the essential process parameters influencing joint quality. Also, the paper focuses on friction stir welding of dissimilar materials, examin ing the impact of preheating and water cooling on various characteristics like hardness, tensile strength, and fatigue behavior. This literature review critically examines recent advancements in dissimilar welding of aluminum–mag nesium alloys, offering essential design recommendations for future studies. It presents a comprehensive overview of existing knowledge and points out important areas that need more investigation. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/123456789/763 2026-06-01T00:00:00Z http://hdl.handle.net/123456789/531 Title: A Multi-Scale Inverted Spatial-Temporal Network for EEG-Based Emotion Recognition Authors: Kokitkar, Vinod R.; Ghuli, Anand Abstract: Understanding human emotional states through Electroencephalography (EEG) signals has gained significant attention due to its applications in healthcare, human-computer interaction, and affective computing. However, existing approaches often struggle to model temporal dynamics and spatial dependencies effectively, which limits recognition accuracy. The primary research gap lies in the inability of conventional and recent models to simultaneously capture multi-scale temporal patterns while preserving channel-specific information over time. To address this limitation, this study proposes a MultiScale Inverted Spatial-Temporal Network (MIST-E) for EEG-based emotion recognition. MIST-E constructs multi-scale representations and employs an inverted embedding strategy to maintain temporal continuity and spatial channel relationships. In addition, a newly designed CNN is used to extract discriminative features for reliable classification. Experimental results on the DEAP dataset demonstrate that MIST-E effectively captures complex spatial-temporal dependencies, achieving 90.56±1.02% accuracy for valence and 91.12±0.98% for arousal. These findings indicate that MIST-E provides improved accuracy compared to existing methods. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/123456789/531 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/509 Title: Review on 3-D printed ABS polymer for rotary control elements Authors: Vhatkar, Mahendra Nana; Patil, Geetanjali Vijay; Bhavi, Iresh; Ali, Syed Abbas Abstract: The purpose of this sudy is most popular topic in additive manufacturing (AM) for defining the various three-dimensional (3D) materials according to their desires is Acrylonitrile Butadiene Styrene (ABS). Depending on the particular 3D materials, the ABS concentration is used differently. To increase the ABS polymer’s strength, various reinforcement materials are available. The ABS polymer and the three reinforced materials have been covered in this article. Kevlar, carbon fibre (CF) and fibre glass (FG) are the three reinforced materials. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/123456789/509 2025-01-01T00:00:00Z