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86 articles for “Optimisation”
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 Read article
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A Qualitative Comparative Study of Storytelling Through the Thematic Apperception Test Among Two Young Adults with a History of Substance Abuse in a Selected De-Addiction Centre in Delhi, India
Abstract: Introduction: Substance use can deeply affect an individual’s personality, relationships, and psychological functioning. The thematic apperception test (TAT), a projective psychological tool, helps uncover unconscious thoughts and emotions through storytelling. Objective: (i) To explore and compare the underlying personality dynamics of two young adult males with a history of substance use through TAT card analysis. Method: A qualitative comparative case study design was used. Ten standard TAT cards were presented …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 1–4 Read article
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Artificial intelligence-integrated nanobiotechnology for precision medicine, smart diagnostics, and sustainable environmental applications
Abstract: Background: Nanobiotechnology integrates nanoscale materials with biological systems, enabling breakthroughs in drug delivery, biosensing, and environmental monitoring. However, the complexity of biological interactions and the vast parameter space of nano‑bio interfaces limit conventional design. Artificial intelligence (AI) offers powerful tools for modelling, predicting, and optimising these systems. Objective: This review provides a systematic, STM‑compliant overview of AI‑integrated nanobiotechnology across three domains: precision medicine (AI‑optimised nanocarriers, personalised therapeutics), smart diagnostics (AI‑powered …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 28, Issue 2, 2025 Read article
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MAP: The Invisible Shield Protecting Our Product's Journey
Abstract: Modified Atmosphere Packaging (MAP) has established itself as one of the most reliable and widely adopted preservation technologies in the modern food industry, offering a practical means of extending shelf life while maintaining the sensory and nutritional integrity of food products throughout storage and distribution. The fundamental working principle revolves around deliberately altering the gaseous composition within sealed packages, primarily through the strategic use of carbon dioxide, nitrogen and oxygen …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 15, Issue 2, 2026 · pp. 52–58 Read article
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Task Scheduling in Cloud Computing using Hippopotamus Optimization Algorithm
Abstract: Cloud computing, which provides remote clients with on-demand services, has emerged as a crucial component of contemporary technology. It is still difficult to schedule tasks effectively in such diverse and dynamic situations. Motivated by the hippopotamus's balanced exploration and exploitation behavior, this research suggests a unique work scheduling method utilizing the hippopotamus optimization algorithm (HOA). In order to maximize resource usage and throughput while minimizing makespan and execution cost, the …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 22–29 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article