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All Journal International Journal of Engineering, Science and Information Technology
Vij, Priya
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Sustainable Supply Chain Practices in Engineering-Based Manufacturing Firms Nuritdinovich, Muhidinov Ayubbek; Vij, Priya
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1494

Abstract

Sustainable Supply Chain Management (SSCM) assesses the environmental implications associated with all conventional supply chain (SC)activities to mitigate their adverse effects. This study presents a fuzzy-based methodology for examining obstacles in SSCM within the environment. Seven manufacturing companies from the electronics industry are participating. The study's findings reveal three primary challenges in engineering-based manufacturing firms. The barriers include knowledge-related factors (insufficient understanding of the adverse effects on business, absence of training programs for industry-specific training, monitoring, and mentoring, lack of technical expertise, and challenges in recognizing environmental possibilities), commitment-related issues (deficiency in corporate social accountability), and design-related challenges (complexities in designing for the reusing/recycling of used goods).The suggested research is among the first investigations undertaken within the environment regarding identifying SSCM barriers in the electrical and electronics industry. Secondly, the obstacles are examined via causation and prominent relationships, which assist decision-makers, policy developers, and organizational managers tackle the essential factors necessary to achieve SSCM activities.
Machine Learning-guided Synthesis of Quantum Entangled Materials Vij, Priya; Nandy, Manish; Pandey, Mamta
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1496

Abstract

The synthesis of materials into quantum entangled materials is a complicated challenge to an accurate and computational prediction of those materials. In this proposed work, it develops an AI-guided framework based on the combination between machine learning (ML) and reinforcement learning (RL), and quantum simulations to push the designing and validating of quantum materials at a much faster pace. In the first, graph neural networks (GNNs) are used to extract the atomic level quantum features, and in the second, generative models (VAE/GAN) are utilized to discover some novel entangled structures. In addition, fabrication with the synthesis parameters as parameters in the reinforcement learning results in an improvement of the experiment synthesis and a decrease of experiment failures as well as significant improvement of reproducibility. It demonstrates that the proposed hybrid ML-quantum simulation is validated on entanglement fidelity in real-world quantum computing platforms using IBM Qiskit and Google Cirq. As the proposed method is way beyond traditional ones, it has higher quantum coherence time, synthesis efficiency as well as higher prediction accuracy. In addition to enabling scaling-up of cryptography, quantum computing, and next generation nanomaterials, it is a cost and scalable framework for creating next generation quantum technologies applications as it is. And the model is further researched for the generalization in regards to real-time experimental feedback and for the expansion of the framework to a more general quantum materials program. The results show that AI approaches can truly accelerate the quantum material innovation even when syntheses are not at all possible.
Co-Authors Nandy, Manish Nuritdinovich, Muhidinov Ayubbek Pandey, Mamta