Poornima University, established in 2012, is a private university in Jaipur, Rajasthan. The university was established by Rajasthan State Legislature vide Act No.
PurposeThe research aim was to work on the discrepancies and ambiguities that have emerged with the application of individual multi-criteria decision-making (MCDM) methods in the determination of key factors in sustainable supply chain management. It aims at improving reliability of the decision-making process by suggesting an aggregation procedure to combine the various decision outcomes.Design/methodology/approachSeveral decision methods, including analytic hierarchy process (AHP), best-worst method (BWM) and fuzzy AHP, were employed to assess and rank the factors for achieving sustainability in steel supply chain. These methods produce different ranking since they make different assumptions, have different preference structures and different data processing mechanisms. To counter this, we made use of the half quadratic (HQ) theory to enable us to compile these rankings. HQ-based method combines varying expert estimation and methodological opinion into single final ranking.FindingsThe HQ-based aggregation can improve consistency and sturdiness of the final ranking; it eliminates the difference in approach. This method facilitates stable and consistent decision-making and helps in consistent strategic planning and policy making of sustainable supply chain management.Originality/valueThe novelty of this study lies in the use of HQ theory in aggregating the ranking based on two or more decision-making methods. It shows how this method is effective in eliminating the uncertainty that comes with single method methods as well as reveals how aggregating is important in enhancing the validity of MCDM in the use of sustainability in analysing a supply chain.
Edge-centric digital process twins struggle with slow adaptation and real-time compliance. We propose a self-evolving Edge-AI architecture with five components. The Hierarchical Neuro-Symbolic Verification Graph integrates symbolic rules and neural graphs, reducing latency by 35% while maintaining over 97% compliance. Federated Evolutionary Drift Adaptation improves drift response by 28% and F1 score by 15% using evolutionary operators. The Multi-Resolution Spatio-Temporal Causal Inference Network enables 40% earlier fault detection via causal separation. The Quantum-Inspired Edge Reinforcement Optimiser speeds convergence by 30% and boosts effectiveness by 12%. The Cross-Layer Digital Twin Consistency Ledger ensures tamper-proof state integrity with <0.5% violations at 10k+ transactions/s, enabling secure, adaptive automation.
Blends of non-edible vegetable oils with diesel fuel are considered a promising alternative for compression ignition (CI) engines in response to fossil fuel depletion and environmental concerns. Challenges to their commercial utilization include questions regarding the long-term durability of these novel fuels. This study focuses on endurance, wear, and the breakdown of lubricating oil in a CI engine when operated on a pre-heated blend of neem oil and diesel. For this purpose, a 272-h endurance test was conducted as per IS 10000 standard, using the optimized pre-heated B30 neem oil blend. We evaluated engine performance, wear of the primary components, condition of the lubricant, and the formation of deposits. In terms of B30 performance, optimum conditions (compression ratio = 22, injection timing = 23 degrees CA BTDC, and injection pressure = 210 bar) resulted in a brake thermal efficiency of 34.99% and a brake specific fuel consumption of 0.29 kg/kWh which was better than traditional diesel. No mechanical failures or ring sticking were found, although some carbon deposits were present. A distinctive feature of this work is the unique long-term analysis regarding the performance improvement, wear and detailed analysis, lubricant and visual deposits diagnostics on a preheated straight blend of neem oil. The findings show that the combustion quality and durability are significantly improved by preheating the fuel; thus, neem oil blends show sustainable and solid prospects for use in CI engines for agricultural and decentralized energy systems.
The present study deals with the concentration of blue aerosol in the lower atmosphere and its modeling for the assessment of the climate of the Barrackpore municipal area (BMA). Since the concentration of blue aerosol is not spatially correlated, its modeling seems to have a meager relation with the municipal boundary. The present study utilizes SIR-C images to find out the concentration of blue aerosols since these images contain microwave sensing to estimate the depth of the aerosol layer. Here, the aerosol optical depth (AOD) in the blue band ranges between 0.01 (extremely clean) and 1.0 (extremely polluted). The results show that the concentration of blue aerosol is found to be higher near the major river Hooghly, whereas itsr concentration is found to be lower in the rural counterpart. The concentration of blue aerosol is found to be higher in the evening, increasing the harshness of the city climate in the nighttime. The normal QQ plot shows the linear positive relation between the theoretical and actual observations, indicating the significance of the observation at the 0.01 level. The study concludes with the finding of the discontinuous and heterogeneous nature of blue aerosols through the computation of neighborhood functions, where the root mean square error (RMSE) value is found to concentrate near 0.2 (less error).
The adaptive systems of managing the microgrid are required for the integration of sources of renewable energy and to counteract the demand in the resilience of energy. In the meantime, the recent techniques has drawbacks of utilizing the resources inefficiently, distribution of power in imbalance manner, and the adaptability is limited for the conditions of demand and supply dynamically. For addressing such issues this research is carried out for trading of energy in the smart microgrid through an Equilibrium System of Hopfield with Decision Nash (DNEHNS). The suggested approach combines the mechanism of fairness equilibrium Nash with the neural network of Hopfield for providing the decisions of trading in balanced and in efficient manner. The key parameters considered in the training includes price in market, demand of load, losses in energy and the consistency in the supply for prioritizing the allocation of energy. The demonstration of the analytical results provides that the performance of the DNEHNS has been enhanced in a significant manner in correlation with the existing techniques. The suggested model has a determination coefficient of 99.85
