In today's highly competitive business environment, businesses and industries are increasingly focused on mitigating carbon emissions and waste, alongside economic goals. This heightened emphasis underscores the importance of green development, aiming to achieve social, economic, and environmental sustainability. Within this context, a novel multi-echelon green supply chain inventory model for deteriorating items is developed by incorporating green technology investment under a carbon tax policy. The proposed framework consists of multiple suppliers, a single producer, and multiple buyers, and operates under selling price- and green-sensitive demand. Preservation technologies are adopted to control the deterioration rate, and the effects of inflation are explicitly considered. To address uncertainty inherent in real-world supply chains, selected parameters are represented using triangular fuzzy numbers. The objective of the model is to maximize total profit while simultaneously reducing carbon emissions. Numerical experiments are conducted in both crisp and fuzzy environments, and the concavity of the total profit function is analytically established. The numerical results indicate that, in the crisp environment, the optimal green investment level is 0.7563 per unit per month, yielding a total profit of48,414.6. In contrast, under the fuzzy environment, the optimal green investment increases to 0.8941 per unit per month, resulting in a higher total profit of76,919.7, which corresponds to an improvement of approximately 58.9
This study explores the integration of renewable energy into sustainable production-inventory models under fluctuating demand patterns and uncertain environmental conditions. A fuzzy logic-based approach models monsoon demand uncertainty, incorporating learning effects and carbon cap-and-trade policies. Ten numerical examples illustrate the model's applicability, with sensitivity analysis evaluating renewable energy adoption's impact on cost optimization and carbon footprint reduction. Numerical results show that learning in a fuzzy environment yields better results with carbon cap-and-trade policies, and waste management investments positively impact sustainability. The research provides valuable insights for policymakers and practitioners, highlighting the benefits of renewable energy integration and sustainable practices in inventory management.
This article deals with the Bayesian and classical estimation approaches for the reliability of stress-strength of a multicomponent system assuming both the stress and strength variables follow exponentiated Pareto distribution independently based on the progressively censored data. In the classical estimation, the maximum likelihood estimate, asymptotic confidence and two bootstrap confidence intervals boot-t $\amp $\amp boot-p are constructed for multicomponent stress-strength (MSS) reliability. In the Bayesian estimation, the Bayes estimates under the squared error loss function using Markov chain Monte Carlo (MCMC) techniques are obtained. The highest posterior density credible interval based on the MCMC method of the MSS reliability are constructed. The different estimates obtained are compared using a Monte Carlo simulation study which is carried out for various sample sizes and different censoring schemes. Two different real data sets are studied to illustrate the real-life applications of the study. Finally, the conclusions based on the study with the future scope of the work are provided.
Coumarins constitute an important class of heterocycles with significant utility in medicinal chemistry, attributed to their structural diversity and broad spectrum of biological activities. Traditional synthetic routes to coumarin derivatives often involve harsh conditions and limited functional group tolerance, prompting the development of more efficient methodologies. Recent advances in synthetic strategies, including transition metal catalyzed C-H activation, carbonylation, cross-coupling reactions, visible-light photoredox catalysis, and metal-free oxidative cyclizations, have greatly expanded access to structurally diverse coumarins under milder and more sustainable conditions. Concurrently, coumarin derivatives continue to attract attention for their diverse biological properties, including anticancer, neuroprotective, antibacterial, antiviral, anti-inflammatory, antidiabetic and other activities observed in preclinical studies. This review provides a comprehensive analysis of contemporary synthetic methodologies for coumarin scaffolds and critically examines their medicinal relevance, highlighting preclinical evaluations and proposed mechanisms of action at the molecular and cellular levels.
With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework shows promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous federated learning approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.
