The concept of Blockchain has penetrated a wide range of scientific areas, and its use is considered to rise exponentially in the near future. Executing short scripts of predefined code called smart contracts on Blockchain can eliminate the need of intermediaries and can also raise the multitude of execution of contracts. In this paper, we discuss the concept of Blockchain along with smart contracts and discuss their applicability in the Internet of Medical Things (IoMT) in the e-healthcare domain. The paper analyses the dimensions that decentralization and the use of smart contracts will take the IoMT in e-healthcare, proposes a novel architecture, and also outlines the advantages, challenges, and future trends related to the integration of all three. The proposed architecture shows its effectiveness with average packet delivery ratio, average latency, and average energy efficiency performance parameters when compared with traditional approaches.

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications.

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.