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Chronic wounds that stall at the inflammatory phase of healing may create several life-threatening complications such as tissue damage, septicemia, and organ failures. In order to prevent these adverse clinical outcomes and accelerate the wound healing process, it is crucial to monitor the wound status in real-time so that immediate therapeutic interventions can be implemented. In addition, continuous monitoring of the wound status can prevent drug overdose at the wound site, leading to on-demand and personalized drug delivery. Inflammatory mediators, such as Interleukin-6 (IL-6) and Interleukin-10 (IL-10) are promising indicators for the progression of wound healing and predictors of disease severity. Toward this end, this work reports a flexible wound patch for multiplexed monitoring of IL-6 and IL-10 at the wound site in order to provide real-time feedback on the inflammation phase of the wound. An optimized composition of gold nanoparticles integrated multiwalled carbon nanotube was demonstrated to improve sensor performance substantially. The sensor also exhibited excellent repeatable, reversible, and drift characteristics. A miniaturized Internet-of-things (IoT)-enabled potentiostat was also developed and integrated with the flexible sensor to realize a wearable system. This IoT-enabled wearable device provides a smart and cost-effective solution to improving the existing wound care through continuous, real-time, and in-situ monitoring of multiple wound biomarkers.

The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir.