Energy storage devices constitute one of the research areas in recent years. Capacitors are commonly used for the storage of electrical energy. The current research is focusing on not only the improvement in energy density but also the materials which are environment friendly. Polymer composites are known to be technically essential materials owing to their wide range of applications. Enormous research has been devoted to zinc oxide- (ZnO-) based polymer nanocomposites, due to their extraordinary dielectric properties. This review article presents a detailed study of the dielectric properties of ZnO-based nanocomposites. The dielectric constant study includes the effect of transition metals and rare earth metals as a dopant in ZnO. This review gives an insight into the mechanism responsible for the variation of dielectric constant in ZnO nanocomposites due to various factors like size of nanoparticles, thickness of the thin film, operating frequency, doping concentration, and atomic number. The observations have been summarized to convey the mechanism and structural changes involved in the ZnO nanocomposites to the researchers. The deployment of biodegradable nanocomposite materials is expected to open an innovative way for their outstanding electronic applications as storage materials.

Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed and deployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy which pulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in an optimized way which reduces the energy dissipation. Ant Colony Optimization (ACO) is being widely used in optimizing the network routing protocols. Ant Based Routing can play a significant role in the enhancement of network life time. In this paper, Intercluster Ant Colony Optimization algorithm (IC-ACO) has been proposed that relies upon ACO algorithm for routing of data packets in the network and an attempt has been made to minimize the efforts wasted in transferring the redundant data sent by the sensors which lie in the close proximity of each other in a densely deployed network. The IC-ACO algorithm was studied by simulation for various network scenarios. The results depict the lead of IC-ACO as compared to LEACH protocol by indicating higher energy efficiency, prolonged network lifetime, enhanced stability period, and the elevated amount of data packets in a densely deployed wireless sensor network.

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Purpose This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm. Design/methodology/approach In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach. Findings The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC). Originality/value The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.