A supercapacitor is a type of electrical component that has larger capacitance, due to asymmetric behavior with better power density, and lower ESR (effective series resistance) than conventional energy-storage components. Supercapacitors can be used with battery technology to create an effective energy storage system due to their qualities and precise characterization. Studies have shown that the use of quantum dots as electrodes in supercapacitors can significantly increase their effectiveness. In this research article, we have used a Drude model based on free electrons (asymmetric nature) to describe the supercapacitor’s discharging characteristics. Commercially available Nippon DLA and Green-cap supercapacitors were used to verify the Drude model by discharging them through a constant current source using a simple current mirror circuit. The parameters of both the fractional-order models and our suggested method were estimated using the least-squares regression fitting approach. An intriguing finding from the Drude model is the current-dependent behavior of the leakage-parallel resistance in the constant current discharge process. Instead of using the traditional exponential rule, supercapacitors discharge according to a power law. This work reflects the strong symmetry of different aspects of designing a hybrid supercapacitor with high efficiency and reliability.

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network’s lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing.

The authors have crafted a comprehensive three step blueprint for planned marketing preparation that harnesses the manifold advantages of artificial intelligenc The authors have crafted a comprehensive three step blueprint for planned marketing preparation that harnesses the manifold advantages of artificial intelligenc