Recent Advances in Wearable Textile-Based Triboelectric Nanogenerators.

We review recent results on textile triboelectric nanogenerators (T-TENGs), which function both as harvesters of mechanical energy and self-powered motion sensors. T-TENGs can be flexible, breathable, and lightweight. With a combination of traditional and novel manufacturing methods, including nanofibers, T-TENGs can deliver promising power output. We review the evolution of T-TENG device structures based on various textile material configurations and fabrication methods, along with demonstrations of self-powered systems. We also provide a detailed analysis of different textile materials and approaches used to enhance output. Additionally, we discuss integration capabilities with supercapacitors and potential applications across various fields such as health monitoring, human activity monitoring, human-machine interaction applications, etc. This review concludes by addressing the challenges and key research questions that remain for developing viable T-TENG technology.

Design and Probing of Prism-Based SPR Nano-Biosensor for Human Sperm Detection.

Human sperm functioning is crucial for maintaining natural reproduction, but its sterility is enhanced by variations in environmental conditions. Because of these agitating properties, powerful computer-aided devices are required, but their precision is inadequate, particularly when it comes to samples with low sperm concentrations. Therefore, for the first time, this article introduces the sulfide material-based structure for the detection of human sperm samples using the prism-based surface plasmon resonance sensor (SPR) Nano-biosensor. The proposed structure is designed on the basis of a prism-based Kretschmann configuration and includes silver, silicon, a sulfide layer, black phosphorus, and a sensing medium. This work takes advantage of the excitement of surface plasmons and evanescent waves in the metal dielectric region. For the detection process, seven sperm samples are taken, with their concentration, mobility, and refractive index measured by the refractometer. The proposed structure provides a maximum sensitivity of 409.17°/RIU, QF of 97.45RIU-1 and a DA of 1.37. The results provide a substantial improvement in comparison to the reported work in the literature.

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection.

Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time-consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non-linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time-frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE-GTRN-LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE-based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure-affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE-GTRN-LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state-of-the-art techniques.