Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

The aroused quest to reduce the delay at interconnect level is the main urge of this paper to come across a configuration of Carbon Nanotube (CNT) bundle namely squarely packed bundle of composite CNTs. The approach, demonstrated in this paper, adapts the composite bundle to adopt for high speed Very Large Scale Integration (VLSI) interconnect with technology sizing down. To reduce the delay of the proposed configuration of composite CNT bundle, the behavioral change of Resistance (R), Inductance (L) and Capacitance (C) has been observed with respect to both width of the bundle and diameter of the CNTs in the bundle. Consequently, the performance of the modified bundle configuration is compared with previously developed configuration namely squarely packed bundle of dimorphic MWCNTs in terms of propagation delay and crosstalk delay at local, semiglobal and global level interconnect. The proposed bundle configuration is ultimately enacted as better one for 32nmand 16nmtechnology node and suitable for 7nmas well.

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors which operate above 10 MHz and low-frequency sensors. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. Thus, identifying emerging trends and gaps in knowledge, it encourages further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

Due to increased energy intensive human activities resulting accelerated demand for electric power coupled with occurrence of natural disasters with increased frequency, intensity, and duration, it becomes essential to explore and advance renewable energy technology for sustainability of the society. Addressing the stated problem and providing a radical solution has been attempted in this study. To harvest the renewable energy, among variety of solar cells reported, a composite a-Si/CZTS photovoltaic devices has not yet been investigated. The calculated parameters for solar cell based on the new array of layers consisting of a-Si/CZTS are reported in this study. The variation of i) solar cell efficiency as a function of CZTS layer thickness, temperature, acceptor, and donor defect concentration; ii) variation of the open circuit current density as a function of temperature, open circuit voltage; iii) variation of open circuit voltage as a function of the thickness of the CZTS layer has been determined. There has been no reported study on a-Si/CZTS configuration-based solar cell, analysis of the parameters, and study to address the challenges imped efficiency of the photovoltaic device and the same has been discussed in this work. The value of the SnO2/a-Si/CZTS solar cells obtained from the simulation is 23.9 %.

Our environment has been impacted by man-made pollutants mainly industries make substantial use of synthetic dyes which exhibit cytotoxicity and have significant environmental consequences. Effective photocatalyst-based approaches for degrading synthetic dyes into less toxic chemical are of great interest. Synthesizing NPs using biological approaches, particularly plant-based approaches offer advantages, decreasing the risk of NPs losing biocompatibility during synthesis, cost-effectiveness, and eco-friendliness. In this study, we employed a green synthesis method to produce manganese oxide nanoparticles (MnO NPs) utilizing leaf extract from the Lathyrus Aphaca plant. The synthesized MnOx NPs were characterized through various techniques; X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR), and UV–visible spectroscopy. XRD analysis showed distinct peaks, indicated the presence crystallographic planes within the MnO₂ nanoparticles, thus confirming their crystalline structure. SEM study highlighted closely packed configurations with average size of approximately ≥ 50-70 nm, with well-defined boundaries. EDX analysis showed the presence of elemental manganese (Mn) in the samples, with peaks corresponding to Mn detected at lower energy levels, predominantly represented by MnO. FTIR, showed the presence of the O-O stretching mode at a frequency of 719 cm^-1, the presence of MnO₆ oxides of manganese, and peak at 548 cm^-1 corresponded to the Mn-O stretching mode. Furthermore, the green-synthesized manganese oxide nanoparticles exhibited promising photocatalytic and adsorption capabilities against Methylene Blue (MB) dye, leading to approximately 93.21% degradation of MB when treated with the green-synthesized MnO nanoparticles derived from plant extract. This highlights the efficacy and potential of these nanoparticles in environmental remediation applications, particularly in the degradation of methylene blue contaminants.