Addressing water scarcity and pollution is imperative in tackling global environmental challenges, prompting the exploration of innovative techniques for effective water and wastewater treatment. Nanotechnology presents promising solutions through the customization of nanoparticles and nanocomposites specifically designed for water purification applications. This review delves into recent advancements in nanoparticle-based technologies for water treatment, with a particular focus on their synthesis methodologies, intrinsic properties, and versatile applications. A wide range of nanoparticles, ranging from metal nanoparticles to carbon-based nanomaterials, along with hybrid nanocomposites such as metal/metal oxide-based nanocomposites, polymer-based nanocomposites, and others, were emphasized for eliminating contaminants from water and wastewater matrices. Furthermore, this review elucidates the underlying mechanisms governing pollutant removal processes, encompassing adsorption, catalysis, and membrane filtration, facilitated by nanoparticles. Additionally, it explores the environmental implications and challenges associated with the widespread deployment of nanoparticle-based water-treatment technologies. By amalgamating existing research findings, this review provides valuable insights into the potential of nanoparticles and nanocomposites in mitigating water-related challenges and presents recommendations for future research trajectories and technological advancements in this domain.

Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be potentially the utmost protuberant research topic. To solve this issue, a hybrid signal denoising framework which comprises of modified empirical mode decomposition (EMD) and an optimized Laplacian of Gaussian (LoG) filter is proposed for suppression of motion artifacts from EEG signals. The modified-EMD decomposes the single-channel noisy EEG signal into a set of the optimal number of intrinsic mode functions (IMFs). Further, the optimized LoG filter has been applied to the motion artifact intermixed EEG signal which is considered as low-frequency noise likely to present at low-frequency IMFs. This filter performs smoothing of the signal and removes background noises or artifacts from the EEG signal. The denoised signal is reconstructed by adding filtered output signal and high-frequency IMFs. The robustness of the proposed method is demonstrated through simulated and real EEG data with artifacts. The experimental values of the different sets of performance assessment metrics reveal that the proposed algorithm outperforms the existing state-of-the-art EEG signal denoising algorithms for suppression of motion artifacts from EEG signals.

This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). Unlike conventional approaches that rely on static or standalone algorithms, the proposed framework employs a structured decision-making pipeline that dynamically adapts to real-time changes in network topology, traffic, and energy conditions. Each AI module plays a distinct role-RL handles local routing decisions, while GA and PSO are invoked for global optimization under resource constraints. Simulations conducted in MATLAB R2021b validate the framework’s effectiveness, demonstrating improvements in packet delivery ratio, end-to-end latency, and energy efficiency when compared to traditional protocols. While this study is based on synthetic evaluations, it outlines the architectural groundwork for future real-world implementation and discusses deployment challenges such as scalability, resource usage, and security. The results highlight the potential of hybrid AI-based routing strategies to enhance the reliability, adaptability, and sustainability of WSNs in dynamic and resource-limited environments.