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This review is a critical analysis of the current state‐of‐the‐art in core spun yarn textile triboelectric nanogenerators (CSY‐T‐TENGs) for self‐powered smart sensing applications. The rapid expansion of wireless communication, flexible conductive materials, and wearable electronics over the last ten years is now demanding autonomous energy, which has created a new research space in the field of wearable T‐TENGs. Current research is exploring T‐TENGs made from CSYs as stable and reliable energy harvesters and sensing devices for modern wearable IoT platforms. CSY‐TENGs are emerging as an important technology due to its simple structure, low cost, and excellent performance in converting mechanical energy into electrical energy and due to its sensing ability. This paper provides a critical review on current progress, it analyzes the unique advantages of CSYs T‐TENGs over conventional T‐TENGs, it describes fabrication techniques and discusses the materials used along with their properties and electrical performance characteristics, and it highlights the recent advancements in their integration with self‐excitation circuits, charge storage devices and IoT‐enabled smart sensing applications, such as environmental and health monitoring. In the conclusion, it discusses the challenges and future directions of CSYs T‐TENGs and it provides a future road map for optimization, upscaling, and commercialization of the technology. This paper provides a critical review on current progress, it analyzes the unique advantages of CSYs T‐TENGs over conventional T‐TENGs, it describes fabrication techniques and discusses the materials used along with their properties and electrical performance characteristics, and it highlights the recent advancements in the integration of CSYs T‐TENGs with self‐excitation circuits, charge storage devices and IoT‐enabled smart sensing applications.

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH 4 + ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH 4 + ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH 4 + concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH 4 + ion levels. The proposed NH 4 + sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.

Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness mechanical energy while enabling self‐sustaining sensing for self‐powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low‐power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI‐enhanced TENG solutions. Triboelectric nanogenerators (TENGs) enable sustainable energy harvesting and self‐powered sensing but face challenges in material optimization, fabrication, and stability. Integrating artificial intelligence (AI) enhances TENG performance through machine learning, improving energy output, adaptability, and predictive maintenance. This review explores AI‐driven TENG advancements, key challenges, and future research directions for practical applications.

The ongoing rise in the number of cancer cases raises concerns regarding the efficacy of the various treatment methods that are currently available. Consequently, patients are looking for alternatives to traditional cancer treatments such as surgery, chemotherapy, and radiotherapy as a replacement. Medicinal plants are universally acknowledged as the cornerstone of preventative medicine and therapeutic practices. Annona muricata is a member of the family Annonaceae and is familiar for its medicinal properties. A. muricata has been identified to have promising compounds that could potentially be utilized for the treatment of cancer. The most prevalent phytochemical components identified and isolated from this plant are alkaloids, phenols, and acetogenins. This review focuses on the role of A. muricata extract against various types of cancer, modulation of cellular proliferation and necrosis, and bioactive metabolites responsible for various pharmacological activities along with their ethnomedicinal uses. Additionally, this review highlights the molecular mechanism of the role of A. muricata extract in downregulating anti-apoptotic and several genes involved in the pro-cancer metabolic pathways and decreasing the expression of proteins involved in cell invasion and metastasis while upregulating proapoptotic genes and genes involved in the destruction of cancer cells. Therefore, the active phytochemicals identified in A. muricata have the potential to be employed as a promising anti-cancer agent.

This paper investigates the application of Neural Network Backstepping Control (NN-BSC) for enhancing the rotational speed control of Oscillating Water Column (OWC) wave energy systems. Traditional control methods face limitations when dealing with nonlinearities, irregular wave conditions, and actuator disturbances. To address these challenges, this research paper introduces a Chebyshev NN within the BSC framework, leveraging its high approximation accuracy and computational efficiency. The design of the NN-BSC involves estimating the disturbance term using the Chebyshev NN and validating the stability OWC control system through Lyapunov analysis. The proposed NN-BSC law effectively handles nonlinearities and improves system robustness under dynamic conditions. Numerical simulations have been conducted using MATLAB/SIMULINK to compare the performance of the uncontrolled OWC system, conventional PI and BSC, and NN-BSC, under scenarios with and without actuator disturbances. The parameters for PI, BSC, and NN-BSC are optimized using a Particle Swarm Optimization (PSO) algorithm, which minimizes a fitness function defined by the Integral Squared Error (ISE). Results indicate that NN-BSC achieves smoother rotor speed tracking, particularly under actuator disturbances, where the conventional PI and BSC exhibits significant performance degradation in terms of ISE. Under actuator disturbance scenarios: (1) NN-BSC achieved the lowest ISE value of 22.5433, outperforming PI (40.6381) and BSC (37.1192), and (2) NN-BSC demonstrated the lowest maximum peak overshoot (0.9651 rad/s ) and fastest settling time (0.0561 s ).

Reactive Power & Harmonic compensation (RPHC) represents a crucial area of investigation within the domains of power engineering. While passive filters have traditionally been favored for mitigating harmonics, their size becomes impractical when addressing multiple harmonics. In contrast, active power filters offer enhanced performance and address the limitations of passive filters, albeit at a higher cost. This study introduces a Shunt Hybrid Active Filter (SHAF) designed for RPHC. In our approach, the Kalman Filter (KF) is utilized for estimation of reference current per-unit (PU) quantity and the current reference crest value, which are derived from a Type-2 Fuzzy Fractional Order PID Controller (T2FFOPIDC). The optimal parameters for the T2FFOPIDC are determined using an innovative hybrid Particle Swarm Optimization (PSO) Firefly Algorithm (hPSOFA), with a focus on minimizing the ITAE. The suggested system is validated, taking balanced as well as unbalanced nonlinear loads, and the outcomes are contrasted with those obtained from an SHAF system based on the Type-1 Fuzzy Fractional Order PID Controller (T1FFOPIDC), which is also optimized using hPSOFA. The compensation strategy employed in the SHAF differs from the conventional (id–iq) or (p–q) techniques, as it necessitates the current from the source side. Various performance metrics, including Reactive Power (Q), THD, and PFS, are assessed to evaluate the effectiveness of the proposed controller. Case studies reveal that the hPSOFA-T2FFOPIDC-based SHAF demonstrates superior RPHC compared to the hPSOFA-T1FFOPIDC-based SHAF across various operational scenarios. Furthermore, the performance of the proposed hPSOFA-T2FFOPIDC-SHAF has been experimentally validated using dSPACE.

Bibliographic analysis has been a very powerful tool in evaluating the effective contributions of a researcher and determining his/her future research potential. The lack of an absolute quantification of the author's scientific contributions by the existing measurement system hampers the decision-making process. In this paper, a new metric system, Absolute index (Ab-index), has been proposed that allows a more objective comparison of the contributions of a researcher. The Ab-index takes into account the impact of research findings while keeping in mind the physical and intellectual contributions of the author(s) in accomplishing the task. The Ab-index and h-index were calculated for 10 highly cited geneticists and molecular biologist and 10 young researchers of biological sciences and compared for their relationship to the researchers input as a primary author. This is the first report of a measuring method clarifying the contributions of the first author, corresponding author, and other co-authors and the sharing of credit in a logical ratio. A java application has been developed for the easy calculation of the Ab-index. It can be used as a yardstick for comparing the credibility of different scientists competing for the same resources while the Productivity index (Pr-index), which is the rate of change in the Ab-index per year, can be used for comparing scientists of different age groups. The Ab-index has clear advantage over other popular metric systems in comparing scientific credibility of young scientists. The sum of the Ab-indices earned by individual researchers of an institute per year can be referred to as Pr-index of the institute.

We report a controlled deposition process using atmospheric plasma to fabricate silver nanoparticle (AgNP) structures on polydimethylsiloxane (PDMS) substrates, essential for stretchable electronic circuits in wearable devices. This technique ensures precise printing of conductive structures using nanoparticles as precursors, while the relationship between crystallinity and plasma treatment is established through X-ray diffraction (XRD) analysis. The XRD studies provide insights into the effects of plasma parameters on the structural integrity and adhesion of AgNP patterns, enhancing our understanding of substrate stretchability and bendability. Our findings indicate that atmospheric plasma-aided printing not only avoids the need for high-temperature sintering but also significantly enhances the electrical and mechanical properties of the conductive structures, advancing the production of robust and adaptable electronic devices for wearable technology.