Nanotechnology for Healthcare: Plant-Derived Nanoparticles in Disease Treatment and Regenerative Medicine.

Nanotechnology has revolutionised biomedical research, offering innovative healthcare solutions. Plant-based nanotechnology is emerging as a sustainable alternative, minimising environmental impacts and enhancing therapeutic effectiveness. This paper explores the potential of plant-derived nanoparticles (PNPs) in medicine, highlighting their biocompatibility, multifunctionality, and eco-friendliness.

Trends and projections of type 2 diabetes mellitus in South Asia: a three-decade analysis and forecast through 2031 using global burden of disease study (1990 - 2021).

Background: The rapid rise of non-communicable diseases, particularly type 2 diabetes mellitus (T2DM), poses a significant global public health challenge, with South Asia experiencing an increasingly severe burden. This study aimed to analyse historical trends of T2DM across South Asia from 1990 to 2021 and forecast incidence through 2031.

Research Design And Methods: We carried out analysis based on the data from the 2021 Global burden of disease study.

Association between vascular endothelial growth factor and atrial fibrillation: a systematic review.

Background: Atrial fibrillation (AF) is the most prevalent form of sustained cardiac arrhythmia, with vascular endothelial growth factor (VEGF) increasingly recognized for its potential role in the pathogenesis of AF through mechanisms involving atrial remodeling, inflammation, and fibrosis. This systematic review aims to synthesize available evidence on the association between VEGF and AF, exploring the implications of VEGF as a biomarker and therapeutic target.

Methods: We conducted a comprehensive search across PubMed, Embase, and Web of Science until November 10 2024, selecting studies based on pre-defined criteria that involve adults with AF and measurements of VEGF levels.

Wealth-based inequalities in tuberculosis prevalence among households having children and young adults in India: insights from Indian demographic and health surveys (2015-2021).

Background: Tuberculosis (TB) remains a significant health concern in India, especially among households with children and young adolescents aged 6-17 years. Despite ongoing research, there is a knowledge gap regarding specific risk factors for TB within this demographic. This study aims to bridge this gap by examining the association between TB and various socio-demographic factors, including socioeconomic status, nutritional status, and environmental conditions.

Measurement of natural radionuclides and health risk assessment in soil samples of the Main Central Thrust region in Garhwal Himalaya, India.

Ionizing radiation emitted from radionuclides is present everywhere in the environment. It is the main source of health hazards to the general public. The present study elaborates on the analysis of primordial radionuclides in the collected soil samples from the Main Central Thrust (MCT) region of Uttarakhand Himalaya in a grid pattern.

Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation.

Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions.

Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization.

Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers and their modified versions are commonly used to maintain temperature stability by reacting quickly to deviations. In this study, the real PID plus second-order derivative (RPIDD) controller is introduced for the first time for industrial temperature control applications, which is a novel alternative that has not yet been investigated in the literature.

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions.

Identification of blast resistant finger millet (Eleusine coracana L.) genotype through phenotypic screening and molecular profiling.

Finger millet blast caused by Pyricularia grisea hinders crop's growth and is a serious threat to economic yield. It can lead to massive yield losses i.e.

A novel adaptive FOCV algorithm with robust IMRAC control for sustainable and high-efficiency MPPT in standalone PV systems: experimental validation and performance assessment.

This paper introduces an innovative, adaptive Fractional Open-Circuit Voltage (FOCV) algorithm combined with a robust Improved Model Reference Adaptive Controller (IMRAC) for Maximum Power Point Tracking (MPPT) in standalone photovoltaic (PV) systems. The proposed two-stage control strategy enhances energy efficiency, simplifies system operation, and addresses limitations in conventional MPPT methods, such as slow convergence, high oscillations, and susceptibility to environmental fluctuations. The first stage dynamically estimates the Maximum Power Point (MPP) voltage using a novel adaptive FOCV method, which eliminates the need for irradiance sensors or physical disconnection of PV modules.

Optimal design of a novel modified electric eel foraging optimization (MEEFO) based super twisting sliding mode controller for controlling the speed of a switched reluctance motor.

Switched Reluctance Motor (SRM) has a very high potential for adjustable speed drive operation due to their cost-effectiveness, high efficiency, robustness, simplicity, etc. Now a days SRMs are widely used in automotive industries as traction motors in electric vehicles and hybrid electric vehicles, air-conditioning compressors, and for other auxiliary services. In this article, a novel super twisting sliding mode controller (STSMC) is proposed to improve the performance of an SRM for reducing the ripple in speed and torque.

Coordinated charging of EV fleets in community parking lots to maximize benefits using a three-stage energy management system.

The rapid global adoption of electric vehicles (EVs) necessitates the development of advanced EV charging infrastructure to meet rising energy demands. In particular, community parking lots (CPLs) offer significant opportunities for coordinating EVs' charging. By integrating energy storage systems (ESSs), renewable energy sources (RESs), and building prosumers, substantial reductions in peak load and electricity costs can be achieved, while simultaneously promoting environmental sustainability.

Development of a high-performance pseudocapacitive composite via electroless deposition of silver nanoparticles on micro-sized silicon.

An energy material has been developed using a one-step chemical reduction method, incorporating silver nanoparticles (AgNPs) that encapsulate micro-sized silicon (mSi) flakes. SEM investigation revealed complete encapsulation of silicon flakes by AgNP's dendritic structure, EDX confirmed the deposition of Ag on Si flakes. Raman spectroscopy confirmed the formation of silver and silicon oxides.

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.

A new type of two-dimensional carbon-based monolayers namely irida-graphene as an anode material for magnesium-ion batteries.

The DFT was employed to assess the ion-storage capability of an irida-graphene monolayer (IGM) in Mg-ion batteries (MIBs). The IGM had a mechanically stable structure. The IGM also exhibited great conductance based on the DOS calculations.

A dual-adaptive stochastic reinforcement chimp optimization algorithm for fire detection and multidimensional problem solving.

Chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant.

New class of thio/semicarbazide-based benzyloxy derivatives as selective class of monoamine oxidase-B inhibitors.

Sixteen thio/semicarbazide-based benzyloxy derivatives (BT1-BT16) were synthesized and evaluated for their inhibitory activities against monoamine oxidases (MAOs). Most compounds showed better inhibitory activity against MAO-B than against MAO-A. BT1, BT3, and BT5 showed the greatest inhibitory activity with an identical IC value of 0.

Comparative photocatalytic performance of iron nanoparticles biosynthesized from aqueous extracts of potato, potato peels, and leaves.

This study presents an eco-friendly, cost-effective approach for synthesizing highly efficient nanocatalysts with the help of organic waste. Iron nanoparticles (INPs) were synthesized from aqueous extracts of potato, potato peel, and potato leaf and were evaluated for their photocatalytic efficiency for the degradation of methylene blue dye. X-ray Diffraction (XRD) confirmed FeO nanoparticles cubic crystal structure with the smallest crystallite size (9.

Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance.

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR).

Robustic and hybrid cobalt doped BaFeO hexaferrites for the photocatalytic degradation of Congo Red for wastewater treatment.

The industrial sector faces a significant challenge in finding the highly effective and efficient treatments for harmful dye-based color effluents. In this study, pure and cobalt doped barium hexaferrite of chemical formula, BaCoFeO (x = 0-0.06) are made via sol-gel auto-combustion (SC) methodology.

Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique.

In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG's and improve the robustness of the system under varying load and generation conditions.

Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks.

The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control.