Antidiabetic phytochemicals: an overview of medicinal plants and their bioactive compounds in diabetes mellitus treatment.

Diabetes mellitus (DM) is a group of metabolic disorders characterized by hyperglycemia due to insufficient insulin secretion or action. Contributing factors include genetic predisposition, obesity, family history, inactivity, and environmental risks. Type 2 diabetes mellitus (T2DM), the most common form, involves impaired insulin secretion by pancreatic β-cells, leading to insulin resistance.

Exploring the link between sex hormone-binding globulin levels and prostate cancer risk: a comprehensive systematic review and meta-analysis.

Background: Sex hormone-binding globulin (SHBG) plays a critical role in regulating androgen bioavailability and has been hypothesized to influence prostate cancer risk, though existing evidence is inconsistent. This systematic review and meta-analysis aimed to evaluate the association between SHBG levels and prostate cancer risk.

Methods: A comprehensive search was conducted across PubMed, Embase, and Web of Science for studies published up to December 1, 2024.

Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia.

This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.

An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods.

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision.

Nutritional Profiling of Wild Edible Plants: Quantification of Macronutrients and Minerals via Energy Dispersive X-ray (EDX) Fluorescence Spectroscopy.

The wild edible plants offer a valuable resource for food and human nutrition. The current study aimed to analyze the proximate composition and ethno-medicinal profiles of twenty selected wild edible plants from the Malakand District, Pakistan. The proximate composition (moisture, ash, crude protein, crude fat, and dietary fiber) was analyzed following Association of Official Agricultural Chemists (AOAC) protocols.

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease detected among people. This cancer is the uncontrolled evolution of atypical skin cells.

Speciation and historical invasions of the Asian black-spined toad (Duttaphrynus melanostictus).

Animal translocations provide striking examples of the human footprint on biodiversity. Combining continental-wide genomic and DNA-barcoding analyses, we reconstructed the historical biogeography of the Asian black-spined toad (Duttaphrynus melanostictus), a toxic commensal amphibian that currently threatens two biodiversity hotspots through biological invasions (Wallacea and Madagascar). The results emphasize a complex diversification shaped by speciation and mitochondrial introgression that comprises two distinct species.

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.

An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.

Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients.

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.