Machine learning algorithms for voltage stability assessment in electrical distribution systems.

Voltage instability poses a significant challenge by limiting power system operation and transmission capacity. Rapid detection and effective corrective actions are essential to prevent voltage collapse. However, traditional methods for assessing voltage security margins are computationally intensive and often impractical for real-time applications.

Solvent polarity effects on the FTIR spectrum, and thermodynamic and electronic properties of metronidazole and its binding with antibacterial drugs: a DFT and molecular docking study.

Metronidazole is widely used as an antimicrobial, particularly effective against anaerobic bacteria and protozoan infections. This study investigates solvent polarity effects on the Fourier transform infrared (FTIR) spectrum, and thermodynamic and electronic properties of metronidazole semiempirical, Hartree-Fock (HF), and density functional theory (DFT) methods. Its binding with antibacterial drugs was also investigated molecular docking.

Genetic surveillance of Plasmodium-Anopheles compatibility markers during Anopheles stephensi associated malaria outbreak.

Background: Despite a previous decline in malaria in Ethiopia, an outbreak in Dire Dawa in 2022 implicated the invasive vector Anopheles stephensi as responsible. The efficient transmission of Plasmodium by invasive An. stephensi raises questions about the molecular basis of compatibility between parasite and vector, and the origin of the Plasmodium being transmitted.

Estimation of state of health for lithium-ion batteries using advanced data-driven techniques.

Accurate estimation of the State of Health (SOH) is crucial for ensuring the performance, safety, and longevity of lithium-ion batteries in electric vehicles. Traditional methods, such as Coulomb Counting and the Extended Kalman Filter, often lack the accuracy and computational efficiency required for modern applications. This study proposes an advanced framework that leverages machine learning models to model the nonlinear degradation patterns of lithium-ion batteries by focusing on key features such as voltage, current, internal resistance, and temperature.

Impact of asymptomatic infections on malaria transmission dynamics.

A considerable proportion of malaria infections are asymptomatic, serving as reservoirs that sustain transmission. This study develops a deterministic mathematical model to examine the spread of malaria with a focus on asymptomatic infections. By analyzing key properties such as positivity, boundedness, and stability of equilibria, the model highlights the epidemiological relevance of these silent carriers.

Advances in Bio-Microelectromechanical System-Based Sensors for Next-Generation Healthcare Applications.

Microelectromechanical system (MEMS)-based sensors have become essential in various fields, including healthcare, automotive, and industrial applications. These sensors integrate mechanical structures and electronics on a single chip, allowing precise, compact, and efficient measurements of parameters like pressure, force, acceleration, and chemical reactions. In this context, this review article presents the essential role of MEMS sensors in healthcare applications.

Chaotic chimp-mountain gazelle optimized FOPID control for frequency regulation in islanded airport microgrids with heterogeneous energy systems.

Ensuring stable frequency regulation in islanded airport microgrids is a challenging task owing to the intermittency of renewable energy sources, unpredictable load variations, and nonlinear dynamics. Conventional optimization techniques often struggle with premature convergence and sub-optimal controller tuning, leading to a poor transient response and inadequate frequency stabilization. These challenges necessitate an advanced optimization strategy that can efficiently handle dynamic airport environments while ensuring enhanced frequency stability.

Experimental investigation on impact of fuel jet size on performance of gasoline-ethanol blend in a SI engine vehicle.

Ethanol is being used in spark ignition engines in Ethiopia for powering vehicles. The target of this research is to use the higher percentage of gasoline-ethanol blend E30 with a simple fuel system modification on a 4-cylinder 4-stroke engine on a vehicle and test its performance. The modified engine was tested for its performance with gasoline ethanol blend by varying its operating conditions and the data were recorded on exhaust emission, wheel torque, brake torque, bp, bsfc and thermal efficiency.

Impact of double split slot geometry on aerodynamic performance of modified airfoil for wind turbine blades.

Introducing a slot into an airfoil is a passive flow control technique that enhances aerodynamic performance by manipulating the boundary layers of fluid flow. This study investigates the aerodynamic performance of a novel double-split slot design using the NACA 0018 airfoil through a detailed 2D steady-state numerical analysis. A parametric study was conducted to evaluate the influence of key design parameters, including slot outlet location, outlet width, and wedge element length, on the force coefficients and the flow structure around the airfoil.

Evaluation of vero and chicken embryo fibroblast cell-based newcastle disease vaccine efficacy in chickens.

The current study was carried out to develop a chicken embryo fibroblast (CEF) and Vero cell-adapted Newcastle disease virus thermostable strain (NDV-I-2) vaccine and evaluate its efficacy against challenge infection. CEF and Vero cells were infected with the vaccine virus, and serial passage was continued until passage 8 and 15, respectively. A characteristic cytopathic effect (CPE) induced by the virus was noted beginning from passages 3 and 10 in the CEF and Vero cell monolayers at 72 h post-infection.

Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.

Accurate detection of brain tumors remains a significant challenge due to the diversity of tumor types along with human interventions during diagnostic process. This study proposes a novel ensemble deep learning system for accurate brain tumor classification using MRI data. The proposed system integrates fine-tuned Convolutional Neural Network (CNN), ResNet-50 and EfficientNet-B5 to create a dynamic ensemble framework that addresses existing challenges.

YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation.

Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato.

Anti-inflammatory activity of flavanones isolated from the roots of Pronephrium penangianum.

Ethnopharmacological Relevance: Pronephrium penangianum, a traditional Chinese Tujia Ethnomedicine, is commonly used to treat various inflammatory conditions, including strains, dysentery, and traumatic injuries.

Aim Of The Study: The study aimed to examine the anti-inflammatory activity of flavanones isolated from the roots of P. penangianum.

AHP and Geospatial technology-based assessment of groundwater potential zones in Natham taluk, Tamil nadu, India.

The world's groundwater resources are under tremendous strain due to overuse and significant climatic changes. The need for potable water for industrial, agricultural, and domestic use is increasing worldwide, necessitating an assessment of aquifer productivity and groundwater potential. Therefore, the present study aimed to evaluate groundwater potential zones (GWPZ) in Natham Taluk, Dindigul district, a hard rock area, to promote sustainable development.

Spatial assessment of heavy metal contamination in groundwater in the Kadaladi region, Tamil Nadu, India.

Groundwater quality in Kadaladi is a critical concern due to contamination from natural and human activities. This study assesses heavy metal concentrations across pre-monsoon and post-monsoon seasons at 44 sampling sites, using the Heavy Metal Pollution Index (HPI), Metal Index (MI), and non-carcinogenic risk (HQ) assessments. Key heavy metals analysed include Cu, Pb, Zn, Ni, Cd, Mn, and Fe, in Pre-monsoon and Post-monsoon with comparisons to WHO (2017) standards using atomic absorption spectrometry.

Spatial distribution of exotic lumbricid earthworm Octolasion tyrtaeum in endangered Taxus contorta stands across Northwest Himalayan moist temperate forests.

This study investigates the distribution and ecological implications of earthworm populations in a moist temperate forest of the northwest Himalayas, located in Himri within the Sunni Forest Division of District Shimla, Himachal Pradesh. Over a two-year period, a total of 1101 earthworms were collected during the autumn and spring seasons from the understory of Taxus contorta Griff., an endangered conifer species listed by the IUCN.

Optimizing intelligent reflecting surface assisted visible light communication networks under blockage and practical constraints using TLBO for IoT applications.

Wireless communication systems can enhance their capabilities by exploring new opportunities and addressing emerging challenges through the integration of the Internet of Things (IoT) in 6G networks. Visible Light Communication (VLC) stands out as a promising wireless access technology for IoT devices. This paper presents a novel Teaching-Learning-Based Optimization (TLBO) optimized Intelligent Reflecting Surface (IRS)-assisted VLC system aimed at maximizing Signal-to-Noise Ratio (SNR) and enhancing illuminance uniformity.

Preliminary Study of Morphological Variability in the Circle of Willis and Its Clinical Significance in Ethiopia: A Cadaveric Study.

Background: The Circle of Willis (COW) is a vital cerebral arterial network that maintains balanced blood flow in the brain. Despite initial insights from Thomas Willis, recent research has highlighted its complex anatomical variations.

Objective: This study aims to evaluate the anatomical variations of the COW in Ethiopian cadavers.

Mechanical properties and residual stresses in multi pass AISI 304 to AISI 1018 steel welds by SMAW and GTAW.

Welding is a highly reliable, efficient, and widely used technique for joining metallic structures. In this study, AISI 1018 Mild Steel (MS) and Stainless Steel (SS) AISI 304 were joined using Shielded Metal Arc Welding (SMAW) and Gas Tungsten Arc Welding (GTAW) multipass welding methods. Post-Weld Heat Treatment (PWHT) was performed on the weldments using an induction furnace to analyse the effects of PWHT on microstructural variations, mechanical characteristics and residual stresses of dissimilar weld joints.

VGG-EffAttnNet: Hybrid Deep Learning Model for Automated Chili Plant Disease Classification Using VGG16 and EfficientNetB0 With Attention Mechanism.

Chili plant diseases significantly impact global agriculture, necessitating accurate and rapid classification for effective management. The study introduces VGG-EffAttnNet, a hybrid deep learning model combining VGG16 and EfficientNetB0 with attention mechanisms and Monte Carlo Dropout (MCD) for robust chili plant disease classification. VGG16 captures spatial and hierarchical features, while EfficientNetB0 ensures efficient, high-accuracy learning.

Explainable AI for Cotton Leaf Disease Classification: A Metaheuristic-Optimized Deep Learning Approach.

Cotton leaf diseases significantly impact global cotton yield and quality, threatening the livelihoods of millions of farmers. Traditional diagnostic methods are often slow, subjective, and unsuitable for large-scale agricultural monitoring. This study proposes an interpretable and efficient deep learning (DL) framework for the accurate classification of cotton leaf diseases using a hybrid architecture that combines EfficientNetB3 and InceptionResNetV2.

DBA-DeepLab: Dual-Backbone Attention-Enhanced DeepLab V3+ Model for Plant Disease Segmentation.

Accurate and efficient plant disease segmentation is crucial for early diagnosis and precision agriculture. In this study, we propose a DBA-DeepLab model, i.e.

Phthalate ester contamination in groundwater at the Himalayan-Potohar Interface: health risks in Rawalpindi, Pakistan.

Phthalate esters (PAEs) are synthetic chemicals widely used as plasticizers, leading to their presence as environmental contaminants with potential health risks. This study investigates the occurrence and concentration of three phthalate esters (PAEs), dibutyl phthalate (DBP), diethyl phthalate (DEP), and dimethyl phthalate (DMP), in groundwater from Rawalpindi City, Pakistan. Fifteen samples were collected and analyzed using liquid-liquid extraction with dichloromethane and gas chromatography-mass spectrometry (GC/MS).

Deciphering of groundwater potential zones in hard rock terrain using GIS technology with AHP statistical methods: A case study of Nilgiri, Tamil Nadu, India.

This research aims to define the potential of using natural networks for groundwater mapping. While neural networks have proven effective for various perceptual tasks, the difficulty in identifying data points below the surface remains a key challenge. The area under study encompasses a mountainous region in the Western Ghats.

A first principle investigation to explore the effect of Zr-site Ti doping on structural, electronic, optical, and mechanical properties of BaZrO.

Doped BaZrO is well recognized as a promising material for proton conduction, particularly in solid oxide fuel cells (SOFCs) and various electrochemical applications. While this material has been thoroughly examined for proton conduction, it has not been as extensively studied for other potential applications, such as photocatalytic water splitting and solar cell devices. This investigation delves into the comprehensive assessment of structural, electronic, optical, mechanical, and thermodynamic properties in Ti-doped BaZrO (BaZrTiO where x = 0,0.