Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm.

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently.

Development of Chitosan-Coated Graphene Oxide and Iron Oxide Nanocomposites for Targeted Delivery of Camptothecin to Liver Cancer Cells.

Innovative drug delivery platforms for selective, regulated, and sustained release of anticancer drugs are crucial in cancer treatment. This study presents nanoparticles developed from chitosan (CS), graphene oxide (GO), and magnetite (FeO), and their nanocomposites to enhance the loading and release efficiency of camptothecin (CPT). Nanostructures were characterized using imaging microscopy, FT-IR, and X-ray diffraction, with an average crystallite size of 5.

Optimizing Microfluidic Channel Design with High-Performance Materials for Safe Neonatal Drug Delivery.

Introduction: Designing the microfluidic channel for neonatal drug delivery requires proper considerations to enhance the efficiency and safety of drug substances when used in neonates. Thus, this research aims to evaluate high-performance materials and optimize the channel design by modeling and simulation using COMSOL multiphysics in order to deliver an optimum flow rate between 0. 3 and 1 mL/hr.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks.

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging.

A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning.

Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL).

Exploring Nonlinear Reaction-Diffusion in Enzyme Immobilized Systems: Integer and Fractional Order Modeling.

This paper presented a kinetic model of the Langmuir-Hinshelwood-Hougen-Watson (LHHW) type for porous catalysts with simple one-dimensional geometry, including spheres, infinite cylinders, and flat pellets. The model was applied to systems involving immobilized enzymes, where enzymes are attached to porous support materials to enhance stability and reusability. The LHHW model provided a tool for understanding and modeling reaction kinetics in heterogeneous porous catalysts and immobilized enzymes.

ERABiLNet: enhanced residual attention with bidirectional long short-term memory.

Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI).

Detection and classification of electrocardiography using hybrid deep learning models.

Objective: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features.

Methods: Precision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques.

Chemical Composition Analysis and Assessment of Antioxidant and Anti-Inflammatory Activities of Crude Extract of on Carrageenan-Induced Paw Edema in Wistar Albino Rats.

A member of the Phyllanthaceae family, is a well-known plant in the tribal areas of Sri Lanka, India's Shaurastra region, Australia, and Malaysia. This study provides information about , a plant with a wide range of therapeutic uses in India. Different extracts from the leaves and roots of were evaluated for their physical and chemical properties, preliminary phytochemical parameters, and pharmacological activities in the current study, followed by their fourier transform infrared spectroscopy (FTIR), gas chromatography-mass spectrometry (GC-MS), antioxidant, and anti-inflammatory properties.

Investigation of the molecular structure of CHBP, biological activities and SARS-CoV-2 protein binding interaction by molecular and biomolecular spectroscopy approaches.

The objective of this investigation is to learn more about the structural, electrical, spectroscopic, and physiochemical characteristics of biologically active cyano-4'-hydroxybiphenyl (CHBP). The title molecule's optimized conformational analysis was computed using the DFT/B3LYP/6-311++G (d, p) level of theory. The observed wavenumbers were compared with theoretical FT-IR and FT-Raman spectra.

Molecular Understanding and Pharmacological Potency of Plant-Derived Compounds in Colorectal Cancer (CRC): A Critical Analysis and Future Perspectives.

Colorectal cancer (CRC) is the main driver of fatality and the 3rd most often determined malignancy. Despite advances in detection and therapy, colorectal cancer (CRC) endures as the largest driver of cancer-related morbidity, and mortality. Modern habits and dietary negligence might be one of the reasons that have enhanced cancer prevalence.

Cardiology and Neurophysiological Stimulation of Internet Gaming Disorders: A Systematic Review.

Background: Internet Gaming Disorder (IGD) is recognized as a mental health condition associated with excessive video gaming, leading to functional impairments. The inclusion of IGD in the DSM-5 has underscored the importance of comprehensively understanding its physiological and psychological effects.

Objective: This systematic review aims to analyze and synthesize existing literature on the cardiophysiological and neurophysiological activities of individuals diagnosed with IGD, with a focus on identifying patterns, trends, and implications for clinical practice and future research.

Assessment of genome mutation analysis for tumor-informed detection of circulating tumor DNA in patients with breast cancer.

Introduction: Breast cancer, one of the most aggressive types of cancer, poses significant challenges for diagnosis and treatment. Emerging as a promising biomarker, circulating tumor DNA (ctDNA) can be used to identify and monitor disease risk. This study sought to examine the impact of mutations in various genes on the progression of breast cancer.

Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data.

Background: Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography).

Investigation of TiO Nanoparticles Influence on Tensile Properties and Thermal Stability of Dry and Wet Luffa-Epoxy Nanocomposites.

Background: Recently, progress has been made toward understanding the efficiency of polymer composites with natural fibres. With the hope of enhancing the characteristics of polymer composites supplemented with natural fibres in a watery environment, TiO nanoparticles have been used to improve their performance in the field.

Methods: These nanoparticles were filled in luffa-epoxy components at 1, 3, and 5% volume fractions.

Evaluation of cytocompatibility and cell proliferation of electrospun chitosan/polyvinyl alcohol/montmorillonite clay scaffold with l929 cell lines in skin regeneration activity and in silico molecular docking studies.

The present investigation describes the development of a novel Chitosan/Polyvinyl Alcohol/Montmorillonite Clay (CS/PVA/MMT) scaffold by adopting an electrospinning method, and their biocompatibility was evaluated in vitro with L929 fibroblast cell line to ascertain its use in wound healing applications. The fabricated scaffold was characterized using analytical techniques. FT-IR measurement exhibited the existence of relevant functional groups and XRD implies scaffolds' amorphous nature.

Comparison study (experimental and theoretical), hydrogen bond interaction through water, donor acceptor investigation and molecular docking study of 3,3-((1,2-phenylenebis (azaneylylidene)) bis (methaneylylidene)) diphenol.

The novel Schiff's base (CS6) was synthesized and confirmed by various studies. The B3LYP/cc-pVDZ basis set was used for theoretical study and the results indicated that both the theoretical and experimental studies correlated well. The interaction energy of CS6-water complex calculated by using the local energy decomposition analysis was found to be -7.

Synthesis, topology, molecular docking and dynamics studies of -phenylenediamine derivative.

The N, N'-(1,2-phenylene) bis (1- (4- chlorophenyl) methanimine) (CS4) was synthesized and characterized by infrared (IR), absorption (UV-vis) and NMR (H and C) spectral analyses. The structural parameters, vibrational frequencies, potential energy and the distribution analysis (PED) were calculated by using DFT with the basis set of B3LYP/cc-pVDZ and these spectral values were compared to the experimental values. HOMO and LUMO studied were performed in order to understand the stability and biological activity of the compound.

Revolutionizing heart disease prediction with quantum-enhanced machine learning.

The recent developments in quantum technology have opened up new opportunities for machine learning algorithms to assist the healthcare industry in diagnosing complex health disorders, such as heart disease. In this work, we summarize the effectiveness of QuEML in heart disease prediction. To evaluate the performance of QuEML against traditional machine learning algorithms, the Kaggle heart disease dataset was used which contains 1190 samples out of which 53% of samples are labeled as positive samples and rest 47% samples are labeled as negative samples.

Computational analysis of anti-cancer drug hydroxyurea adsorption on nanocages of gold, silver and copper: SERS and DFT assessment.

The use of nanostructures in targeted drug delivery is effective in decreasing anticancer drug toxicity. Here, we discuss the theoretically predicted adsorption and interaction behavior of hydroxyurea [HU] with nano metal cages (nmC). HU interact the nmC through the N4 in primary amine with energies of -29.

Multi-Scale and Spatial Information Extraction for Kidney Tumor Segmentation: A Contextual Deformable Attention and Edge-Enhanced U-Net.

Kidney tumor segmentation is a difficult task because of the complex spatial and volumetric information present in medical images. Recent advances in deep convolutional neural networks (DCNNs) have improved tumor segmentation accuracy. However, the practical usability of current CNN-based networks is constrained by their high computational complexity.

Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues.

A Comparative analysis of PESC and PPSC copolyesters: Insights into viscosity, thermal behavior, crystallinity, and biodegradability.

The study examined various properties of synthesized copolyesters PESC and PPSC. Inherent viscosities of the copolyesters, measured in 1,4-dioxane at 32 °C, were 0.65 dL/g for PESC and 0.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems.

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability.