Role of traditional vs social media on purchase intention: A sequential mediation model.

In an increasingly saturated media environment, this study examines how traditional and social media influence consumer purchase intentions in the fashion industry through nostalgia, perceived credibility, and persuasion knowledge. Drawing on the Elaboration Likelihood Model (ELM), survey data from fashion-conscious consumers in Pakistan were analyzed using structural equation modeling. The results show that both media types evoke nostalgia and enhance credibility, which in turn reduce persuasion knowledge and strengthen purchase intentions.

Unsupervised machine learning solutions for electroosmotically driven Casson hybrid nanofluid flow using sigmoid and Fibonacci neural networks: a biomedical approach.

This work investigates the electroosmotic peristaltic transport of a Casson (blood)-based hybrid nanofluid via an asymmetric channel embedded inside a porous medium. The model takes into consideration electric and magnetic field effects, Ohmic heating, as well as velocity and thermal slip conditions. The governing equations are simplified and solved by employing unsupervised sigmoid-based neural networks (SNNs), Fibonacci-based neural networks (FNNs), and their hybrid model (FSNNs) under the assumptions of low Reynolds number and long wavelength.

Levilactobacillus brevis MW362790 and Pediococcus pentosaceus MT323062 Alleviate Cadmium-Induced Oxidative Stress and Hepato-renal Toxicity.

Heavy metal contamination, particularly cadmium (Cd), poses serious health risks through bioaccumulation and oxidative stress. Certain lactic acid bacteria (LAB) exhibit metal-binding properties and have demonstrated potential in mitigating Cd toxicity. This study evaluated the ameliorative effects of two locally isolated LAB strains Levilactobacillus brevis MW362790 and Pediococcus pentosaceus MT323062 against Cd-induced toxicity in rat model.

Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles.

Diabetes mellitus (DM) is a chronic metabolic disease that affects millions of people worldwide, posing major health risks and financial challenges. Early diagnosis and treatment are essential for reducing complications and improving patient outcomes. This research explores the application of supervised algorithms to predict DM using a variety of datasets such as clinical features, genetic markers, and lifestyle variables.

Dynamics and stability of soliton solutions for the seventh-order Sawada-Kotera-Ito equation with applications.

The seventh-order Sawada-Kotera-Ito equation is a fundamental nonlinear partial differential equation that arises in modeling complex wave phenomena in fluid dynamics and other physical systems. In this study, two analytical techniques, namely, the [Formula: see text]-model expansion method and the extended simplest equation method are employed to derive exact analytical solutions to the seventh-order Sawada-Kotera-Ito equation. As a result, we construct explicit solutions that describe solitary waves, kink and anti-kink waves, breather-type waves, and other wave structures.

Intelligent waste sorting for urban sustainability using deep learning.

Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. The model is trained on 15,535 images from a publicly available dataset using preprocessing and data augmentation to increase generalisation and mitigate class imbalance.

Modelling cross-diffusion in MHD Williamson nanofluid flow over a nonlinear stretching surface via Morlet wavelet neural networks.

In this paper, a new numerical technique was developed to investigate magnetohydrodynamic (MHD) flow of Williamson nanofluid past a nonlinear stretching surface imbedded in a porous medium laden with Soret and Dufour effects. The control equations, which are highly nonlinear partial differential equations, are first converted into ordinary differential equation (ODEs) using similarity transformation and then are solved effectively by the hybrid computational method applying Morlet Wavelet Neural Networks (MWNNs) combined with a heuristic optimizers neural network and particle swarm as MWNNs-PSO-NNA. The proposed MWNNs-PSO-NNA shows a very low mean square error and Theil's Inequality Coefficient indicating that the accuracy of the model.

Fractional-order SIR model for ADHD as a neurobiological and genetic disorder.

This study develops and analyzes a fractional-order Susceptible-Infected-Recovered (SIR) epidemiological model to investigate the transmission dynamics and control of Attention Deficit Hyperactivity Disorder (ADHD) within a population. The model incorporates memory effects via the Caputo fractional derivative, capturing long-term dependencies characteristic of ADHD progression. Numerical simulations are carried out using the Laplace Residue Power Series (LRPS) and Runge-Kutta 4th Order (RK4) methods for different values of the fractional-order parameter α.

A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model.

The amount of video data produced daily by today's surveillance systems is enormous, making analysis difficult for computer vision specialists. It is challenging to continuously search these massive video streams for unexpected accidents because they occur seldom and have little chance of being observed. Contrarily, deep learning-based anomaly detection decreases the need for human labor and has comparably trustworthy decision-making capabilities, hence promoting public safety.

A hybrid steganography framework using DCT and GAN for secure data communication in the big data era.

The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today's digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data.

An adaptive feedback system for the improvement of learners.

Teachers who are aware of their students' strengths and weakness can tailor their teaching methodologies to meet the challenging students efficiently for better results. This helps them to identify any potential learning challenges at an early stage leading to improved academic performance and success ratio. This also fosters a learning environment where students feel motivated and valued to excel in their respective fields.

Predicting tuberculosis drug properties using extended energy based topological indices via a python driven QSPR approach.

In the present work, the physicochemical characteristics of important anti-tuberculosis (TB) drugs such as isoniazid, pyrazinamide, ethambutol, ethionamide, linezolid, and levofloxacin are explored using extended energy-based topological indexes. Based on the molecules of the drugs, we calculate the extended energies of many widely recognized indexes such as Zagreb Second Index, Harmonic Index, Randic Index, Sombor Index, Reduced Sombor Index, and Average Sombor Index. All the calculations are done using Python, and the rigorous algorithmic implementation in the form of matrix formulation and computation of the eigenvalue is also given for reproducibility.

mediated green synthesized cobalt oxide nanoparticles dispersed on reduced graphene oxide for electrocatalytic water splitting.

In this research, we synthesized (CoO) and (CoO) nanoparticles (NPs) utilizing aqueous and ethanolic extracts, respectively, of leaves. The biosynthesized NPs were sonicated with reduced graphene oxide (rGO) to produce rGO@(COO) and rGO@(CoO) nanocomposites (NCs) and their respective calcined (700 °C) products rGO@(COO) and rGO@(CoO). The nanomaterials (NMs) were characterized through XRD, FTIR, UV-visible spectroscopy, SEM, TGA, and DSC analyses.

Legendre based neural networks integrated with heuristic algorithms for the analysis of Lorenz chaotic model: an intelligent and comparative study.

The optimal control methods for managing Lorenz model are achieved using an innovative intelligent computing framework that integrates artificial neural networks with stochastic unsupervised learning-based optimizers, specifically Firefly (FA) and Archimedes algorithm (AOA). The hyper parameters (weights & biases) of unsupervised neural networks are refined through Legendre polynomials Artificial Neural Networks (LENN) optimized with global search techniques, including FA-AOA collectively referred to as LENN-FA-AOA. This design approach is employed to Lorenz model across three (3) different scenarios using various step sizes and input intervals.

Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system.

Wind turbine faults, including electrical, mechanical or aerodynamics-related, can potentially reduce operational efficiency, causing downtimes or, in some cases, leading to severe damage. Hence, timely detection of these operational anomalies is crucial for optimizing performance and reducing maintenance costs. The following study explores the application of Supervisory Control and Data Acquisition (SCADA) data for fault detection and diagnosing different operational states in wind turbines by focusing on environmental and operational factors which affect the performance.

Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI.

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns.

Weissella confusa MW051433 Mitigates Hypercholesterolemia by Biliary Cholesterol Excretion: The In Vivo Evidence.

The current study explored cholesterol-lowering potential and the underlying mechanism (biliary cholesterol excretion) of a prebiotic (mannitol), probiotic (Weissella confusa MW051433), and their synbiotic (mannitol + W. confusa MW051433) in high fat diet induced hypercholesterolemic murine model. The experiment employed a randomized controlled design consisting of five groups: negative control, positive control and three treatment groups, with six rats in each group.

Multi blockchain architecture for judicial case management using smart contracts.

The infusion of technology across various domains, particularly in process-centric and multi-stakeholder sectors, demands transparency, accuracy, and scalability. This paper introduces a blockchain and intelligent contract-based framework for judicial case management, proposing a private-to-public blockchain approach to establish a transparent, decentralized, and robust system. An Integrated Solution for Judicial Case Management using Blockchain Technology and Smart Contracts.

Peristaltic flow of electromagnetic tri-hybrid Carreau nanofluid using backpropagated Levenberg-Marquardt technique: an entropy generation analysis in blood cells.

The present research concentrates on examining entropy generation during the flow phenomenon of a three-dimensional peristaltic motion of a magnetized tri-hybrid nanofluid within a curved rectangular duct using a machine learning technique called backpropagated Levenberg-Marquardt (BLMT). The Carreau constitutive model is used for base liquid (blood). To obtain the most accurate solutions for the governing equations, an analytical tool called the Homotopy Perturbation Method (HPM) is utilized along with a machine learning methodology ANN-BLMT method on MatLab.

Big data analytics and AI as success factors for online video streaming platforms.

As the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave.

Managing workplace hazards and environmental contaminants for promoting green human resource management and health and safety in developing economies.

BackgroundAt a global scale, the impact of the manufacturing sector on the environment has led to the increased demand for the sustainable practices and strategies that help the businesses achieve environmental, social, and economic objectives particularly within the context of developing economies.ObjectiveThis paper addresses this gap by investigating the relationship between employees and firms' environmental performance in the manufacturing sector operating in a developing country, Pakistan-where the environmental focus is sparse and organizational structures rarely follow cross-functional systems.MethodsQuantitative research was employed and SmartPLS technique was used to test the theoretical model with a valid response rate of 77 percent of senior and middle-level managers of manufacturing firms.

On computing extended graph energies and their thermodynamic properties for benzenoid hydrocarbons.

In this paper, several graph-based descriptors are applied, including some extended energies, such as Sombor and Banhatti Sombor energies, for the prediction of some important thermodynamic properties of BHC. In fact, strong relationships are observed between such extended energies and many properties that include boiling point, Kovats retention index, formation enthalpy and entropy, octanol-water partition coefficient, and acentric factor. These results evidence that graph-based descriptors can be employed as a rapid and reliable tool for the prediction of thermodynamic properties.

Particle swarm optimization based analysis to unlocking the neutrino mass puzzle using [Formula: see text] flavor symmetry.

New research has highlighted a shortfall in the Standard Model (SM) because it predicts neutrinos to have zero mass. However, recent experiments on neutrino oscillation have revealed that the majority of neutrino parameters indeed indicate their significant mass. In response, scientists are increasingly incorporating discrete symmetries alongside continuous ones for the observed patterns of neutrino mixing.