Effectiveness of ChatGPT in facilitating learning for students with special educational needs: An empirical study in Saudi Arabia.

Study Purpose: This paper aims to explore the effectiveness of ChatGPT in facilitating learning for medical students with special educational needs (SEN) while acknowledging and addressing the challenges that SEN students may encounter in utilizing this technology.

Methods: This cross-sectional survey study assessed ChatGPT's efficacy in supporting medical students with SEN across three Saudi Arabian universities. Utilizing purposive and convenience sampling, a questionnaire was administered to 283 SEN students.

Towards a green economy: Investigating the impact of sustainable finance, green technologies, and environmental policies on environmental degradation.

Environmental degradation is a problem, and the consequences, in terms of emission of pollutants into different ecosystems, human health, and sustainable development are disastrous. This study explains the complex interactions that exist among sustainable finance, green technology innovation, green energy adoption, the climate change financial policy, green growth index, government spending, and financial globalization across the globe. This research applies to an extensive dataset that ranges to 23 years in 50 countries by enforcing robust dynamic econometric methods such as unit root tests, cointegration analysis, and generalized method of moments (GMM) estimations for the analysis of these complicated issues.

Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.

Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards.

Fall prevention among older adults in KSA: Role of physical therapy.

Objectives: Falls and fall-related injuries among older adults are a growing public health concern. Although multiple factors and co-morbidities are associated with falls, balance and gait disorders are among the most common causes. Physical therapists have expertise in fall-risk assessment and management.

Exploring behavioral determinants of residents' ecological conservation in rural tourism development.

This study explores the residents' behavioral determinants of ecological conservation in rural tourism development, focusing on resident participation to promote sustainable development in the tourism industry. By integrating the Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM), it examines the roles of subjective norm, responsibility attribution, ecological environmental risk perception, and human capital in shaping ecological conservation behavior. Based on data from 331 valid responses collected through a questionnaire survey, structural equation model (SEM) was performed using SPSS and AMOS to analyze ecological conservation behavior.

Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing.

Energy-Efficient and Trust-Based Autonomous Underwater Vehicle Scheme for 6G-Enabled Internet of Underwater Things.

This paper introduces a novel energy-efficient lightweight, void hole avoidance, localization, and trust-based scheme, termed as Energy-Efficient and Trust-based Autonomous Underwater Vehicle (EETAUV) protocol designed for 6G-enabled underwater acoustic sensor networks (UASNs). The proposed scheme addresses key challenges in UASNs, such as energy consumption, network stability, and data security. It integrates a trust management framework that enhances communication security through node identification and verification mechanisms utilizing normal and phantom nodes.

Evolutionary game of international trade network based on trade policy differences.

Trade policy differences among different countries are important factors affecting international trade cooperation. In this paper, we build an evolutionary game model of international trade in which complex networks portray game relationships and trade policy differences are game strategies of players. Compared with the fully coupled game relationship and two-strategies game, the game relationship dynamic adjustment, trade policy differences and stochastic game payoffs in this paper are more in line with the real international trade context.

How Does Gamification Improve Purchase Intention? Through the Lens of Perceived Brand Coolness and Time Poverty.

Gamification has been extensively employed in marketing practices to meet the diverse needs of consumers. Previous research suggests that gamification marketing plays a pivotal role in influencing customer purchase intention. However, the precise mechanism through which gamification marketing impacts purchase intention requires further investigation.

The Impact of Physical Hazards on Workers' Job Satisfaction in the Construction Industry: A Case Study of Korea.

This study investigates the impact of workplace physical hazards on job satisfaction in the construction industry, focusing on the mediating role of mental threats and the moderating effects of perceived job quality and security. The study findings indicate that exposure to physical hazards significantly contributes to mental stress, leading to reduced job satisfaction. Importantly, a heightened awareness of physical risks amplifies the mental burden, further decreasing job satisfaction.

Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.

Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan.

Talent management intervention towards performance outcomes of Jordanian telecommunication companies: mediating role of perceived organizational support.

Background: Grounding on the Resource-Based View (RBV) and organization support theory (OST), this study examines the effect of talent management practice (TMP) on perceived organizational support (POS), organizational performance (OP), and employee performance (EP) within the Jordanian telecom sector, with a specific focus on the mediating effect of POS's between TMP-EP and OP relationship.

Methods: This study uses a cross-sectional method to collect data from 397 personnel at three Jordanian telecom enterprises. The Smart-PLS application assisted with structural equation modeling, which was used to examine the data.

Advanced analysis via statistical physics model to study the efficiency of catechol removal from wastewater using Brazil nut shell activated carbon.

This paper presented the preparation, characterization, and adsorption properties of Brazil nut shell activated carbon for catechol removal from aqueous solutions. The equilibrium adsorption of catechol molecules on this activated was experimentally quantified at pH 6 and temperatures ranging from 25 to 55 °C, and at 25 °C and pH ranging from 6 to 10. These results were utilized to elucidate the role of surface functionalities through statistical physics calculations.

A novel EWMA-based adaptive control chart for industrial application by using hastings approximation.

Control charts are commonly used for process monitoring under the assumption that the variable of interest follows a normal distribution. However, this assumption is frequently violated in real-world applications. In this study, we develop an adaptive control chart based on the exponentially weighted moving average (EWMA) statistic to monitor irregular variations in the mean of the Truncated Transmuted Burr-II (TTB-II) distribution, employing Hastings approximation for normalization.

Customer churn prediction model based on hybrid neural networks.

In today's competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer retention and ensuring the sustainable development of an organization. However, traditional machine learning algorithms and single deep learning models have limitations in extracting complex nonlinear and time-series features, resulting in unsatisfactory prediction results. To address this problem, this study proposes a hybrid neural network-based customer churn prediction model, CCP-Net.

Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation.

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations.

AI companies' strategies with traditional vs. digital assets amid geopolitical and banking crises.

The financial industry evolves rapidly, offering numerous opportunities but also introducing inherent risks that require careful navigation by institutions. This paper explores the performance of traditional and digital assets as tools for hedging, diversification, and safe-haven strategies during the Russian-Ukrainian crisis and the Silicon Valley Bank (SVB) collapse. Utilizing daily data from April 30, 2021, to September 15, 2023, and employing the Asymmetric Dynamic Conditional Correlation (ADCC) model, which combines four Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models and two residual distributions, we conduct a comprehensive analysis.

Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things.

Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged.

Physician Deserts: Navigating the Texas Terrain of Provider Supply and Demand with GIS Mapping.

Rural health disparities in Texas impact population health due to limited healthcare access, insurance, and transportation challenges, especially in medically underserved areas. A shortage of specialists in rural regions worsens these issues, leading to increased morbidity and mortality rates. Our research aimed to address a knowledge gap by investigating the availability of three medical specialists-cardiologists, pulmonologists, and endocrinologists-in rural counties of Texas and identifying areas where access to healthcare is limited.

Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in vehicular ad hoc networks (VANETs).

This research tackles the critical challenge of BeiDou signal spoofing in vehicular ad-hoc networks and addresses significant risks to vehicular safety and traffic management stemming from increased reliance on accurate satellite navigation. The study proposes a novel hybrid machine learning framework that integrates Autoencoders and long short-term memory (LSTM) networks with an advanced cryptographic method, attribute-based encryption, to enhance the detection and mitigation of spoofing attacks. Our methodology leverages both real-time and synthetic navigational data in a comprehensive experimental setup that simulates various spoofing scenarios to test the resilience of the proposed system.

A new two-parameter Rayleigh distribution: Statistical properties, actuarial measures, regression analysis, and applications.

This paper presents a novel two-parameter distribution derived from the Rayleigh distribution, thoroughly investigating its essential mathematical properties. We employ estimation techniques to determine the proposed distribution's estimated parameters. Through extensive simulation studies, we analyze and evaluate the asymptotic behavior of the model estimators.

Advance drought prediction through rainfall forecasting with hybrid deep learning model.

Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management.

An empirical research based on spatial-temporal evolution of high-quality tourism development in Fujian Province of China.

The high-quality development of tourism is crucial to the sustainable development of regional economy. To evaluate high-quality tourism development, this paper has developed an index system with 6 second-level indicators and 24 third-level indicators and used methods of entropy-weight, AHP, and TOPSIS to empirically assess the high-quality tourism development of 9 cities in Fujian Province. According to the results, there are obvious regional differences in the development of high-quality tourism in Fujian Province.

On the generalization of Hermite-Hadamard type inequalities for -convex function via fractional integrals.

The main motivation in this article is to prove new integral identities and related results. In this paper, we deal with -convex function, Hermite-Hadamard type inequalities, and Katugampola fractional integrals. Firstly, we established the Hermite-Hadamard type inequalities for -convex function via Katugampola fractional integrals operators.

A case study for Kuwait mortality during the consequent waves of COVID-19.

The World Health Organization (WHO) announced on March 11, 2020, that COVID-19 could be considered a pandemic. This epidemic has become a huge issue for academics, doctors, healthcare providers, epidemiologists, and decision-makers alike. Motivated by studying natural phenomena, we concentrated on making a statistical model capable of fitting natural pandemics into the whole world.