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Healthcare 4.0 is considered the most promising technology for gathering data from humans and strongly couples with a communication system for precise clinical and diagnosis performance. Though sensor-driven devices have largely made our everyday lives easier, these technologies have been suffering from various security challenges. Because of data breaches and privacy issues, this heightens the demand for a comprehensive healthcare solution. Since most healthcare data are sensitive and valuable and transferred mostly via the Internet, the safety and confidentiality of patient data remain an important concern. To face the security challenges in Healthcare 4.0, Web 3.0 and blockchain technology have been increasingly deployed to resolve the security breaches due to their immutability and decentralized properties. In this research article, a Web 3.0 ensemble hybrid chaotic blockchain framework is proposed for effective and secure authentication in the Healthcare 4.0 industry. The proposed framework uses the Infura Web API, Web 3.0, hybrid chaotic keys, Ganache interfaces, and MongoDB. To allow for more secure authentication, an ensemble of scroll and Henon maps is deployed to formulate the high dynamic hashes during the formation of genesis blocks, and all of the data are backed in the proposed model. The complete framework was tested in Ethereum blockchain using Web 3.0, in which Python 3.19 is used as the major programming tool for developing the different interfaces. Formal analysis is carried out with Burrows–Abadi–Needham Logic (BAN) to assess the cybersecurity reliability of the suggested framework, and NIST standard tests are used for a thorough review. Furthermore, the robustness of the proposed blockchain is also measured and compared with the other secured blockchain frameworks. Experimental results demonstrate that the proposed model exhibited more defensive characteristics against multiple attacks and outperformed the other models in terms of complexity and robustness. Finally, the paper gives a panoramic view of integrating Web 3.0 with the blockchain and the inevitable directions of a secured authentication framework for Healthcare 4.0. [ABSTRACT FROM AUTHOR]

Presently, numerous universities in Saudi Arabia have embraced online learning following the experience gained during the COVID-19 pandemic. While several studies have assessed the quality of online learning and student satisfaction during that period, limited research has explored students' satisfaction post-pandemic control despite several universities planning to offer some courses online. Investigating student satisfaction post-pandemic is crucial for predicting the quality of online learning and assisting universities in enhancing the outcomes of online learning. Consequently, this study aims to examine student satisfaction with online learning by proposing a model derived from the updated Information System Success Model. The updated model factors include system quality, service quality, and information quality, supplemented by student–student interaction quality and self-directed learning. Data were collected from a sample of 150 undergraduates at King Saud University in the second semester of the 2023–2024 academic year. The research model was validated using the PLS approach. The findings indicated that only self-directed learning significantly affects students' satisfaction with online learning. This study contributes theoretically by expanding the existing literature and enhancing the understanding of the factors that drive student satisfaction. Moreover, it provides practical contributions to decision-makers and educators developing online learning strategies focusing on enhancing self-directed learning abilities. [ABSTRACT FROM AUTHOR]

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method's parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems. [ABSTRACT FROM AUTHOR]

This research investigates the uncertainties in supply chains using symmetrical and asymmetrical modeling tools, focusing on the attitudes of millennials towards Facebook retail shopping. By exploring antecedents such as pleasure, credibility, and peer interaction, this study delves into the extent of E-commerce via Facebook among Generation Z in the Middle East. Built on an exhaustive literature review, a conceptual framework is designed targeting solely Generation Z members. Employing partial least squares structural equation modeling for data analysis, the findings indicate a strong correlation between attitude and the propensity of Generation Z to make Facebook retail purchases (R2 = 0.540), affecting enjoyment, credibility, and peer communication (R2 = 0.589). This study offers strategies for supply chain improvements and validates the potential of E-commerce on Facebook among Generation Z. [ABSTRACT FROM AUTHOR]

Logistics in the healthcare industry involves coordinating the distribution of medical supplies and equipment across various departments and organizations. Supply Chain Management can help healthcare facilities identify weaknesses and devise strategies to address them. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates the motivations behind the individuals' desire to use Internet of Things (IoT) solutions in healthcare. In order to better understand the factors that influence the use of IoT for e-HMS, a survey was administered to 210 healthcare IoT users. The study focuses on the potential medicinal applications of IoT technologies and incorporates the concepts of performance expectations, healthcare hazard, and trust (PHT) and perceived enabling circumstances (PFC) to complement past findings in the field. Overall, the study appears to be focused on contributing to the existing knowledge about the factors that influence the adoption of IoT technologies in healthcare, and it emphasizes the importance of considering theoretical constructs such as PHT and PFC in this context. The findings of the study can be used by IoT creators, medical experts, and vendors to optimize e-HMS and provide insight into the potential and limitations of UTAUT simulation to improve the logistic of Supply Chain Management in healthcare 4.0. The results have been analyzed by applying machine learning classifiers and have been visualized using different metrics. [ABSTRACT FROM AUTHOR]