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Brand advocates, characterized by their enthusiasm for promoting a brand without incentives, play a crucial role in driving positive word-of-mouth (WOM) and influencing potential customers. However, there is a notable lack of intelligent systems capable of accurately identifying online advocates based on their social interactions with brands. Knowledge Graphs (KGs) offer structured and factual representations of human knowledge, providing a potential solution to gain holistic insights into customer preferences and interactions with a brand. This study presents a novel framework that leverages KG construction and embedding techniques to identify brand advocates accurately. By harnessing the power of KGs, our framework enhances the accuracy and efficiency of identifying and understanding brand advocates, providing valuable insights into customer advocacy dynamics in the online realm. Moreover, we address the critical aspect of social credibility, which significantly influences the impact of advocacy efforts. Incorporating social credibility analysis into our framework allows businesses to identify and mitigate spammers, preserving authenticity and customer trust. To achieve this, we incorporate and extend DSpamOnto, a specialized ontology designed to identify social spam, with a focus on the social commerce domain. Additionally, we employ cutting-edge embedding techniques to map the KG into a low-dimensional vector space, enabling effective link prediction, clustering, and visualization. Through a rigorous evaluation process, we demonstrate the effectiveness and performance of our proposed framework, highlighting its potential to empower businesses in cultivating brand advocates and driving meaningful customer engagement strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Abu-Salih, Alotaibi, Al-Okaily, Aljaafari and Almiani.)

With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these models experience the problem due to the fast development of network technologies. There is no thorough research in regards to the latest models and their applications and advantages. As a result, it is essential to have a comprehensive study of information diffusion models. The primary goal of this research is to provide a comparative study on the existing models such as the Ising model, Sznajd model, SIR model, SICR model, Game theory and social networking services models. We discuss several of their applications with the existing limitations and further categorizations. Vulnerabilities and privacy challenges of information diffusion models are extensively explored. Furthermore, categorization including strengths and weaknesses are discussed. Finally, limitations and recommendations are suggested with diverse solutions for the improvement of the information diffusion models and envisioned future research directions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2019 The Authors.)

Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node's moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM., Competing Interests: The authors declare no conflict of interest.

In order to decrease the time on the deceased donor kidney wait list and to have more organs available, criteria for acceptable organs for transplant could be made less stringent. There are reports of successful recipient outcomes using kidney donors presenting with disseminated intravascular coagulation (DIC). We report a unique circumstance where two patients received kidneys from the same deceased donor who had DIC; one patient developed thrombotic microangiopathy (TMA) while the other did not. This difference in outcome may indicate that both donor and recipient factors contribute to the development of posttransplant TMA.