This paper introduces a proper model for ferromagnetic core which includes hysteresis, saturation, eddy current losses, and anomalous losses. This model can generate symmetrical and asymmetrical loops with high accura...
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Like other global mass gatherings, religious pilgrimages, such as Hajj, Arba’een, and the Hindu festival Kumbh Mela, attract millions of pilgrims to gather at specific holy sites on specific dates. During disease pan...
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Like other global mass gatherings, religious pilgrimages, such as Hajj, Arba’een, and the Hindu festival Kumbh Mela, attract millions of pilgrims to gather at specific holy sites on specific dates. During disease pandemics, mass gatherings can become super spreader events, causing exponential growth of infections in multiple regions. Epidemic modeling approaches can be valuable tools for studying the impact of mass gatherings on global health during disease outbreaks. To assess the use of epidemic models at religious pilgrimages, we compile published studies that proposed epidemic models at mass religious gatherings. A review of existing epidemic models at various religious gatherings highlights the role of epidemic modeling approaches in assessing the implications of religious pilgrimages on disease pandemics. All the articles surveyed showed a link between hosting religious gatherings and an increase in the number of cases of the simulated epidemic. In addition, we found that the SEIR mathematical model was the most common type developed with variations in some of the retrieved papers. The results reported in these studies motivate further investigation of the role of epidemic modeling and simulation in estimating the size and geographic scale of infections while hosting religious gatherings. Finally, we believe that this survey paper draws attention to the application of epidemic models in the advanced planning of recurrent religious pilgrimages, as it is not feasible to cancel, suspend, or reallocate these pilgrimages. These epidemic models can provide a baseline for policymakers to determine which control measures should be implemented and when.
The current landscape of data-centric Internet of Vehicles (IoVs) encompasses a fusion of Human-driven Vehicles (HVs), Autonomous Vehicles (AVs), Road-Side Units (RSUs), and edge-based devices engaged in periodic comm...
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The current landscape of data-centric Internet of Vehicles (IoVs) encompasses a fusion of Human-driven Vehicles (HVs), Autonomous Vehicles (AVs), Road-Side Units (RSUs), and edge-based devices engaged in periodic communication. Given the stringent latency requirements inherent in vehicular communications, the emergence of edge-based vehicular Digital Twins (DTs) plays a pivotal role in problem-solving, ensuring rapid response, regulatory compliance, and seamless availability. While these communications serve as the backbone of IoV, they also create an opportune environment for cybercriminals to exploit. Vulnerabilities at the network layer facilitate intrusions, resulting in a surge of data falsification attacks in recent years. Addressing this challenge demands resilient and intelligent threat detection schemes capable of adapting to the dynamic nature of IoV. This study conducts a comprehensive examination of the vulnerabilities in Vehicle-to-Digital twin (V2DT) data communication through the lens of an attacker utilizing False Data Injection Attack (FDIA). It utilizes cutting-edge Blockchain-based decentralized storage and buffering mechanisms for vehicle dynamics data en route to edge-based DTs. Further, deep learning-powered sensor data analysis serves as an additional layer of security. Evaluation of the proposed threat detection and mitigation model demonstrates 100% tamper detection in V2DT communication, coupled with a 96% accurate classification of anomalous driving behaviors, including aggressive driving or FDIAs.
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