Non-parametric Bayesian mixture model to study adverse events of COVID-19 vaccines

The vaccine adverse event reporting system (VAERS) is a vital resource for post-licensure vaccine safety monitoring and has played a key role in assessing the safety of COVID-19 vaccines. However it is difficult to properly identify rare adverse events (AEs) associated with vaccines due to small or zero counts. We propose a Bayesian model with a Dirichlet Process Mixture prior to improve accuracy of the AE estimates with small counts by allowing data-guided information sharing between AE estimates. We also propose a negative control procedure embedded in our Bayesian model to mitigate the reporting bias due to the heightened awareness of COVID-19 vaccines, and use it to identify associated AEs as well as associated AE groups defined by the organ system in the Medical Dictionary for Regulatory Activities (MedDRA) ontology. The proposed model is evaluated using simulation studies, in which it outperforms baseline models without information sharing and is applied to study the safety of COVID-19 vaccines using VAERS data

An Energy-Efficient Cross-Layer approach for cloud wireless green communications

In wireless sensor networks (WSN), energy consumption is one of the crucial issues. It is very important to conserve energy at each sensor node to prolong a network lifetime. The main challenge in WSN is to develop an energy efficient algorithm to minimize energy consumption during transmitting information from deployed sensors up to the cloud resources. Many researches have been studied the designing of energy efficient technique based on one-layer stack model approach. In this study, we propose Energy-Efficient Cross-Layer (EECL) approach by using the interaction of MAC layer and physical layer information to be exploited by a network layer to achieve energy efficient communication. More precisely, network layer could utilize the MAC layer and physical layer information to establish an energy efficient route path to be used in forwarding data. The proposed EECL approach uses X-MAC protocol in support of duty cycle which introduces short preambles that switches to wake-up/sensing mode only for nodes belonging to routing path while the other nodes set to be in sleep mode. The distance between nodes that influences energy consumption and Bit Error Rate (BER) are set to be the parameters which they are help in indicating the required power for each hop during route path selection in WSN and avoid the rely-hops that suffering from high average BER and with farther distance. We conduct the experiment using Matlab to evaluate the effectiveness of our proposed approach in terms of power consumption and obtained data rate. The results show that our proposed EECL approach outperforms its representatives in the ability of tuning the power utilized in respect with required data rate that could satisfy the desired Quality-of-Service (QoS)