A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas.

Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably.

A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT.

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference.

Strengthening Marriages in Egypt: Impact of Divorce on Women.

Divorce rates have been increasing around the world, and the Middle East is not immune to this reality. This pilot study investigates the phenomenological experiences of divorce for 20 Egyptian females. The study is qualitative, using in-depth interviews asking 14 questions that address different aspects of struggling marriages and post-divorce life.