Red Sea University

About: Red Sea University is a education organization based out in Port Sudan, Sudan. It is known for research contribution in the topics: Exergy & Exergy efficiency. The organization has 73 authors who have published 111 publications receiving 812 citations. The organization is also known as: RSU & جامعة البحر الاحمر.

Internet of Things Applications, Challenges and Related Future Technologies

Abstract: Nowadays Internet of Things (IoT) gained a great attention from researchers, since it becomes an important technology that promises a smart human being life, by allowing a communications between objects, machines and every things together with peoples. IoT represents a system which consists a things in the real world, and sensors attached to or combined to these things, connected to the Internet via wired and wireless network structure. The IoT sensors can use various types of connections such as RFID, Wi-Fi, Bluetooth, and ZigBee, in addition to allowing wide area connectivity using many technologies such as GSM, GPRS, 3G, and LTE. IoT-enabled things will share information about the condition of things and the surrounding environment with people, software systems and other machines. by the technology of the IoT, the world will becomes smart in every aspects, since the IoT will provides a means of smart cities, smart healthcare, smart homes and building, in addition to many important applications such as smart energy, grid, transportation, waste management and monitoring . In this paper we review a concept of many IoT applications and future possibilities for new related technologies in addition to the challenges that facing the implementation of the IoT.

Maternal and perinatal outcomes of dengue in PortSudan, Eastern Sudan.

Abstract: Aim To investigate maternal and perinatal outcomes (maternal death, preterm delivery, low birth weight and perinatal mortality) of dengue at PortSudan and Elmawani hospitals in the eastern Sudan.

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Abstract: Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

An outbreak of hepatitis E and high maternal mortality at Port Sudan, Eastern Sudan

Abstract: During 4 months (November 2010–March 2011) of an outbreak of hepatitis E virus (HEV), 39 pregnant women presented at Port Sudan Hospital, Sudan, with various symptoms of viral hepatitis. The diagnosis of viral hepatitis was confirmed by serology using ELISA anti-HEV IgG and IgM. The mean (SD) maternal age and gestational age were 24·0 (4·2) years and 33·6 (3·7) weeks, respectively. Eight (20·5%) women were primigravidae. There were 11 (28·2%) maternal deaths, 14 (36·0%) intrauterine fetal deaths, and eight (20·5%) cases of postpartum haemorrhage. There were nine (23·0%) cases of preterm (<37 weeks of gestation) deliveries. Fulminant hepatitis with hepatic encephalopathy was the most common cause of death among these patients. Nine of these women died before delivery and the other two died immediately following the delivery due to severe haemorrhage. There were no significant differences in clinical and biochemical data between the women who died (11) and those who survived.