Iron deficiency (ID) is the most prevalent and severe nutritional disorder globally and is the leading cause of iron deficiency anemia (IDA). IDA often progresses subtly symptomatic in children, whereas prolonged deficiency may permanently impair development. Early detection and frequent screening are, therefore, essential to avoid the consequences of IDA. In order to reduce the production cost and complexities involved in building advanced ID sensors, the devices were fabricated using a home-built patterning procedure that was developed and used for this work instead of lithography, which allows for fast prototyping of dimensions. In this article, we report the development of graphene-based field-effect transistors (GFETs) functionalized with anti-ferritin antibodies through a linker molecule (1-pyrenebutanoic acid, succinimidyl ester), to facilitate specific conjugation with ferritin antigen. The resulting biosensors feature an unprecedented ferritin detection limit of 10 fM, indicating a tremendous potential for non-invasive (e.g., saliva) ferritin detection.

Cloud computing is a technology for efficiently using computing infrastructures and a business model for selling computing resources and services. However, intruders find such complex and distributed infrastructures appealing targets for cyber-attacks. Cyber-attacks are severe threats that can jeopardize the quality of service provided to clients and compromise data integrity, confidentiality, and availability. Cyber-attacks are becoming more complex, making it more challenging to detect intrusions effectively. Due to the high traffic and increased malicious activities on the Internet, a single Intrusion Detection System (IDS) can be overwhelmed. Despite the various Deep Learning (DL) approaches that have been proposed as alternative solutions, there are still pertinent security issues to be addressed especially in federated cloud computing domains. This work proposes a Secure Federated Intrusion Detection Model Version 1 (SecFedIDM-V1) using blockchain technology and Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). The Cobourg Intrusion Detection Dataset (CIDDS) was acquired, pre-processed and split into 60:20:20, 70:15:15, and 80:10:10 for training, testing, and validation respectively to develop the proposed intrusion traffic classification component of the proposed model. The developed SecFedIDM-V1 was later deployed as a Python-based web application that captures network packets for classifying attacks into normal or an attack type. The attack packets are recorded in a Hyperledger Fabric (a private blockchain technology) to serve as a signature database to be used by other nodes in the network. From the evaluation results of the intrusion classifier, the 80:10:10 BiLSTM network performed better than GRU with a Precision of 0.99624, Recall of 0.99906, F1 Score of 0.99614, False Positive Rate (FPR) of 0.00094, False Negative Rate (FNR) of 0.00395 and True Positive Rate (TPR) of 0.99605. The SecFedIDM-V1 can be deployed alongside Firewalls in a federated cloud computing environment to reinforce the security of the infrastructure.

Available radio resources in a wireless communication network are normally shared among multiple users. The state during which the number of users admitted into a network exceeds the capacity of the network is known as network congestion. Network congestion causes degradation of Quality of Service (QoS) and Grade of Service (GoS), which results in users' dissatisfaction. Thus, it is of uttermost importance to proactively prevent network congestion. Call Admission Control (CAC) is a radio resource management (RRM) technique that can be employed to prevent network congestion, thereby ensuring the GoS/QoS of admitted calls. In addition to preventing network congestion and ensuring QoS/GoS, it is sometimes necessary to meet other network or users' requirements. This survey outlines different call admission control schemes used to achieve specific objectives for different deployment scenarios.

Micronutrients provide the essential building blocks for brain development, healthy growth and a strong immune system in children. Malnutrition in form of micronutrient deficiencies develop gradually over time and their negative effects are not seen until irreversible damage may have occurred. The World Health Organization (WHO) supports the 2016 Global Nutrition Report (GNR), emphasizing the enormous importance of investing in the critical first 1,000 days nutritional requirement as it directly affects the attainment of 12 out of the 17 United Nations (UN) Sustainable Development Goals (SDGs). Up till now, in many countries, early detection of malnutrition is carried out by specific, majorly blood-based tests in specialized laboratories by trained personnel. This review expatiates on diagnostic trends towards early detection of micronutrient malnutrition highlighting the significant role of Engineering in this cause. Focusing on the children within the 1000-day critical window, suggestions on modalities for continual tracking required to prevent malnutrition using biosensors are also advanced in this review.