Electrochemical biosensors include a recognition component and an electronic transducer, which detect the body fluids with a high degree of accuracy. More importantly, they generate timely readings of the related physiological parameters, and they are suitable for integration into portable, wearable and implantable devices that are significant relative to point-of-care diagnostics scenarios. As an example, the personal glucose meter fundamentally improves the management of diabetes in the comfort of the patients’ homes. This review paper analyzes the principles of electrochemical biosensing and the structural features of electrochemical biosensors relative to the implementation of health monitoring and disease diagnostics strategies. The analysis particularly considers the integration of the biosensors into wearable, portable, and implantable systems. The fundamental aim of this paper is to present and critically evaluate the identified significant developments in the scope of electrochemical biosensing for preventive and customized point-of-care diagnostic devices. The paper also approaches the most important engineering challenges that should be addressed in order to improve the sensing accuracy, and enable multiplexing and one-step processes, which mediate the integration of electrochemical biosensing devices into digital healthcare scenarios.

The theoretical and practical progress that has occurred in the field of computer networks during the past fifteen years has enhanced the economical efficiency and social relevance of related real-world use cases. Nevertheless, this ubiquitous usage has also introduced numerous security risks. Therefore, monitoring hardware and software resources represents one of the main instruments used in order to prevent potential attacks and to ensure the security and reliability of a network. Various solutions have been reported in the related scientific literature. In essence, most of the existing approaches are not suitable to implement a real-time hardware monitoring and management solution, particularly in heterogeneous networks. Therefore, the main contribution of this paper is represented by an architectural and implementational model, which is effective in order to build an interconnected system that can help system and network administrators to secure a network. This requirement is met by considering symmetrical design and implementation features related to various operating systems. Thus, the existing symmetrical relationships among identified parameters allow for the data to be wrapped into the same custom network packages, which are transported over the communication medium or are stored using the same data structures or tables. The system has been thoroughly assessed considering several real-world use case scenarios, and the results demonstrate that the proposed model can be applied to software-defined networks, which can be protected by relevant intrusion detection systems (IDS).

The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.

Research on quantum computers has advanced significantly in recent years. If humanity ever creates an effective quantum computer, many of the present public key cryptosystems can be compromised. These cryptosystems are currently found in many commercial products. We have devised solutions that seem to protect us from quantum attacks, but they are unsafe and inefficient for use in everyday life. In the paper, hash-based digital signature techniques are analyzed. A Merkle-tree-based digital signature is assessed. Using a Verkle tree and vector commitments, the paper explores novel ideas. The authors of this article present a unique technology for developing a post-quantum digital signature system using state-of-the-art Verkle tree technology. A Verkle tree, vector commitments, and vector commitments based on lattices for post-quantum features are used for this purpose. The concepts of post-quantum signature design utilizing a Verkle tree are also provided in the paper.