Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.

The year 2020 will remain in the memory of all mankind as the year of the COVID-19 pandemic. The pandemic has affected the world economy, caused more than a million deaths in 2020 alone, changed the way people work and learn and, last but not least, changed the way people interact in society. Social distancing, along with wearing a face mask, is the best solution to prevent the spread of COVID-19 virus. What happens when in certain spaces social distance is difficult and can be accidentally violated without intention? In the absence of widely available tests, not only for people but also for surfaces or indoor air, the risk of contamination is very difficult to assess. This paper proposes a simple method, with low cost and widely implementable, to warn of respecting the density of people in a room. It proposes a dual monitoring system based on Internet of Things (IoT) to assess the indoor air quality and risk of COVID-19 transmission.

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.

Moving to the cloud is a topic that tends to be present in all enterprises that have digitalized their activities. This includes the need to work with software environments specific to various business domains, accessed as services supported by various cloud providers. Besides provisioning, other important issues to be considered for cloud services are complexity and performance. This paper evaluates the processes to be followed for the deployment of such a software environment in the cloud and compares the manual and automated methods in terms of complexity. We consider several metrics that address multiple concerns: the multitude of independent paths, the capability to distinguish small changes in the process structure, plus the complexity of the human tasks, for which specific metrics are proposed. We thus show that the manual deployment process is from two to seven times more complex than the automatic one, depending on the metrics applied. This proves the importance of automation for making such a service more accessible to enterprises, regardless of their level of technical know-how in cloud computing. In addition, the performance is tested for an example of an environment and the possibilities to extend to multicloud are discussed.

The paper presents an integrated information system for monitoring current state of natural resources and providing valuable help by assisting decisions makers in critical situations. The specific case studied is from the domain of water resources management but the architecture is easily adaptable to suit other types of natural resources (e.g. soil, air). The key points of the proposed solutions are standardization, web services and cloud computing.