Global population growth, climate change, and food safety are some of the challenges that the agri-food sector is current facing. Addressing these in a sustainable way requires the adoption of sustainable-oriented innovations capable of developing new knowledge and reshaping production methods and business models. Digitalization and technological evolution in the agri-food sector, culminating within Agriculture 4.0 (A4.0), represent the latest revolution, as testified by the extant body of knowledge. The lack of a systematic model to map the body of knowledge in A4.0 and how it addresses the agri-food sustainability challenges represents the input of our review. We propose a systematic literature review to discover the research routes in A4.0, increasing the understanding of this paradigm as agri-food sustainability enabler. A taxonomy, composed of 566 terms grouped in seven categories representative of the extant research routes, is proposed: the names assumed by agri-food digitalization process, the main aims and issues of A4.0, the features of the agricultural sector, the parameters monitorable and calculable through A4.0, data, and technologies. It emerged that A4.0 absolve the role of enabler in sustainability agri-food providing to the company a set of data and technological sustainable oriented innovations able to solve, according to an interdisciplinary approach, environmental, social, and economic challenges. The findings identify research gaps and theoretical and practical implications for a wide range of stakeholders, such as engineering managers, agri-food companies, researchers, or companies involved in the web content analysis. Limitations and future research route complete the study.

The digital twin is currently recognized as a key technology allowing the digital representation of a real-world system. In smart manufacturing, the digital twin enables the management and analysis of physical and digital processes, products, and people in order to foster the sustainability of their lifecycles. Although past research addressed this topic, fragmented studies, a lack of a holistic view, and a lack of in-depth knowledge about digital twin concepts and structures are still evident in the domain of the shop floor digital twin. Manufacturing companies need an integrated reference framework that fits the main components of both physical and digital space. On the basis of a systematic literature review, this research aims to investigate the characteristics of the digital twin for shop floor purposes in the context of smart manufacturing. The “hexadimensional shop floor digital twin” (HexaSFDT) is proposed as a comprehensive framework that integrates all the main components and describes their relationships. In this way, manufacturing organizations can rely on an inclusive framework for supporting their journey in understanding the shop floor digital twin from a methodological and technological viewpoint. Furthermore, the research strengthens the reference literature by collecting and integrating relevant contributions in a unique framework.

Digital Twin (DT) technology monitors, simulates, optimizes, models, and predicts the behavior of physical entities. Healthcare is a significant domain where a DT can be functional for multiple purposes. However, these diverse uses of DTs need a clear understanding of both general and specific aspects that can affect their adoption and integration. This paper is a meta-review that leads to the development of a conceptual framework designed to support the high-level evaluation of DTs in healthcare. Using the PRISMA methodology, the meta-review synthesizes insights from 20 selected reviews out of 1,075 studies. Based on this comprehensive analysis, we extract the functional, technological, and operational aspects that characterize DTs in healthcare. Additionally, we examine the structural (e.g., hierarchical) relationships among these aspects to address the various complexity scales in digital health. The resulting framework can promote the effective design and implementation of DTs, offering a structured approach for their assessment.

Several research studies discuss the process of agriculture digitalization proposing technologies able to face the current agri-food challenges. However, the extant bibliometric studies do not fully exploit the complementarity of different modern bibliometric tools, such as performance analysis and science mapping and do not consider the entire research field timespan. Therefore, the aim of this study is to complement and update the previous works and provide a broad quantitative and qualitative view of Agriculture 4.0 research by using synergistically performance analysis and science mapping. The analysis was realized on a sample of 2334 papers adopting statistical frequency analysis and VOSviewer. Performance analysis provided key findings about the following indicators: research subject areas, publications trend, most productive journals, document types, authors productivity, authors' and index keywords, most cited papers, most productive and influent institutions, country map collaboration, the documents by funding sponsor. Science mapping provided key findings about the main thematic research field’ clusters and their evolution overtime: technology application in agricultural industry, data model for analysis and prediction, experimentation and applicative studies of smart agriculture, decision support systems for crop monitoring. This study could benefit: food companies, supporting in technologies identification; governments, suggesting policies to stimulate agricultural digitalization process; academics, incentivizing more consciousness about the topic and research agenda; those who approach this topic for the first time, facilitating bibliographical referencing.

An ever-growing number of companies are moving toward the Industry 4.0 paradigm, adopting a range of advanced technologies (e.g., smart sensors, big data analytics, and cloud computing) and networking their manufacturing systems. This improves the efficiency and effectiveness of operations but also introduces new cybersecurity challenges. In this article, the impact assessment methodology is applied in the context of manufacturing systems 4.0 (also known as smart manufacturing systems, cyber manufacturing systems, or digital manufacturing systems), thus identifying the critical assets to be protected against cyber-attacks and assessing the business impacts in the case of subtractive and additive technologies. The research design of the single case study with multiple units of analysis is applied. In particular, a large company, a leader in the manufacturing of aeronautical components, is considered a representative case study, and its two main types of manufacturing cells that is, those based on networked computer numerical control machines and 3-D printers, are taken as applicative cases for the methodology. The application of the impact assessment methodology in the manufacturing context 4.0 of aeronautical components represents a useful guide for researchers in the field of cybersecurity and for companies intending to implement it in their smart manufacturing environments. In particular, based on this study, companies can define the critical manufacturing data to protect against cyber-attacks, isolate the business impacts in case of cybersecurity breaches, correlate the identified business impacts with the specific data category, and assess the level of business impacts.