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Considering the lack of the scientific studies on the selected topic, the authors of this article raise the aim to set up the definition of digital shadow economy and identify its distinctive features and channels. Thus far, the studies on illegal digital activities have covered ambiguous inter­pretations of digital shadow economy that incorporated both criminal and economic aspects of the activities performed. The results of the empirical research have enabled to formulate the definition of digital shadow economy that refers to illegal activities, such as digital service provision and sales of goods/services online, when operating exceptionally in digital space, the entities violate the existent legal norms and regulations with a pursuit of illegal mutual interest and material benefits. The newly formulated definition of digital shadow economy has served as a corner-stone for identification of the distinctive features and channels of this phenomenon. Hence, the results of the research may make a significant and weighty contribution to the development of the theory of economics and may raise the awareness of what the phenomenon of digital shadow economy implies. First published online: 09 Jan 2017

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

In today’s industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events.

Increasing volumes of e-trade contribute to motivation of consumers to obtain commodities and services in electronic space.At the same time, upsurge of e-trade determines rising scopes of shadow economy in respect of favourable conditions for traders and service providers to operate in e-space evading tax paying. The purpose of the article is to identify the factorsof digital shadow consumption. In order to fulfil the defined purpose, the empirical research – survey of consumers (e-trade participants) – was performed. The research of the scientific literature has revealed that thus far the problem of consumers’participation in digital shadow economy has been basically analysed focusing on the impact of e-payment systems on shadow economy. Nevertheless, the rapid spread of e-services determines the changes in the concept of shadow economy itself. It remains indistinct which features indicate whether economic activities performed in e-space should be accounted or not. Widely exploited e-spaces such as social network platforms, alternative future currencies, e-trade systems, cyber computer games or online gambling terminals generate turnover of real money (or its electronic equivalent), which is not officially accounted.The problem raised in this article is highly topical for Lithuania, where online networks as well as mobile connection systems are comparatively advanced (with reference to the data of Lithuanian Department of Statistics, the number of households possessing a computer and the Internet access made over 65% in 2013). Intense exploitation of advanced IT technologies and online networks is considered as a breeding ground for generation of digital economy, a part of which is presumed to be digital shadow. The results of the research have revealed that the most significant factors of digital shadow consumption include lower prices of products and services in digital black markets, unfavourable economic situation in the country, technological advancement, IT advantages, time saving obtaining a product/service in the local market and lack of opportunities to obtain a desired product in the local market. The majority of the consumers neither verify the status of a trader nor request (or not always request) purchase confirmation documents, which highly contributes to motivation of an illegal trader to maintain e-activities unregistered, this way escaping revenue taxation.

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

With the rapid growth of today’s market, food processing industries are compelled to adopt advanced solutions to ensure product safety and quality, reduce costs amidst low-profit margins, guarantee timely delivery of an increased demand for product preferences, and enhance sustainability. To cope with these challenges, Digital Twin (DT) technology has emerged as a promising solution. Due to the relative novelty of this technology, research on its application within the food processing industry remains limited, necessitating additional studies to better understand its potential and multifaceted role from different perspectives. This review analyzes existing studies on DT applications in the food industry, discussing the concept, benefits, and challenges hindering its full adoption. On this basis, this study contributes to the existing body of knowledge on DT by identifying the state-of-the-art within the food processing industry and categorizing use cases from both industrial and academic contexts. Further, it explores the DT from a sustainability perspective, emphasizing its role in optimizing resource utilization for more efficient and sustainable food processing. Furthermore, the study examines its potential as a versatile tool to support sustainability initiatives within the industry. Lastly, it discusses the future impact of DT in shaping the evolution of food processing.

In the dynamic landscape of modern manufacturing, the pursuit of efficiency, reliability, and optimal performance has prompted the integration of cutting-edge technologies. Among these, digital twins (DT) have emerged as transformative tools, offering a virtual representation of physical processes, systems, and equipment. This paper delves into the pivotal role of digital twins in advancing the monitoring and supervision of manufacturing processes, focusing specifically on process digital twin (PDT) and its application in the domain of froth flotation in minerals processing. Our data-driven digital twin, replicating the behavior of a flotation cell, was developed using a combination of industrial and simulation data anchored by Artificial Neural Networks. This approach provides precise process emulation of the flotation process. Industrial evaluations of the AI model within the Digital Shadow demonstrated an overall 94% accuracy in estimating insightful information regarding the flotation cell operations with 2 s in response time. This research significantly contributes to the practical implementation of digital twins in industrial processes, highlighting their potential to revolutionize process control and enhance efficiency in the industrial sector.

Digital twin (DT) is a research topic that gained momentum in the Industry 4.0 era. The goal of DT is to create a virtual real-time intelligent system that is a typical twin of the physical system. DT provides analysis, prognosis, planning, and rapid response when needed. Digital shadow (DS) is an artifact concept of DT that provides a real-time replica of the physical system. Information in a DS is passed in one direction only, from the physical system to the virtual one. While in DT, the information goes in both directions. The definition and roles of DS and DT are overlapping. However, DS can be defined as the main component of a DT system. In this paper, DS roles are specified. Based on these roles, A DS framework architecture is proposed, and the communication system between its components. The proposed framework design is built using distributed system concepts such as event-driven architecture, microservices, and containerization. These concepts are well defined and utilized in the software engineering domain. The originality of the proposed framework is the definition of a systematic approach for designing and integrating digital models (DMs) from different vendors and domains. An experiment is designed to prove the framework’s ability to shadow a physical system in real-time. Multiple DMs are implemented and deployed on the proposed DS framework. These DMs are used to shadow a natural gas compressor system. Experimental results prove the practicality of our proposed DS framework to operate in real-time.

Digital twinning in structural engineering is a rapidly evolving technology that aims to eliminate the gap between physical systems and their digital models through real-time sensing, visualization, and control techniques. Although Digital Twins can offer dynamic insights into physical systems, their accuracy is inevitably compromised by uncertainties in sensing, modeling, simulation, and control. This paper proposes a specialized Digital Twin formulation, named Risk Twin, designed for real-time risk visualization and risk-informed control of structural systems. Integrating structural reliability and Bayesian inference methods with Digital Twinning techniques, Risk Twin can analyze and visualize the reliability indices for structural components in real time. To facilitate real-time inference and reliability updating, a simulation-free scheme is proposed. This scheme leverages precomputed quantities prepared during an offline phase for rapid inference in the online phase. Proof-of-concept numerical and real-world Risk Twins are constructed to showcase the proposed concepts.