Land administration systems are of great importance for a large number of stakeholders. One of the key problems related to land administration systems is the problem of the correctness of their state, meaning that data stored in land administration systems are not in concordance with the actual legal, spatial and topographic situation. The main causes of land administration systems’ incorrect state are data collection and compilation, data processing, and data misuse. In this paper, we discuss the problems of data tampering, the long time needed for registering land administration system’s transactions, and the possibility of double spending, which all can add incorrectness in a land administration system. Our research is based on the hypothesis that these problems may be addressed by means of distributed ledger technology, or to be more precise, by means of blockchain technology. The solution is presented in a form of a smart contract written in Solidity programing language that can cover even those more specific use cases in land administration systems such as sharing of ownership, transferring part of ownership, splitting or merging of real estate, and limiting the possibility of trading a real estate. The proposed smart contract represents an implementation of a programming interface that was created based on both ERC-20 and ERC-721 token standards, to satisfy the specific needs of land administration systems.

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan’s modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring.

During the last couple of years, non-fungible tokens became the most prominent implementation of blockchain technology apart from cryptocurrencies. This is mainly due to their recent association with digital art, but the application of non-fungible tokens has been in the focus of researchers since the appearance of Blockchain 2.0. It was usually tightly coupled with the research on possible applications of blockchain technology in some real-life applications, such as land administration, healthcare, or supply chain management. Since the initial release of the Ethereum blockchain in 2015, until 2022, more than 44 million smart contracts have been created, and out of those that are still active, more than 70% are based on some prominent templates. In the Ethereum blockchain, the creation of nonfungible tokens is usually based on Ethereum Request for Comments 721. In this paper, the authors are proposing the creation of a new standard that would support fractional ownership of non-fungible tokens. Fractional ownership is necessary so non-fungible tokens and blockchain technology could be applied to an even wider number of use cases. This paper also presents an example of a possible implementation of the newly proposed standard in the Solidity programming language.

Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.