The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

This article deals with compression of binary sequences with a given number of ones, which can also be considered as a list of indexes of a given length. The first part of the article shows that the entropy H of random n-element binary sequences with exactly k elements equal one satisfies the inequalities $k{log}_2(0.48·n/k)<H<k{log}_2(2.72·n/k)$. Based on this result, we propose a simple coding using fixed length words. Its main application is the compression of random binary sequences with a large disproportion between the number of zeros and the number of ones. Importantly, the proposed solution allows for a much faster decompression compared with the Golomb-Rice coding with a relatively small decrease in the efficiency of compression. The proposed algorithm can be particularly useful for database applications for which the speed of decompression is much more important than the degree of index list compression.

Remanufacturing processes are one of the key pillars of the so-called closed-cycle economy. Unfortunately, the design and organization of such processes pose a significant problem, due to, among other things, the high variability of the process as a consequence of both the degree of wear and tear and the quality of the remanufactured components. This article presents a method for optimizing the flow of the brake caliper remanufacturing process in the MTO (make-to-order) model. For the optimization process, the system dynamics (SD) method, which belongs to a group of simulation methods, was used. The developed model allows, in particular, the prediction of the number of components at the input to the regeneration process and the method of defining the size of the transport batch and evaluating its impact on the length of the production cycle of the batch of regenerated products. The issues of staffing management at individual positions and the organization of transport operations with variable production batch sizes were also discussed. The provided experiments have shown that reducing the size of the transport batch contributes to shortening the production order execution time, both with standard and increased staffing.