Industrial production is currently experiencing a revolutionary transformation through digitization processes and networked technology, so that it necessarily goes through a series of essential changes, which require conceptual design and creation of new terms. In the field of social organization of work, the processes of management and control are experiencing radical changes, and many novelties that bring technological innovations provide the opportunity to analytically observe the interaction relationships of different systems, without losing individuals, organizations and society in the context. Given that digital transformation is not a uniform process, the paper presents the advantages of a holistic approach in the analysis of organizational changes. New ways of organizing work bring various kinds of challenges that must be understood in order to be able to detect the social mechanisms that are at the very basis of change. The paper points out the specific social dimensions of technology that appear during the creation of organizational processes within the framework of the Fourth Industrial Revolution. The very term ‘industrial revolution’ is understood in the paper in a broader sense, and includes changes in social relations and the status of certain social groups, and not only changes in the production process and factors of production. The paper explains the status changes in power that are connected with the possibility of making business decisions in various forms of organizational practices, showing the increasingly pronounced complexity of interactional relationships between people and technology, which points to the necessity of interdisciplinary observation and finding a holistic approach to understanding the nature of the changes that are taking place.

Business operations of companies in modern conditions are subject to enormous market, social and especially technological pressures from the environment. Information and communication technologies have become so incorporated both in our everyday life and in the operations of every company, that without them we feel almost lost and helpless. Big Data, as a theoretical (philosophical) concept has existed for decades, but only recently, thanks to the extraordinarily rapid development of information and communication technologies, it has become applicable in practice, and as a business concept it has been recognized as a unique opportunity for success in the business world. Like all organizations, small and medium-sized enterprises can find a unique opportunity to improve their own business in the application of this concept. The number of users is growing exponentially, generating a huge amount of different data every second through different sources (YouTube, Twitter, Instagram, Facebook, Google, Skype, Internet, E-mail). All those unimaginably large amounts of data need to be stored somewhere: processed, analyzed, presented and interpreted, and then propose (suggest) specific business solutions based on those results. Realization of those activities in real or reasonable time, and often unexpected and surprising conclusions, are made possible by the Big Data concept. This article aims to shed light on the concept and technology of Big Data and its application at the level of small and medium enterprises. Big Data is a theoretical and technological concept, which is able to revolutionize the way of decision-making in companies and achieve extraordinary and concrete results. A secondary, but no less important, goal of writing this paper is to point out the importance of small and medium-sized enterprises, which outnumber the large ones. Most of them strive for a stable, dominant and high market position, so it can be concluded that they are extremely important for development and progress of each country.

Purpose Lean Management (LM) is a process improvement approach with growing interest from healthcare organizations. Obtaining a culture of continuous improvement is a primary objective of LM, and a culture of continuous improvement indicates a mature LM approach, and here leadership plays a central role. However, a comprehensive overview of leadership activities influencing LM maturity is lacking. This study aims to identify leadership activities associated with continuous improvement and, thus, LM, maturity. Methods Following the PRISMA guidelines, a scoping literature review of peer-reviewed articles was conducted in twenty healthcare management journals. The search provided 466 articles published up until 2023. During the selection process, 23 studies were included in the review. The leadership activities related to continuous improvement maturity were identified using the grounded theory approach and data coding. Results The analysis highlighted a total of 58 leadership activities distributed across nine themes of LM leadership. Next, analysing leadership activities concerning the different maturity levels revealed three maturity stages: beginner, intermediate, and expert. Based on the findings, we propose a framework that guides suitable leadership activities at the various stages of LM maturity. The framework provides leaders in healthcare with a practical overview of actions to facilitate the growth of the LM approach, and the related propositions offer academics a theoretical basis for future studies. Conclusion This review presents the first comprehensive overview of LM leadership activities in relation to continuous improvement and LM maturity. To enhance LM maturity, leaders are encouraged to consider their leadership style, (clinical) stakeholder involvement, alignment with the organizational strategy, and their role in promoting employee autonomy.

. Screening for lymphedema and accurate quantitative assessment of dermal backflow patterns on ICG represents a major shift in current clinical practice paradigms, putting an emphasis on early detection of lymphedema rather than palliative treatments and symptomatic relief. These findings set the stage for the development of a practical, universal, ICG-based quantification system for the staging of lymphedema, a significant advancement in the field of plastic surgery.

The material extrusion fused deposition modeling (FDM) technique has become a widely used technique that enables the production of complex parts for various applications. To overcome limitations of PLA material such as low impact toughness, commercially available materials such as UltiMaker Tough PLA were produced to improve the parent PLA material that can be widely applied in many engineering applications. In this study, 3D-printed parts (test specimens) considering six different printing parameters (i.e., layer height, wall thickness, infill density, build plate temperature, printing speed, and printing temperature) are experimentally investigated to understand their impact on the mechanical properties of Tough PLA material. Three different standardized tests of tensile, flexural, and compressive properties were conducted to determine the maximum force and Young’s modulus. These six properties were used as responses in a design of experiment, definitive screening design (DSD), to build six regression models. Analysis of variance (ANOVA) is performed to evaluate the effects of each of the six printing parameters on Tough PLA mechanical properties. It is shown that all regression models are statistically significant (p<0.05) with high values of adjusted and predicted R2. Conducted confirmation tests resulted in low relative errors between experimental and predicted data, indicating that the developed models are adequately accurate and reliable for the prediction of tensile, flexural, and compressive properties of Tough PLA material.

This paper highlights the growing importance of edge computing and the need for AI techniques to enable intelligent processing at the edge. Edge computing has emerged as a paradigm shift that brings data processing and storage closer to the source, minimizing the need for transmitting large volumes of data to remote locations. The integration of AI capabilities at the edge enables intelligent and real-time decisionmaking on resource-constrained devices. This paper discusses the significance of Edge AI across various domains, including automotive applications, smart homes, industrial IoT, and healthcare. By leveraging AI algorithms on edge devices, efficient implementation and deployment become possible, leading to improved latency, privacy, and security.The various AI techniques used in edge computing are presented, including machine learning, deep learning, reinforcement learning and transfer learning. As AI continues to play a pivotal role in driving edge computing, the integration of hardware accelerators and software platforms is gaining utmost significance to efficiently run inference models. A variety of popular options have emerged to accelerate AI at the edge, and notable among them are NVIDIA Jetson, Intel Movidius Myriad X, and Google Coral Edge TPU. The importance of specialized System-on-a-Chip (SoC) solutions for Edge AI, capable of supporting high-performance video, voice, and vision processing alongside integrated AI accelerators is presented as well. By examining the transformative potential of Edge AI, this paper aims to inspire researchers, practitioners, and industry professionals to explore the vast possibilities of integrating AI at the edge. With Edge AI reshaping the future of edge computing, intelligent decision-making becomes seamlessly integrated into our daily lives, driving advancements across various sectors.

Although many women perform postural tasks while listening to music, no study has investigated whether preferred music has different effects than non-preferred music. Thus, this study aimed to explore the effects of listening to preferred versus non-preferred music on postural balance among middle-aged women. Twenty-four women aged between 50 and 55 years were recruited for this study. To assess their static balance, a stabilometric platform was used, recording the mean center of pressure velocity (CoPVm), whereas the timed up and go test (TUGT) was used to assess their dynamic balance. The results showed that listening to their preferred music significantly decreased their CoPVm values (in the firm-surface/eyes-open (EO) condition: (p < 0.05; 95% CI [−0.01, 2.17])). In contrast, when the women were listening to non-preferred music, their CoPVm values significantly (p < 0.05) increased compared to the no-music condition in all the postural conditions except for the firm-surface/EO condition. In conclusion, listening to music has unique effects on postural performance, and these effects depend on the genre of music. Listening to preferred music improved both static and dynamic balance in middle-aged women, whereas listening to non-preferred music negatively affected these performances, even in challenged postural conditions.