Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

The concept of Industry 4.0 means a new paradigm of modern manufacturing. This phenomenon requires continuous innovation processes and technological development from each enterprise. Traditional concepts of quality must absorb changes and prepare themselves for new challenges. The studies linked to successful adaptation to Industry 4.0 focus mostly on technical dimensions and forget the impact of organisational culture. One should, however, remember that quality culture plays a crucial role in the organisational culture of manufacturing enterprises with elements of quality management implemented. Developed quality cultures support the innovation environment, which is why it is necessary for the enterprises to identify the current level of their quality culture and detect significant factors that differentiate individual quality cultures and focus on them. Given this fact, the aim of the paper is to analyse the typical cultures and quality concepts and to detect the factors that differentiate individual quality cultures in Slovakia. We use data from our own survey; dependences were indicated by means of correspondence analysis and the test of proportion. The improvement and assurance of quality, the use of information and the overall effectiveness are significant factors detected by the discriminant analysis. The conclusions of the survey may be used by scientific researchers but especially by manufacturing enterprises interested in quality which are coming to terms with the era of Industry 4.0.

Research background: Enterprise generative AI system-based worker behavior tracking and monitoring, socially responsible organizational practices, employee performance management satisfaction, and human resource management procedures, relationships, and outcomes develop on hiring and objective performance assessment algorithms in terms of human resource management activities, functions, processes, practices, policies, and productivity. Deep reinforcement and machine learning techniques, operational and analytical generative AI and cloud capabilities, and real-time anomalous behavior recognition systems further fintech development for credit and lending services, payment analytics processes, and risk assessment, monitoring, and mitigation. Generative AI tools can bolster predictive analytics by collaborative and interconnected sensor and machine data for tailored, seamless, and fine-tuned product, operational process, and organizational workflow development, efficiency, and innovation, driving agile transformative changes in digital twin industrial metaverse. Purpose of the article: We show that enterprise generative AI-driven schedule prediction tools, job search and algorithmic hiring systems, and synthetic training data can improve team selection, job performance and firing decisions, hiring decision processes, and workforce productivity in terms of prediction and decision-making by use of algorithmic management, system performance, and production process tracking tools. Blockchain-based fintech operations can shape cloud-based financial and digital banking services, quote-to-cash process automation, cash-settled crypto futures, digital loan decisioning, asset tokenization simulated transactions, transaction switching and routing operations, tailored peer-to-peer lending, and proactive credit line management. Collaborative unstructured enterprise data processing, infrastructure, and governance can develop on AI decision and behavior automation technology, retrieval augmented generation and development management systems, and real-time data descriptive and predictive analytics, driving productivity surges and competitive advantage in digital twin industrial metaverse. Methods: Reference and review management tools, together with evidence synthesis screening software, harnessed were Abstrackr, AMSTAR, ASReview Lab, CASP, Catchii, Citationchaser, DistillerSR, JBI SUMARI, Litstream, PICO Portal, and Rayyan. Findings & value added: The current state of the art is improved for theory on organizational issues and for policy making as deep learning-based generative AI tools and workplace monitoring systems can augment performance and productivity, gauge employee effectiveness, build resilient, satisfied, and engaged workforce, assess human capital, skill, and career development, drive employee and productivity expectations in relation to flexibility and stability, and shape turnover, retention, and loyalty. Cloud and account servicing technologies can be deployed in generative AI fintechs for embedded cryptocurrency trading, transaction monitoring and processing, digital asset transfers, payment screening, corporate and retail banking operations, and fraud prevention. Generative AI technologies can reshape jobs and reimagine meaningful work, involving creativity and innovation and adaptable and resilient sustained performance, providing valuable constructive feedback, optimizing workplace flexibility and psychological safety, and measuring and supporting autonomy and flexibility-based efficiency, performance, and productivity, while configuring demanding, engaging, and rewarding experiences by cloud and edge computing devices in digital twin industrial metaverse.

The scientific periodical journal (indexed on Web of Science) published by the Tomas Bata University in Zlin offers results of basic and applied economic research in the English language.

Production of food-grade emulsions is continuously rising globally, especially in developing countries. The steepest demand growth is in the segment of inexpensive meat products where edible emulsions serve as lubricants to mitigate economic loses linked with mechanical damage during automated processing of artificial casings. Provided that production goal is to minimize emulsion transfer into the product, its vast majority becomes voluminous greasy and sticky waste. Public sewage treatment plants cannot process such waste, its cleaning processes tends to collapse under loads of emulsions. To make matters worse, composition of emulsions often changes (according to actual pricing of main components) and emulsion manufacturers carefully guard their recipes. Therefore, running of in-house sewage plants would require continuous experimentation linked with need for skilled personnel, frequent changes in technology setup and high operating costs in general. Consequently, it was repeatedly and independently reported that emulsion waste is poured onto wildlife, resulting in environmental damage and an intense rotting odor. Three new methods of emulsion breakdown are proposed and techno-economically assessed. High versatility of methods was confirmed and multiple austerity measures were incorporated. Emulsions are also assessed in terms of an energy source for aerobic and anaerobic microorganisms. It is reported that the addition of edible emulsion to compost does not result in increased product quality or cost reduction. It is firstly revealed that edible emulsions can instantly create an anaerobic environment and accelerate biogas production through the formation of surface films on feedstock surface. Adding waste food-grade emulsions to the biogas plant makes it possible to 100% reduce process water consumption in biogas stations as the process speed can be shortened by approximately 12%.

Research background: Project portfolio optimization isa demanding process in the case of considering a large number of project intentions and has so far been the subject of research by many authors, especially foreign authors. However, the issue of project portfolio optimization is an area that is not sufficiently addressed by Slovak authors. This was the main impulse to create a specific mathematical model of integer programming with bivalent variables to optimize the company's project portfolio with the intention to reflect the specific requirements of Slovak companies. Purpose of the article: The aim of the article is to propose a mathematical model of integer programming with bivalent variables to optimize the project portfolio with a focus on Slovak companies. Methods: In accordance with the aim of the article, a questionnaire survey was carried out with the intention of identifying the criteria that are perceived by the managers of Slovak companies as important in the optimization of the project portfolio. These criteria were subsequently reflected in the mathematical model design using the mathematical programming method. Findings & Value added: Based on a literature review aimed at the project portfolio optimization, we have found a gap in considering the compliance of project intentions and strategic objectives of the company within the optimization of the project portfolio. Based on the results of the questionnaire survey, the significance of the mutual compliance of project intentions with the strategic objectives of the company was confirmed from the point of view of Slovak companies. Given the fact that our aim was to create an innovative integer programming model with bivalent variables orientated to the conditions of Slovak companies, we included in the resulting model the criteria that were not considered within the scope of existing research in this area, and which are perceived as important by the Slovak companies.