This paper examines a new approach to defining digital ecosystems. Within the digital economy of ecosystems, competition is eliminated, and organizations form unions and alliances in order to work together and cooperate to reach a set goal. This means a digital ecosystem can be viewed as a complex environment in which organizations without any hard ties operate. Digital ecosystems differ from traditional ecosystems in many ways. The business organization of the latter is based on management decision making by people. This paper presents theoretical foundations for developing digital ecosystems based on a literary review. Based on the logic of scientific search using the keywords “ecosystem” and “biological ecosystem”, the commonality of the properties of the digital ecosystem and the biological ecosystem is shown. The aim of the study is to identify common characteristics in biological, economic and digital ecosystems in order to substantiate the possibility of using the same approaches for research and modeling of such systems. A definition of a digital ecosystem is proposed by the authors which points out the main features of this kind of system and highlights the dominant role of modern digital technologies in the formation of the digital ecosystem. The paper looks at the distinctive features of digital ecosystems and characteristics similar to the characteristics of biological ecosystems, such as ecosystem participants, presence of limiting impacts, lack of vertical hierarchical communication. The developed model can be used to model digital ecosystems. The authors believe that the emergence of a trend in the transformation of ecosystems in the direction of expanding the collaboration of economic agents is reasonable. At the same time, digitalization helps to replace competition with collaboration. The paper finishes with a discussion of the obtained results and a plan for further research.

Energy costs are the key factors regarding the selection of appropriate renewable energy (RWG) alternatives. All costs of a power plant, such as investment, operation, maintenance, and repair are considered in the scope of levelized costs. Therefore, for the effective determination of the selling price of the energy, levelized cost has a guiding role. Because the levelized costs of RWG alternatives develop the sustainable production and energy consumption for the long term, the leading indicators of these costs should be analyzed significantly. Accordingly, in this study, it is aimed to investigate the levelized cost of RWG alternatives by using bipolar q-rung orthopair fuzzy (q-ROF) hybrid decision-making approach. The novelty of this study is to recommend an integrated decision-making model based on bipolar and q-ROFSs with golden cut. At the first stage, bipolar q-ROF multi stepwise weight assessment ratio analysis (M-SWARA) is employed for weighting the selected criteria of levelized costs of RWG alternatives. At the following stage, bipolar q-ROF technique for order preference by similarity to ideal solution (TOPSIS) is considered to rank the alternatives in terms of the levelized cost performance. On the other side, vise kriterijumska optimizacija i kompromisno resenje (VIKOR) model is also considered to rank the alternatives. In addition to this issue, the sensitivity analysis is also performed with four cases comparatively. Hence, consistency, reliability and coherency of the proposed model can be measured. It is identified that capacity loss has the greatest importance regarding the levelized cost of RWG projects. Solar is found as the best clean energy type with respect to the levelized cost management performance. In this context, it would be appropriate for investors to design projects close to the center. This will contribute to increasing the efficiency and productivity of these projects.

There is currently a discussion going on in the scientific community about using digital twins and modeling to manage risks in the supply chains. This need for constructing digital twins is caused by the low reliability and stability of supply chains due to the faults in their operation. These faults are a result of risks in the supply chains which can be consolidated into two types. The first type is operational risks. These are the current risks of the supply chain itself caused by an uncertainty of supply and demand as well as by an obstructed flow of information along the supply chain. The second type is critical risks caused by force majeure. These risks disrupt the normal operation of the supply chain and critically reduce the most important performance indicators of the company such as annual income and profits. Risks happen due to natural or man-made causes such as fires and floods in the distribution centers or at production facilities, legal disputes with suppliers, strikes, terrorist attacks on logistics facilities and others. Dynamic simulation and analytical optimization are two dominant technologies for managing risks of the supply chains, which helps to increase their reliability and stability if failures occur. Through optimizing and simulating of the supply chains, companies can generate new infor-mation about the impact of failure and influence the supply chain and its performance by looking at various scenarios that simulate the locations of failures, the duration and recovery policies. An analysis of the literary sources shows that there is no single approach to build the concept for a supply chain digital twin. This article gives an overview of the literature according to this problem and offers the author's point of view on the concept for a supply chain digital twin.

The hybrid workplace is a concept on the lips of every industry trend in the world today. With digitalization becoming more normalized across every sphere in the global village. Every workplace needs to maximize and transcend obstacles and innovations to ease into the hybrid workplace. The COVID-19 pandemic brought a wave for an increased need for a hybrid workplace. Although some countries have relaxed the lockdown in their states, businesses are taking their time to set up a more formidable work arrangement. Many are already operating the hybrid system while others are running fully remote. The pandemic has taught the work a lesson of preparation and planning. Beyond that is also the lesson of flexibility and adaptability in the workplace. In prioritizing the future of work, there is the need to embrace the hybrid workplace model. Indeed, the future of work would likely be the hybrid workplace model.

This paper proposes a novel and secure blockchain hyperledger sawtooth-enabled consortium analytical model for smart educational accreditation credential evaluation. Indeed, candidate academic credentials are generated, verified, and validated by the universities and transmitted to the Higher Education Department (HED). The objective is to enable the procedure of credential verification and analyze tamper-proof forged records before validation. For this reason, we designed and created an accreditation analytical model to investigate individual collected credentials from universities and examine candidates’ records of credibility using machine learning techniques and maintain all these aspects of analysis and addresses in the distributed storage with a secure hash-encryption (SHA-256) blockchain consortium network, which runs on a peer-to-peer (P2P) structure. In this proposed analytical model, we deployed a blockchain distributed mechanism to investigate the examiner and analyst processes of accreditation credential protection and storage criteria, which are referred to as chaincodes or smart contracts. These chaincodes automate the distributed credential schedule, generation, verification, validation, and monitoring of the overall model nodes’ transactions. The chaincodes include candidate registration with the associated university (candidateReg()), certificate-related accreditation credentials update (CIssuanceTrans()), and every node’s transactions preservation in the immutable storage (ULedgerAV()) for further investigations. This model simulates the educational benchmark dataset. The result shows the merit of our model. Through extensive simulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems.

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.