【Abstract】Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids inte -grate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.
【Abstract】Global competition encourages all industries to attain cutting-edge performance by continuously developing their goods and processes; knowledge is the most effective and powerful weapon for long-term sustainabil-ity and growth. Effective blockchain adoption (BCA) and better supply chain visibility (SCV) via organiza-tional and production knowledge management (KM) have emerged as the most powerful instruments for improving sustainable organizational performance (SOP). Therefore, drawing on the resource-based view and technology acceptance model, this study seeks to underline the empirical relationships among KM, BCA, SCV, and SOP in a developing country context of Chinese manufacturing, as research in this sector is still nascent encompassing these constructs. Data were collected from 289 respondents (senior, middle, and junior) level staff members from manufacturing industries and analyzed by a novel approach; partial least square structural equation modeling (PLS-SEM). The empirical analyses indicated that KM significantly impacts BCA. BCA also positively affects SCV. Besides BCA, the KM also positively impacts SOP. The mediation effect analysis revealed the significant serial mediating impact of BCA and SCV on the relationship of KM to SOP. This study enriches the inadequate literature and throws light on BCA from an organizational resource perspective. The study deepens our understanding and delivers valued insights to the managers and policy -makers of manufacturing industries concerning the role of KM, BCA, and SCV in achieving SOP. (c) 2022 The Authors. Published by Elsevier Espana, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
【Abstract】Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone's battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones' services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones' battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm's efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.
【Abstract】In this article, we study a social learning model in which agents iteratively update their beliefs about the true state of the world using private signals and the beliefs of other agents in a non-Bayesian manner. Some agents are stubborn, meaning they attempt to convince others of an erroneous true state (modeling fake news). We show that while agents learn the true state on short timescales, they "forget" it and believe the erroneous state to be true on longer timescales. Using these results, we devise strategies for seeding stubborn agents so as to disrupt learning, which outperforms intuitive heuristics and gives novel insights regarding vulnerabilities in social learning.
【Abstract】Purpose The paper aims to provide managerial recommendations for implementing circular economy (CE) principles in both organizational and interorganizational contexts, including when using digital tools, such as building information modeling (BIM) and blockchain. Drawn from the construction sector in the Netherlands, the findings can be generalized to similar sectors where a company may receive multiple inputs as part of its supply chain augmented by digital technologies. Design/methodology/approach Design addresses the research question: what are the strategic and tactical approaches of organizations on the CE pathway? Sub-questions target initiatives pursued by participants, and look toward information, roles and functions for supporting the CE process. Applying a multiple-case study approach (21 semi-structured interviews with 29 participants) the paper explores strategic initiatives of construction companies implementing CE pathways. The strength of the research design comes from facilitation of rich and deep qualitative insights from Netherlands-based managers embedded within global supply chains contributing to conceptual mapping. A limitation is data from one country (though representing both national and multinational companies). Findings Interviewed managers share guidance for production-related construction companies anchored in materials and product design. Recommendations include to (1) develop both internally and externally the awareness of CE amongst leaders, (2) communicate with internal and external stakeholders for shared vision across the supply chain, (3) start with pilot projects, and (4) ensure product data-integration for CE business models through computer modeling and blockchain for decision-making processes, choices of materials, business model coordination and product (re)design. Continuous learning about CE roles and responsibilities amidst organizational process restructuring is required throughout linear to CE transitions. Extending the time for the CE principles evaluation process would allow for reconsideration of decisions made for CE implemented projects. Originality/value A novel CE gameplan with a hurdles and recommendations checklist provides an operational interface with decision making points between internal factors for the host organization and external supply chain partners.
【Abstract】The paper studies a new distributed control method to solve the economic dispatch problem (EDP) under directed topology based on consensus protocol. Electrical equipment is closely related to frequency, and the frequency of each generator varies independently during operation. Therefore, it hinders the realization of economic dispatch. To solve the problem, we combine a frequency regulator with a consensus protocol, which eliminates the effect of frequency variation on the designed consensus algorithm. Meanwhile, considering the problem of excessive communication cost and low computational efficiency in large-scale power systems, an event-triggered mechanism is introduced into the designed algorithm. Furthermore, in order to overcome the unexpected loss of communication links, the time-varying topology mechanism is employed to develop the distributed economic dispatch (DED) algorithm to improve the robustness. Then, the stability of the above algorithm is proved by graph theory and convergence analysis. Finally, several simulations illustrate that our proposed methods are effective.
【Abstract】New and disruptive technologies, including cryptocurrencies and new payment methods, are revolutionising the way people engage with finance. Although they provide significant benefits to consumers, they are also inadvertently creating new money laundering and terrorist financing risks. This paper examines the risks that are, or are predicted to be, prevalent in three technology sectors-distributed ledger technologies (including cryptocurrencies), new payment methods and financial technologies (FinTech), through a systematic scoping review process. Specifically, the paper identifies enablers of both crimes, the precise criminal methods they might facilitate, at-risk stakeholders (of exploitation and/or complicity) and risk characteristics. The study involves systematic scoping reviews of the academic and futures literatures as well as a consultation exercise with experts to assess the likely veracity of the findings. In addition to identifying an array of specific risks, we identify six underlying trends that facilitate them. We discuss these, their policy implications, future directions for research and their benefit for conducting risk assessments to assess forthcoming technological developments.
【Abstract】In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts' decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.
【Abstract】The blockchain technology is considered as the most secure and decentralized way to manage your payments with cryptocurrencies. Furthermore, with the advent of NFT's which are a way to make owning digital art a possibility we can see how blockchain has multiple purposes. Blockchain gained traction because of its attributes such as security, immutability, expandability and decentralism. We can see blockchain being used more and more for non-financial applications. On the topic of electronic medical records which need to be secure and be accessed globally, it is still being stored locally or on the cloud which is confined to an organization or a group of organizations. We can observe that healthcare is a field that can benefit massively from blockchain technology. It can make electronic medical records be securely accessed in case of emergencies using smart contracts making the system effectively be used hand in hand with conventional database systems. Smart contracts can be used to manage the accessibility of electronic medical data by third party organizations globally without the data being tampered, manipulated or misused. The system can be used with the current database as it is interoperable. With the successful implementation of this system, it will give access to patients, health care organizations and other third parties the ability to view and manage the electronic medical data.
【Abstract】With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as the use of video conferencing and metaverse platforms has increased due to COVID-19, ethics concepts and boundaries related to information and communications technology, cyber etiquette, AI ethics, and robot ethics have become more ambiguous. Because the definitions of ethics domains may be confusing due to the various types of computing platforms available, this paper attempts to classify these ethics domains according to three main platforms: computing devices, intermediary platforms, and physical computing devices. This classification provides a conceptual ethics framework that encompasses computer ethics, information ethics, cyber ethics, robot ethics, and AI ethics. Several examples are provided to clarify the boundaries between the various ethics and platforms. The results of this study can be the educational basis for the sustainability of society on ethical issues according to the development of technology.
