【Abstract】The bitcoin market has substantially grown in recent years. The researchers are exploring its various repercussions for socioeconomic and political matters; however, the literature still lacks clear evidence on how bitcoin interacts with energy and the environment. This study aims to explore the causal relationship between bitcoin, clean energy, and carbon emissions allowances by applying the novel time-varying Granger causality test on the daily data spanning from Sept 17, 2014, to October 12, 2021. The empirical findings confirm that both clean energy and emission allowances are causally associated with bitcoin. However, this causal relationship varies over time and the duration of causality is longer as suggested by the recursive evolving procedure. The outcome is robust when bitcoin is measured by the volume and the price. Furthermore, the results obtained from robustness analysis conducted through heteroskedastic consistent test also validate the findings that bitcoin causes clean energy and carbon allowance. The findings offer a platform for government officials and policy managers to improve clean energy and carbon allowance markets for sustainable development by managing and using the tools to control and regulate cryptocurrency markets.
【Abstract】We study how introducing a central bank digital currency affects equilibrium allocations and welfare in an environment where both currency and bank deposits are used in exchange. We highlight an important policy tradeoff: while a digital currency tends to improve efficiency in exchange, it may also crowd out bank deposits, raise banks' funding costs, and decrease investment. We derive conditions under which targeted digital currencies, which compete only with physical currency or only with bank deposits, raise welfare. If such targeted currencies are infeasible, we illustrate the policy tradeoffs that arise when issuing a single, universal digital currency.
【Abstract】Despite its perceived utility, several companies and managers continue to hold reservations about blockchain implementation in supply chain management. To elucidate the causes, we examine the behavioural and organisational antecedents that influence the adoption of blockchain technology in supply chain management. To do this, we undertake thematic analysis on blockchain-related agri-food business news and expert opinions from the Ovid database's Agricola section. Four themes are central to our model: organisational adoption strategies, technical advantages, environmental obstacles, and implementation intention. Using these themes, we build a thematic map and derive three propositions about the role of behavioural antecedents to blockchain implementation intention in supply chains, thus substantiating Behavioural Reasoning Theory (BRT) and Technology Organisation and Environment (TOE) theory's core arguments. Our findings elucidate novel factors influencing blockchain adoption in supply chains. More specifically, we show that managers who consider the technological benefits associated with blockchain capacity are able to provide stakeholders with new opportunities and embrace adoption strategies such as product launch and partnership formation while also considering environmental and contextual barriers such as market fragmentation, scarcity of research, and regulatory restrictions.
【Abstract】Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
【Abstract】Twitter sentiment has been shown to be useful in predicting whether Bitcoin's price will increase or decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. We utilise not only sentiment extracted from tweets, but also the volume of tweets. We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).
【Abstract】Industrial Internet of Things (IIoT) is facing significant scalability and security challenges in sharing mas-sive device data across heterogeneous network domains. Traditional cloud-based sharing solutions for IIoT applications that are getting closer to devices are uneconomical and inefficient because of their cen-tralized architecture. We propose a blockchain-based data-sharing framework called EdgeShare for edge data-sharing services among heterogeneous network domains by introducing a two-layer overlay net -work topology and an edge computing paradigm that can dramatically alleviate system pressure and reduce communication delays to improve data-sharing efficiency. Under such framework, a novel smart contract is designed to construct access control strategies with dissimilar granularities to achieve differ-ent levels of data access control in an undeniable and automated manner. All IIoT data-sharing activities are recorded in a blockchain for secured transaction logging and auditing. We implement an EdgeShare prototype and conduct extensive experiments and analyses to evaluate the feasibility of our proposals. In contrast with centralized sharing methods, results demonstrate that the proposed framework can effec-tively improve the reliability, efficiency, and security of massive data sharing while introducing reason-able and acceptable overheads. (c) 2021 Elsevier B.V. All rights reserved.
【Abstract】The complexity of blockchain technologies is reflected in the various ways in which social actors operate with crypto transactions, and this is seen in the wide range of discourses and practices adopted on the crypto market. Using social constructivism, this study highlights the fact that social practices in the crypto area respect certain thematic ideologies, through which individual discourses reflect different pragmatic expectations in relation to virtually encrypted transactions. Drawing on inductive thematic analysis based on 38 semi-structured interviews with Romanian investors, four major themes emerge: (1) unbreakable power of code encompasses rather libertarian discourses on potential state policies, (2) key role of peers explains the investments in the crypto market as a form of community membership, (3) assuming plenty of risks highlights the discourses focused on substantial and unpredictable gains, and (4) preferring safety above all encompasses discourses on financial stability through rather conservative behaviors.
【Abstract】Peer-to-peer (P2P) energy trading platform is an upcoming energy generation and effective energy managing strategy that rewards proactive customers (acting as prosumers) in which individuals trade energy for products and services. On the other hand, P2P trading is expected to give multiple benefits to the grid in minimizing the peak load demand, energy consumption costs, and eliminating network losses. However, installing P2P energy trading on a broader level in electrical-based networks presents a number of modeling problems in physical and virtual network layers. As a result, this article presents a thorough examination of P2P studies of energy trade literature. An overview is given with the essential characteristics of P2P energy trading and comparatively analyzed with multiple advantages for the utility grid and individual prosumers. The study then addresses the physical and virtual levels that systematically categorize the available research. Furthermore, the technological techniques have been gone through multiple problems that need to overcome for P2P energy trading in electrical networks. Finally, the article concludes with suggestions for further research.
【Abstract】In order to explore the effects of propofol anesthesia system based on block chain on brain tissue and brain function protection, 88 patients undergoing craniocerebral trauma surgery are selected for prospective study. The anesthetic dose of propofol during operation in the control group is determined according to general information and personal situation. The team uses blockchain to process the patient's clinical data and general information to calculate the optimal dose of propofol. The changes of hemodynamic indexes of patients in the two groups are compared, including dural incision (T1), 1 h after craniotomy (T2), end of surgery (T3), and 6 h after surgery (T4). The changes of neuron-specific enolase (NSE) and central neuron-specific protein (S100 beta) at T1, T2, T3, and T4 in 2 groups are observed and recorded. The changes of malondialdehyde (MDA) at T1, T2, T3, and T4 are compared between the two groups. Based on the analysis of clinical data and general information of patients on blockchain, the optimal intraoperative anesthetic dose of propofol for patients can effectively protect the brain function of patients, and the intraoperative anesthesia state is relatively stable, and the changes and improvements of intraoperative indicators are good.
【Abstract】The credibility of information is known as a major cause of a wide range of issues, such as: altered product information in food supply chains; fake transactions on E-commerce platforms; lengthy claim settlement time in agricultural insurance; and costly borrowings in agricultural financing. For a more specific example in food supply chains, end customers want to check the product information, but either doubt the authenticity of information, or simply do not have access to the information. The reason is that upstream suppliers and downstream retailers are often reluctant to share data, fearing privacy loss or business secret leakage. The consequence is that regulatory departments may face enormous challenges to identify accurate contamination sources, if there is scarce information or falsely recorded information at any stage of the food supply chain. In this paper, we focus on four common scenarios demanding information credibility in the agricultural supply chain: product traceability, E-commerce platforms, agricultural insurance, and agricultural financing. We review some high-profile smart credibility applications with emphasis on how blockchain related technologies can provide the information credibility by examining extant issues and relevant frameworks.
【Abstract】Purpose This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic. Design/methodology/approach This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016-March 2021. Findings The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash. Originality/value The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.
【Abstract】This study aims to compose a portfolio consisting crypto hedge fund and ASEAN-5 stock market and to examine the hedging effect of crypto hedge fund against those stock markets. This study employs dynamic portfolio approach using data from the period of July 2013 to August 2021. This analysis finds that crypto hedge fund can provide hedging effect against ASEAN-5 stock portfolio resulting in hedging effectiveness with positive value. Crypto hedge fund is also proven to be able to increase the risk adjusted performance of all ASEAN-5 portfolio observed under this study, shown by an increase in Sharpe ratio and Sortino ratio.
【Abstract】Electronic voting (e-voting) has been studied for many years. Recently, researchers find that blockchain can provide an alternative secure platform for e-voting systems, because of its properties of tamper resistance and transparency. However, existing blockchain-based schemes either require central authorities to tally ballots or can only handle a limited number of voters. This paper tries to propose a self-tallying e-voting system, i.e. the public can verify the validity of all ballots and tally the ballots without a centralized authority. To achieve this goal, we solve two challenges, namely how to cancel out all random numbers used for ballots and to prove the validity of ballots using a non-interactive zero knowledge proof. Our scheme is proved to be secure and shown to be practical by experiments.
【Abstract】FinTech - along with the blockchain, other distributed ledger, smart contract, and tokenization usually assumed to accompany it - could change the way governments procure goods and services. Yet, both the academic and practitioner literature omits discussion of any policy-level analysis of the interaction between procurement rules and FinTech innovations. Procurement authorities and procurement law can play a vital role in the development of FinTech. They can help build the FinTech platforms and ecosystems that help them engage in public procurement. They should not try to procure such FinTech outright. Any FinTech applications facilitating public procurement should thus encourage compliance with the principles the international community has developed over decades.
【Abstract】Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzge and Sapkauskiene [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question. (C) 2021 The Author(s). Published by Elsevier Inc.
【Abstract】The study applies the grey model (GM(1,1)) to the Verhulst differential equation for forecasting the Bitcoin transaction counts. The grey Verhulst model (GVM) is based on the data set of Bitcoin as recorded along 10 years from the 1st August 2010. The model accuracy is checked by the mean absolute percentage error (MAPE), while the model predictability is assessed by analysing a plot of the Verhulst model constructed upon the parameters provided by the GVM. The MAPE criterion suggests the reasonable accuracy of the overall GVM forecasting values and high accuracy by considering the last 400 forecasting values. Furthermore, the Verhulst model plot suggests that the GVM is potential on predictability as the plot is not chaotic. The GVM forecasting values suggest a slight future decline in transacting Bitcoin; this may be due to its competition with the other emerging cryptocurrencies. The GVM suggests a relatively high performance as compared to the usual one-variable forecasting model GM(1,1).
【Abstract】This article presents the problem regarding the assurance of the information that circulates in the devices that are connected to the internet, due to the multiple threats to which they are exposed, running the risk of losing or leaking information; However, with the passage of time, new technologies are emerging that encourage organizations to maintain continuous improvement of their applications and tools, but at the same time the emergence of new technologies brings with it cybersecurity risks that organizations must face when carrying out an integration with them. Therefore, through the action research methodology, two phases were defined, one of them reviewing Blockchain technologies and the second comparing Blockchain technologies to finally determine the most adaptable technology to a cybersecurity Framework focused on IIoT, capable of managing the integrity of the information and achieving a consensus among all the nodes belonging to a certain network.
【Abstract】Leading luxury brands have incorporated technologies to recreate brand images and reinvent consumer experience. The fashion industry is experiencing a historic transformation thanks to emerging technologies such as blockchain and non-fungible tokens (NFTs) along with impactful technologies such as artificial intelligence (Al), machine learning (ML), and virtual reality (VR). With metaverse as a new social platform around the corner, academics and industry alike are querying how these new technologies might reshape luxury brands, reinvent consumer experience, and alter consumer behavior. This research charts new academic territory by investigating how newly evolved technologies affect the fashion industry. With practical examples of luxury brands, this article has theorized the irreversible trend of digital fashion: the attraction of NFT collectibles. It then proposes intriguing questions for scholars and practitioners to ponder, such as will young consumers, essentially living online, buy more fashion products in the digital world than in the real world? How can the fashion industry strategize for the coexistence of digital collections and physical goods?
【Abstract】The model of higher education institutions has remained widely unchallenged over time. Although study topics keep evolving and the inclusion of increasingly sophisticated technologies have revolutionised the format of lectures and learning experiences. Their value chain remains unchanged, with institutions acting as intermediaries, between professors (knowledge) and students (recipients), and as central authorities granting and validating student knowledge. The work here presented is a review of blockchain technology and its application in education, emphasising the opportunities for disrupting the current value chain of academic institutions. Blockchain technology's prime novelty constitutes an incorruptible digital ledger of transactions, capable of recording virtually any nature of value exchange. When coupled with other emerging technologies such as the internet of things and big data, new paths for the decentralised exchange of education and recording of knowledge and skills appear. The result is an extensive analysis of the evolution of blockchain technology for education applications, along with a forecast of plausible scenarios of disruption for academic institutions. Blockchain technology is steadily advancing at accelerating rates; applications aimed at decentralising educational institutions are already available. Meanwhile, the technology keeps gaining momentum in a growing base of adepts.
