【Abstract】This study investigates the quantile dependence and spillovers for return and volatility of Bitcoin and futures of crude oil, copper, natural gas, and gold. We apply quantile vector autoregression and quantile connectedness approaches using a rolling-window method to examine spillover dynamics. The empirical results reveal that return spillovers increase when asset returns deviate from normal market conditions, and volatility spillovers are particularly increased during bullish market conditions. Moreover, the study finds that under bearish and normal market conditions, Bitcoin is a major recipient of return spillovers from all futures, and crude oil and copper are major transmitters of return spillovers to natural gas and gold, respectively. However, during bullish market states, Bitcoin becomes a major transmitter of return spillovers to other futures. Under unstable market conditions, gold is a major transmitter of volatility spillover to crude oil and natural gas. Furthermore, the directional link from Bitcoin to other futures is stronger when market issues exist.
【Abstract】With the increased specifications of healthcare services, diagnosis become very common in most of the healthcare sector to provide better treatment for patients. Here, the person's medical information can be tracked with the help of an Electronic Health Record (EHR). The EHR system maintains the digital medical records of medical organizations. Enterprise-wide data systems, along with other networking activities, are used to exchange medical documents in the current environment where the patient seeks rapid access to their medical records. Moreover, the EHR system faces many problems regarding integrity, management and security. Still, there are some concerns regarding the security and privacy of patient medical records. However, it suffers from managing the large volume of electronically generated healthcare data. Thus, this work adapts blockchain technology, which effectively stores healthcare data. Therefore, decentralized technology was just recently introduced in order to offer an innovative viewpoint on data security and system effectiveness. In this work, a blockchain-related system is developed to secure patients' health data with high verifiability. A total of two mechanisms are carried out in the developed model to encrypt the privacy of the health data that is Data Sanitization and Polynomial Interpolation-based Cryptography (DSPIC). In the data sanitization mechanism, the optimal key is generated through the Modernized Position-based Coot and Penguins Search Optimization Algorithm (MP-CPeSOA). The polynomial interpolation method is carried out through the estimation of values between two data points to handle the heterogeneity. The objective function is considered for securing the patient's health data with Euclidian distance, hiding ratio, preservation ratio and correlation among the original data and the restored data. In this developed model, the generated key is digitally signed to provide high security for the patient's health data. At last, the effectiveness of the suggested blockchain-adopted EHR preservation system is enhanced by conducting experiments on different EHR management schemes.
【Keywords】Electronic health record management system; Multi-objective function; Blockchain technology; Modernized position-based coot and penguins; search optimization algorithm; Data sanitization and polynomial interpolation-; based cryptography; Optimal key generation
【Abstract】ChatGPT and Metaverse are contemporary artificial intelligence tools that are increasingly being used in healthcare professional training, particularly for remote patient monitoring. These technologies offer immersive and personalized learning experiences for nurses, improving their skills and confidence in managing remote patient care. ChatGPT can create simulated patient interactions that mimic real-life scenarios, while the Metaverse can provide virtual reality simulations and scenarios for nurses to practice and learn in a safe and controlled environment. The unification of ChatGPT and Metaverse technology in nursing education can enrich the learning experience and equip nurses with the necessary skills for remote patient monitoring, ultimately leading to improved patient outcomes and quality of care.
【Abstract】Are we witnessing the return of virtual worlds such as Deuxieme Monde (1997) or Second Life (2003), boosted and enhanced by technologies? Or is it the coming of the next generation of the Internet (Web 3.0)? Or is it just a marketing repackaging of virtual reality markets that up to now did not deliver as expected? This paper attempts to provide answers to these questions. It introduces the notion of the metaverse, looks at its definition(s), and describes its key elements, thereby outlining the metaverse ecosystem. The article also attempts to draw lessons from the pioneering experiences of former virtual worlds, and thus examines some case studies from the video game industry. In conclusion, we investigate the metaverse's potential constraints (energy/environment, cost of hardware and bandwidth, lack of business models, regulation) and opportunities, and reveal the challenges ahead for its widespread adoption.
【Abstract】This work aims to explore the fusion of deep learning and blockchain technology for research applications in photography and art studies. A digital image watermarking model is established, followed by the development of a versatile watermarking algorithm model using singular value decomposition (SVD) and deep learning techniques, thereby extending the applicability of watermarking technology. Furthermore, the performance parameters of the blockchain model are designed using the practical byzantine fault tolerance consensus mechanism to ensure transaction consistency and reliability. Finally, the algorithm's normalized cross-correlation is analyzed and attack experiments are conducted on the original images. Adjustments are made to the model to simulate the scalability of sharded blockchains, and scalability of cumulative revenue is evaluated through simulation experiments. Through simulation experiments, it is demonstrated that this model can capture value functions in a sharded blockchain environment, accelerating convergence speed and achieving scalability. After the optimal value function is obtained, cumulative revenue remains consistently stable, indicating the model's robustness and performance. Deep learning networks are currently successfully extracting key features from photographic works, thereby demonstrating the model's effectiveness in image processing. Attack experiments currently show that 3.901% of the detected images contain collagen. The degree of decentralization has significantly increased after optimization, with a value of 0.896 before optimization and 0.216 after optimization. Post-optimization, no single entity currently dominates the system. The current decentralization degree significantly exceeds the pre-optimization state (P < 0.05), suggesting the model's robustness against anomalous or malicious inputs. The combination of deep convolutional neural networks with SVD and blockchain technology is currently exhibiting strong convergence performance and the ability to extract critical image features during photo post-processing. It currently demonstrates excellent convergence and holds significant potential for applications such as image copyright protection and enhancing image processing in various domains.
【Abstract】The paradoxical nature of competitors collaborating, known as coopetition, is a phenomenon of increasing importance in a world that is becoming progressively integrated through digitalisation. This paper is among the first to explore the potential opportunities and limitations of blockchain as an enabler of coopetition. The distributed nature and cryptographic capabilities of the blockchain challenges the dichotomous view of coopetition - collaborate or do not collaborate - through its technological flexibility regarding information-accessibility. This paper focuses on the empirical setting of the wind turbine industry, in which a multilateral instance of coopetition is taking place with five turbine manufacturers and 11 first tier suppliers being involved in developing new standards for the industry. Limiting factors for coopetition and blockchain are found to be based in social contexts such as competence- and integrity-based trust, and in legal context with competition laws hindering the extent to which coopetition can occur.
【Abstract】Given the disruptive changes that blockchain has brought to various industries, the profound evolutionary paths of blockchain technology (BCT) can help to comprehend it better. Thus, this paper investigates BCT from a technology management perspective that includes three parts: 1) topic identification; 2) topic evolution analysis; 3) topic prediction. In the first part, the major application fields and annual topics of BCT are identified based on cluster analysis and the hierarchical Dirichlet process (HDP), respectively. The second part calculates the topic importance and similarities amongst annual topics. Five evolutionary patterns were obtained based on the similarity relations. In the last part, both emerging and lasting topics are predicted by considering their importance and evolutionary patterns. The findings are substantial and interesting, including five main BCT-application fields, several emerging and lasting technical topics and the two longest evolutionary paths.
【Abstract】From the planting base to the consumer's table, agricultural products must go through multiple links such as planting, processing, transportation, warehousing, and sales. The quality and safety of agricultural products have received extensive attention from all walks of life. Based on the block chain technology, this paper will build a traceability system for the quality and safety of agricultural products, refine the research objects, and design solutions from the aspects of overall structure, role authority, operating process, and functional modules according to the characteristics of planted agricultural products, so as to realize the whole process of agricultural product supply chain tracking, traceability to ensure the quality and safety of agricultural products.
【Abstract】The increasing prevalence of immersive technologies and blockchain platforms in modern commerce has ignited animated debates among intellectual property law scholars on the use of nonfungible tokens (NFTs) in the sale of crypto-assets or virtual property. Despite the rapidly growing interest in the implications of NFTs for copyright law, particularly in the realm of digital art, relatively little attention has been given to the question of whether the rights of copyright stakeholders (as opposed to the works in which such rights subsist) are capable of tokenisation as NFTs or of being transferred via NFT-tethered transactions in blockchain environments. This article highlights the dangers of treating copyright as capable of being tokenised or transferred as NFTs on blockchain platforms, and argues that such an approach poses fundamental risks to the 'nemo dat' principle in property law. The article further proposes that the right of communication in copyright law should be extended to include the minting of NFTs in relation to digital files containing creative expression, to protect the interests of digital artists from the exploits of rogue crypto-traders on blockchain platforms.
【Abstract】Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user's activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.
【Abstract】This article provides a thorough examination of phishing attempts, their use, several contemporary visual similarity-based phishing detection systems, and their comparison evaluation. This research article aims to propose an effective design technique for IDS with regard to online applications. We develop a new set of features based on time-frequency analytics that makes use of 2-D models of monetary operations for preventing money laundering systems. As a classification algorithm, random forest is used, and clustering algorithm is used to tune the hyperparameters. Our findings imply that bitcoin exchanges would behave in an excessive reporting manner more than private banks under this law. We specifically take into account the monetary operations as a digital signal and attempt to build a classifier using a collection of frequently mined rules. Our tests on a replicated transaction dataset based on actual banking operations demonstrate the effectiveness of our suggested approach.
【Abstract】Blockchain is an exciting new technology that has garnered attention across multiple industries. This new technology offers several advantages, including decentralization, transparency, and immutability. However, several issues limit the effectiveness of this technology, such as scalability, interoperability, and privacy. A systematic review of blockchain scalability research was conducted using three primary databases: ACM, Science Direct, and IEEE. The review examined the state of the art in blockchain scalability, identifying the most important research trends and challenges. The solutions that have been established can be categorized into two main groups: those that pertain to block storage and those that pertain to the underlying blockchain mechanism. Numerous solutions were suggested for each main group. The most common proposed solutions for improving the scalability of blockchain networks in the literature are improving the consensus algorithm and using sharding. Most of the solutions were proof of concept and need more investigation in the future.
【Abstract】Smart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT-enabled medical equipment enables remote monitoring of patients, allowing for in-home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain-based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT-based healthcare.
【Abstract】The paper investigates long memory, structural breaks, and spurious long memory in the daily trading volume of the largest and most active cryptocurrencies and stablecoins, namely, Bitcoin, Ethereum, Tether, USD coin, Binance coin, Binance USD, Ripple, Cardano, Solana, Dogecoin and Bitcoin cash. The overall results show that both long memory and structural breaks are present in the cryptocurrencies trading volume, and the detected long memory property is not driven by structural breaks but rather true and thus not spurious. Given this, we conduct out-of-sample forecasting and indicate that the ARFIMA model, which accounts for long-range dependence, has a superior forecasting performance over the standard ARIMA model for four cryptocurrencies, namely, Binance coin, Ripple, Cardano, and Dogecoin at most forecasting horizons ahead and the shorter forecasting horizon (1-day ahead) for most cryptocurrencies under investigation.
【Abstract】Smart contracts are the building blocks of blockchain systems that enable automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analysing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behaviour of smart contracts, as a new feature. We design a learning and monitoring module to determine fraudulent smart contract behaviours.Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, the experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%), and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviours of smart contracts.
【摘要】现有区块链内容监管方案均采用事后治理方式，缺乏事前审计，且存在签名失效和多版本区块验证效率低的问题。针对这些问题，首先，设计了一种可动态调整可追责的数据审计方法，实现了对区块链交易数据的事前审计；其次，设计了一种编辑可控的数字签名方案RCDSS(redaction-controlled digital signature scheme)，解决了因编辑操作造成的签名失效问题；最后，设计了一种区块链数据一致性验证协议，实现了对查询结果的高效验证。安全分析和性能测试结果表明了方案的安全性和有效性。该方案在实现监管可控的情况下，仍然保持了较高的区块生成、验证效率，为区块链内容监管提供了一种新的解决思路。
