The study examined the influence of Human Resource (HR) practices on job satisfaction in the decentralized health service delivery in Shinyanga region, Tanzania. The influence of HR practices on job satisfaction is vastly examined by different scholars. Their findings revealed mixed results ranging from significant positive to adverse influence on job satisfaction. Thus, to bridge the knowledge gap, this study examined the specific HR practices in the specific area context. The specific HR practices tested in this study included human resource planning (HRP), supervision, performance appraisal, training and compensation. The region was selected because over different periods of time, it experienced unsatisfactory performance in health service delivery. This was reflected by inability of the region to attain 50% of the Mellenium Development Goals related to health and lowest achievements in health service delivery as reflected in the preparatory stage in launching the Results Based Financing in which the region ranked the last in the then 21 regions of the country. An explanatory Survey research design with mixed research approach was employed for the study. The survey data were collected from 287 respondents and supplemented by the qualitative data. The study found that all the five HR practices had some chances on job satisfaction. However, HRP and supervision revealed significant chances of having job satisfaction implying that they were effectively undertaken. Nevertheless, these practices were constrained by the ineffective employees’ participation in HRP and the inability of the Council Health Management Teams (CHMTs) to provide supportive supervision in health facilities. It was thus recommended to enhance employees’ participation in HRP and supervision for improving job satisfaction. Likewise, it is also important for the facilities to continuously appraise human resource performance and use the results in making human resource decisions.
Zijian Bao, Qinghao Wang, Wenbo Shi, Lei Wang · 6 authors
As a decentralized, public, and digital ledger technology in Peer-to-Peer network, blockchain has received much attention from various fields, including finance, healthcare, supply chain, etc. However, some challenges (e.g., scalability, privacy, and security issues) severely affects the wide adoption of blockchain technology. Recently, Intel software guard extensions (SGX), as new trusted computing technologies, have provided a new solution to the above challenges in the blockchain area. Although many studies have focused on using SGX technology to enhance their schemes in the blockchain areas, no comprehensive survey has systematically analyzed and delineated these studies. This article is the first to systematically discuss the application status of SGX in the blockchain area. In this article, we study the scheme designs, advantages, and disadvantages of the existing works using a six-layer hierarchical structure of the blockchain. We also summarize the functions of SGX and formally analyze the advantages and disadvantages of SGX. Finally, we review the remaining challenges and present a list of possible directions for future research.
Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm is used to optimize the code coverage of the input, which construct the fuzzing framework for smart contract together. Finally, by comparing Oyente and ContractFuzzer, the performance and efficiency of the framework are proved.
Adamu Sani Yahaya, Nadeem Javaid, Muhammad Umar Javed, Muhammad Shafiq · 6 authors
The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.
Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.
Julian Adam Wise, Meng Chak Chan, Dihon Tadic, Stephanie Miles · 9 authors
Abstract This research demonstrates financial derivative trade of unprocessed materials, for the mining industry through legal smart contracts. Within the mining supply chain, a stock of mined resources can reside in a mineral stockpile for over twenty years without gaining financial interest and without undergoing the mineral extraction process to derive value from the asset. This research elaborates on a blockchain solution implemented to increase miners’ short-term cash flow for business operations through the issuance of derivative assets on mineral stockpiles which can be traded through legally binding smart contracts. The system is the first to enable mining companies’ access to the underlying asset’s value earlier in the production lifecycle through smart contract technology whilst providing hedge funds with access to new financial products for investment portfolios.
The access to common-pool resources, i.e. to resources in limited common property, are legally distributed in a far more diverse way than limited private property resources. In transportation, a critical case for common-pool resources appear in Green Transport Corridors (GTC), that has been coined by European Union as being «sustainable logistics solutions for cargo transportation’ with a shared pool of resources aiming for multimodal trans-shipment routes with a concentration of freight traffic between significant hubs». Although there are already existing implementations of GTC concepts, there are still a lot of open questions concerning GTC governance and ownership models hindering easy marketing of the GTC approach. This paper discusses how and to which extent smart contracts in combination with blockchain technology as innovative solutions are able to facilitate GTC governance and how smart contracts can be applied to provide legal certainty by managing and allocating distributed access to common-pool resources. Smart contracts can be considered as computerised transaction protocols for the execution of underlying legal contracts, and they do not only target reducing transaction costs by realising trackable and irreversible transactions through blockchain technology for distributed databases, but also show high potential to strengthen cooperative business structures and to facilitate the entrepreneurial collaboration of cross-organisational business processes. From a legal perspective, it is controversial whether the use of smart contracts to distribute access to resources in terms of both general common-pool resources. GTCs implies an added value automatically for legal certainty and fair balance among different forms and degrees of access granted to different members of the cooperative. In cases of incorrect performance, change of circumstances or unduly induced contracts smart contracts fall considerably short on the protection of weaker parties, which the paper illustrates at the example of GTCs to be a decisive detriment of the cooperative members. The paper analyses these potentials and risks of smart contracts for the case of GTCs and showcases from both business and legal perspective in terms of their potential as viable means of distributing access to common-pool resources comprising infrastructure. Keywords common-pool resources, cooperative governance, blockchain, smart contracts, Green Transport Corridors.
In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coeficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.
Mohammad Zainullah Khan, Yousaf Ali, Hassan Bin Sultan, Muhammad Hasan · 5 authors
The ever-increasing computing power backed by Moore's law and the rapid breakthroughs in encryption has transformed the way currency is transferred and used. While the traditional government-controlled currency maintains its dominant position, a new virtual currency known as cryptocurrency has sprung up. It competes head-on by decentralizing the system, facilitating peer-to-peer transactions, and offering a universal exchange medium. However, the phase shift process is in its early stage, hindered by doubts in the mind of the public. In this paper, a comparison has been made between traditional, digital fiat and cryptocurrency, using the technique for order of preference by similarity to ideal solution (TOPSIS). It helped to ascertain the current level of awareness amongst the people of Pakistan and their likelihood of adopting the virtual currency. A forecasting approach along with a case study is also employed to see the future trends and security concerns revolving around the new medium.
The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.
Cryptocurrencies including Bitcoin are known to be vulnerable to so-called ‘double-spending’ attacks, where the same digital currency is used to execute multiple different transactions simultaneously. Little is known, however, about the underlying reasons for this vulnerability. Here we develop an agent-based model to study how features of cryptocurrency networks contribute to their vulnerability to double-spending attacks. Perhaps surprisingly, we find neither the number of network nodes nor its path length seem to influence the probability of successful attacks. We find robust evidence that the network's clustering coefficient has substantial influence. In particular, scale-free networks, with their small clustering coefficients, are more than twice as likely to succumb to double-spending attacks than are networks with larger coefficients, such as regular networks. The implication is that cryptocurrency networks, which are scale-free, may be uniquely susceptible to double-spending attacks.
We examine how liquidity affects cryptocurrency market efficiency and study commonalities in anomaly performance in cryptocurrency market. Based on the unique features of cryptocurrencies, we build a model with anonymous traders valuing cryptocurrencies as payments for goods and investment assets, and find that decreases in funding liquidity translate into lower asset liquidity in the cryptocurrency market. Empirically, we observe that many widely recognized stock market anomalies also exist in the cryptocurrency market, though some have opposite long/short legs. We also find supportive evidence that a decrease in cryptocurrency liquidity enhances anomaly returns while preventing the cryptocurrency market from achieving efficiency.
Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.
Cryptocurrency can be defined as a digital asset and a virtual element designed to be an alternative exchange tool for cash in terms of how it works, securing transactions using encryption (cryptography).Looking across the world, there are Bitcoin, Ethereum, Bitcoin Cash, Ripple, Litecoin, Cardano, Nem, Iota, Stellar, Dash and many more cryptocurrencies.The most famous of the cryptocurrencies today is Bitcoin, which is the most preferred in terms of transaction volume and constitutes approximately 50% of the cryptocurrency volume.Bitcoin, created by a person or community named Satoshi Nakatomo in 2008 and the first transfer in 2009, is a digitally created cryptocurrency.The aim of this study is to investigate the relationship between Bitcoin, a cryptocurrency, and gold ounce prices and dollar index.In the study, 2012-2019 was determined as the term and the monthly data were examined.ARDL Boundary test approach was used as a method to determine the cointegration relationship between the examined variables.As a result of the study, a long-term co-integrated relationship between Bitcoin's gold and foreign exchange price was determined.With this result, a 1% increase in the gold ounce price will increase Bitcoin prices by about 15% in the long run; The 1-unit increase in the USD index indicates that it will increase Bitcoin prices by about 0.28%.However, it was concluded that there was no co-integrated relationship between the variables in the short term.
Businesses in the insurance sector use DLT technology to open transaction flows and make job efficiency better. Researchers verified that blockchain technology for reinsurance solves fraud and claim processing issues to produce efficient insurance services. We examine how blockchain technology optimizes reinsurance contracts by combining insurance facts and blockchain pilot results using statistical procedures. Companies can complete insurance claims more rapidly at a 30% to 40% lower expense level while spotting 85% of possible fraud. Using this service reduces business expenses and processes run faster since it manages settlements directly plus records all system activities. Significant insurance companies like B3i, IBM, Swiss Re and Etherisc test blockchain-based reinsurance systems in their projects. Bitcoin offers benefits to the industry but users encounter three main difficulties due to unclear rules regarding transactions, several control limitations, and problems with integrating legacy systems. Through scientific data we learn that Distributed Ledger Technology provides dependable partnership programs to reinsurance through a trusted central platform. With fraud-resistant technology from blockchain systems the reinsurance and retrocession industry will develop better protection at lower prices and improved trustworthiness.
Over five thousand digital currencies have been issued by private sector actors since the release of the Bitcoin digital currency in 2009. Private sector issuance of distributed ledger technology (DLT)-based digital currencies such as Bitcoin, Ethereum and other altcoins threaten the stability of financial market infrastructures and preservation of monetary policy. Consequently, many central banks and monetary authorities have begun research and experimentation on central bank-issued digital currencies (CBDCs) to mitigate this threat. In this paper, we present a comprehensive survey of publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes from across the world to enable an understanding of the motivations and best practice approaches for undertaking CBDC experiments. We provide a classification and generic framework for CBDCs and highlight existing DLT platform limitations and use cases in the financial services industry. Overall, our paper organizes in one place, all the relevant, publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes to serve as a reference point for central banks, monetary authorities and researchers desiring to undertake research on DLT-based CBDCs. Ultimately, we present a survey on the technical feasibility and challenges of leveraging DLT to issue the selected CBDC experiments surveyed in this paper.
Gianmaria Del Monte, Diego Pennino, Maurizio Pizzonia
Public blockchains should be able to scale with respect to the number of nodes and to the transactions workload. The blockchain scalability trilemma has been informally conjectured. This is related to scalability, security and decentralization, stating that any improvement in one of these aspects should negatively impact on at least one of the other two. In fact, despite the large research and experimental effort, all known approaches turn out to be tradeoffs. We theoretically describe a new blockchain architecture that scales to arbitrarily high workload provided that a corresponding proportional increment of nodes is provisioned. We show that, under reasonable assumptions, our approach does not require tradeoffs on security or decentralization. To the best of our knowledge, this is the first result that disprove the trilemma considering the scalability of all architectural elements of a blockchain and not only the consensus protocol. While our result is currently only theoretic, we believe that our approach may stimulate significant practical contributions.
The article describes the main trends in the field of intellectualization of transport systems and mobility. The possibility of using the Blockchain technology in transport systems is considered. The article proposes to use Blockchain technology for increasing cybersecurity through the creation of a safe and reliable system for sending parameters of the current state of each vehicle using the signals of neighboring vehicles. The authors have developed a tracking system for car actions using the Blockchain system based on the Exonum platform. Mathematical foundations of this system are presented in the article. Data input and confirmation of their acceptance of the transaction is carried out using an Elliptic Curve Digital Signature Algorithm (ECDSA). ECDSA security is related to the complexity of the private key search task described in the article. This largely relates to the management of large-scale systems, such as transport system. Failure to follow simple rules and recommendations can lead to serious consequences, such as road accidents and congestion. Therefore, the proposed system can assist in making decisions with autonomous cars and in investigating crimes as well as traffic offences.
This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on top of a private Ethereum network. Contrary to existing solutions, we rely on the legacy billing and metering of the existing utility company in order to avoid making major changes to the existing infrastructure. The trading logic, including the auction mechanism, used to exchange energy can be defined in a smart-contract and applied within the platform. We conduct extensive experiments to evaluate the performance of some existing auction mechanisms and the underlying private Ethereum network in supporting the corresponding energy trading transaction load. We develop a virtualization-based simulator for Ethereum and measure both the transaction throughput and latency under different network and workload scenarios. The obtained results have shown that the current Ethereum implementation can support charging requests from EVs during peak hours in very crowded cities, such as Singapore.