scholarly journals Dependability Analysis of Bitcoin subject to Eclipse Attacks

2021 ◽  
Vol 6 (2) ◽  
pp. 469-479
Author(s):  
Chencheng Zhou ◽  
Liudong Xing ◽  
Qisi Liu
Keyword(s):  
Automotive Industry ◽  
Financial Services ◽  
Model Parameters ◽  
Continuous Time Markov Chain ◽  
System Behavior ◽  
Modeling And Analysis ◽  
System Protection ◽  
Blockchain Technology ◽  
Dependability Analysis ◽  
Immense Potential

The immense potential of the blockchain technology in diverse and critical applications (e.g., financial services, cryptocurrencies, supply chains, smart contracts, and automotive industry) has led to a new challenge: the dependability modeling and analysis of the blockchain-based systems. In this paper, we model the Bitcoin, a peer-to-peer cryptocurrency system built on the blockchain technology that allows individuals to trade freely without involving banks or other intermediate agents. We analyze the dependability of the Bitcoin system subject to the Eclipse attack. A continuous-time Markov chain-based method is suggested to model the system behavior under the Eclipse attack and further quantify the dependability of the Bitcoin system. The effects of several model parameters (related to the miner’s habits in system protection, restart, and mining frequency) on the system dependability are demonstrated through numerical examples. Findings from this work may provide effective guidelines in designing a resilient and robust Bitcoin system.

Analysis of the Application of Blockchain Technology in the Field of Supply Chain Finance in the Natural Gas Industry

2021 ◽  
Author(s):  
Jianping Zhang ◽  
Fuping Wang ◽  
Yongsong Pu ◽  
Pu Li ◽  
Yingkai Ma ◽  
...  
Keyword(s):  
Supply Chain ◽  
Natural Gas ◽  
Financial Services ◽  
Organizational Innovation ◽  
Gas Industry ◽  
Blockchain Technology ◽  
Supply Chain Finance ◽  
Natural Gas Industry ◽  
Financial Framework ◽  
Gas Supply

Abstract After China's supply chain finance business has gradually matured in the consumer finance field, it has begun to extend to the industrial finance field. As a branch of industrial finance, the natural gas industry supply chain finance business has gradually developed, and the number of participants has gradually increased. The article mainly introduces the development status of natural gas supply chain financial services in China. Research has found that there are still many problems in the current industry development, such as the inability of effective collaboration among participants, and the inability to unify logistics, information flow, capital flow and energy flow in the industry. On this basis, the article studies the methods of blockchain technology to solve corresponding problems, and proposes the application ideas of blockchain technology in the field of natural gas supply chain finance, hoping to promote development by constructing a business model business architecture and technical architecture, This model can produce significant economic and social benefits, has a high theoretical feasibility, but there is no concrete examples at present. Finally, suggestions are made in five aspects, including strengthening the design of top-level systems, incorporating energy flows into the supply chain financial framework system, creating an open innovation atmosphere, enhancing technological progress, strengthening core corporate social responsibility, and promoting core corporate organizational innovation.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Computational Models ◽  
Human Perception ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Affective States ◽  
Modeling And Analysis ◽  
Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

Application of a FRF Based Model Updating Technique for the Validation of Finite Element Models of Components of the Automotive Industry

1995 ◽  
Author(s):  
Stefan Lammens ◽  
Marc Brughmans ◽  
Jan Leuridan ◽  
Ward Heylen ◽  
Paul Sas
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Automotive Industry ◽  
Stiffness Matrix ◽  
Model Updating ◽  
Dynamic Stiffness ◽  
Element Model ◽  
Model Parameters ◽  
Finite Element Models ◽  
Analytical Dynamic

Abstract This paper presents two applications of the RADSER model updating technique (Lammens et al. (1995) and Larsson (1992)). The RADSER technique updates finite element model parameters by solution of a linearised set of equations that optimise the Reduced Analytical Dynamic Stiffness matrix based on Experimental Receptances. The first application deals with the identification of the dynamic characteristics of rubber mounts. The second application validates a coarse finite element model of a subframe of a Volvo 480.

Evaluation of factors affecting the decision to adopt blockchain technology: A logistics company case study using Fuzzy DEMATEL

2020 ◽  
Vol 39 (5) ◽  
pp. 6279-6291
Author(s):  
Ayça Maden ◽  
Emre Alptekin
Keyword(s):  
Financial Services ◽  
Technology Implementation ◽  
Fuzzy Decision Making ◽  
Critical Factors ◽  
Fuzzy Dematel ◽  
Real Time Processing ◽  
Factors Affecting ◽  
Blockchain Technology ◽  
Logistics Company ◽  
Reducing Costs

Blockchain practices have been attracting attention in industries other than financial services, since blockchain is not only an information technology, but also an institutional technology owing to its new currency economics and distributed structures. Today, supply chains, power, and food/agriculture have emerged as promising areas in terms of their potential to incorporate blockchain technology for improving processes and reducing costs. Logistics corporations, especially, have been concentrating on developing efficiency in integrated data, fleet management, and communication issues, to achieve cost advantages. Experts from a well-known logistics company in Turkey contributed to our study by helping to assess critical factors for successful blockchain technology implementation. Our research topic included determining whether blockchain technology is suitable for this company. Fuzzy decision-making trial and evaluation laboratory (DEMATEL) was used to determine and evaluate the critical factors to encourage blockchain technology adoption, based on the company’s requirements. For the company experts, the factors affecting the decision to adopt blockchain technology were, in order of priority: cryptocurrency, instant money transfer, privacy, real time processing, smart contract, security, authentication, transparency, immutability, traceability, distributed ledger, reduced delays, and peer-to-peer networks.

The Role of Blockchain Technology to Make Business Easier and Effective

2020 ◽  
pp. 16-44
Author(s):  
Vartika Koolwal ◽  
Sunil Kumar ◽  
Krishna Kumar Mohbey
Keyword(s):  
Risk Management ◽  
Financial Services ◽  
Information Exchange ◽  
Management Education ◽  
Innovative Technology ◽  
Security Issues ◽  
Blockchain Technology ◽  
Novel Technology ◽  
Wide Range

Blockchain is the new “buzz” word that has attracted the attention of industries and businesses. It is an innovative technology that provides information exchange in an efficient and transparent manner. It has a wide range of application varying from cryptocurrency, healthcare, risk management, education, financial services, internet of things (IoT), border security to public services. However, security issues and threats of this novel technology is also an important topic. In this chapter, the authors provide a comprehensive study of applications, challenges, and issues and how to combat them in the blockchain. Major areas of concern are security, scalability, cryptocurrency's malicious attacks, etc.

scholarly journals Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis

2020 ◽  
Vol 10 (9) ◽  
pp. 3145 ◽  
Author(s):  
Victor Chang ◽  
Raul Valverde ◽  
Muthu Ramachandran ◽  
Chung-Sheng Li
Keyword(s):  
Risk Analysis ◽  
Tax Evasion ◽  
Financial Services ◽  
Predictive Analytics ◽  
Operational Risk ◽  
Regulatory Compliance ◽  
Heston Model ◽  
Business Strategies ◽  
Modeling And Analysis ◽  
Risk Research

Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research.

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