The financing of terrorism is the most critical aspect in the growth of terrorism, and combating the financing of terrorism is the most crucial aspect to eliminating the roots of terrorism. Suppose the actions of governments and international financial and non-financial organisations successfully combat the traditional methods of financing terrorism. In that case, terrorists will move beyond conventional methods and turn to new financial technologies. Due to features such as anonymity, creation of security layers and decentralisation, virtual currencies, especially Bitcoin, are among the financial innovations that criminals, including terrorists, pay attention to for investment and financing. In this article, we will see in a detailed review how Bitcoin, regardless of whether it can be considered a valid currency in the future, by changing the financial systems, has become attractive to illegal networks and can create a revolution in financing terrorism.

Bitcoin and digital currencies have emerged as a new market for investment. Therefore, the prediction of their future trend and prices is highly significant. In this research, the factors influencing the price of Bitcoin were identified and extracted based on previous researches. The identified factors include the US dollar index, CPI index, S and P 500, Dow Jones, and gold price. Considering the performance of metaheuristic algorithms in predicting Bitcoin price, this research utilized genetic algorithm and particle swarm optimization algorithm, and proposed a hybrid algorithm to improve their performance.According to our results, among the investigated factors, the US dollar index has the greatest impact on Bitcoin price, followed by inflation rate and the CPI index. Additionally, the proposed hybrid algorithm outperforms the particle swarm optimization and genetic algorithms, with a prediction error of 7.3%. It should be noted that the type and magnitude of the impact of the investigated factors may change over time. For example, a factor that previously had a direct impact may become reversed or neutralized over time.

Blockchain is a technology that enables distributed and secure data structures for various business domains. Bitcoin is a notable blockchain application that is a decentralized digital currency with immense popularity and value. Bitcoin involves many concepts and processes that require modelling for better comprehension and development. Modelling is a technique that simplifies and abstracts a system at a certain level of detail and accuracy. Software modelling is applied in Model-Driven Engineering (MDE), which automates the software development process using models and transformations. Domain-specific languages (DSLs) are languages that are customized for a specific domain and offer intuitive syntax for domain experts.  To address the need for specialized tools for Bitcoin blockchain modelling, we propose a novel Unified Modelling Language (UML) profile that is specifically designed for this domain. UML is a standard general-purpose modelling language that can be extended by profiles to support specific domains. A meta-model is a model that defines the syntax and semantics of a modelling language. The proposed meta-model, which includes stereotypes, tagged values, enumerations, and constraints defined by Object Constraint Language (OCL), is defined as a UML profile. The proposed meta-model is implemented in the Sparx Enterprise Architect (Sparx EA) modelling tool, which is a widely used tool for software modelling and design. To validate the practicality and effectiveness of the proposed UML profile, we developed a real-world case study using the proposed meta-model and conducted an evaluation using the Architecture Tradeoff Analysis Method (ATAM). The results showed the proposed UML profile promising.

While cryptocurrency trading is often based on various analyses and strategies, studies show that a significant portion of traders make decisions driven by emotions. This study evaluated the impact of investor sentiment on Bitcoin returns from 2019 to 2020 using daily time-series data. We employed Vector Auto-Regression (VAR), Impulse Response Functions (IRF), Variance Decomposition (VD), and Cointegration tests to analyze the relationship between sentiment indices, including the Fear and Greed index, Google Search index, Investor Happiness index, Bitcoin Trading Volume index, and American Association of Individual Investors (AAII) index, and the Euro-Dollar price, gold price, and S&P 500 index with Bitcoin returns. The findings indicated a negative relationship between the Fear and Greed index, Euro-Dollar price, gold price, Google Search index, and S&P 500 with Bitcoin returns. Conversely, there was a positive relationship between the AAII index, Investor Happiness index, and Bitcoin Trading Volume index with Bitcoin returns.Keywords: Investor Sentiment, Cryptocurrency, Bitcoin, Vector Auto-Regression (VAR) Model IntroductionIn recent years, cryptocurrencies have attracted widespread interest as an investment asset. While cryptocurrency trading often involves various analyses and strategies, studies have shown that a significant portion of traders make decisions based on emotions (Guler, 2021; Eom et al., 2018). Bitcoin sentiment analysis tools can be powerful in identifying market trends if used effectively. This study examined the role of investor sentiment in the cryptocurrency market, specifically evaluating the impact of sentiment on Bitcoin returns from 2019 to 2020. We utilized a wide range of sentiment indicators, including the Fear and Greed index, Google Search index, Investor Happiness index, Bitcoin Trading Volume index, and American Association of Individual Investors (AAII) index. Analyzing the relationship between these sentiment measures and Bitcoin returns can provide insights into the various dimensions of investor sentiment in the cryptocurrency market. Materials & MethodsThis study evaluated the impact of investor sentiment on Bitcoin returns from 2019 to 2020 using daily time-series data. We employed Vector Auto-Regression (VAR), Impulse Response Functions (IRF), Variance Decomposition (VD), and cointegration tests to examine the short-term and long-term relationships among the variables. The model for analyzing the impact of investor sentiment indices and other independent variables on Bitcoin returns was specified as:      (1) where ​ represents Bitcoin returns, which are obtained through Eq. 2:                                                                                              (2)where Pt ​ is the price on day t and Pt-1 ​ is the price on the previous day. Forex represents the Euro-Dollar price in the Forex market, Gold is the price of an ounce of gold, and SP500 is the United States stock market index (S&P 500). The investor sentiment indices used in the model include FG (Fear and Greed index), Happy (Investor Happiness index), Vol (Bitcoin Trading Volume index), AAII (American Association of Individual Investors index), and Google (Google Search index). The Google Search index is derived from the data on the number of searches for the term "Bitcoin price" on Google Trends.This model allowed us to evaluate the relationship between the investor sentiment indices and Bitcoin returns based on the existing theories and prior research in this field. Research FindingsThe results indicated several significant long-term relationships: The American Association of Individual Investors (AAII) index had a positive long-term relationship with Bitcoin returns. According to the impulse response functions, increases in the AAII index led to higher Bitcoin returns. The Fear and Greed index had a negative long-term relationship with Bitcoin returns. Increases in this index resulted in lower Bitcoin returns. The Euro-Dollar exchange rate had a negative long-term relationship with Bitcoin returns. Higher Euro-Dollar prices led to decreased Bitcoin returns. The gold price had a negative long-term relationship with Bitcoin returns. Increases in gold prices corresponded with lower Bitcoin returns. The Google Search index had a negative long-term relationship with Bitcoin returns. Higher search volumes for "Bitcoin price" were associated with decreased Bitcoin returns. The Investor Happiness index had a positive long-term relationship with Bitcoin returns. Increases in this index led to higher Bitcoin returns. The S&P 500 index had a negative long-term relationship with Bitcoin returns. Higher S&P 500 levels corresponded with lower Bitcoin returns. The Bitcoin Trading Volume index had a positive long-term relationship with Bitcoin returns. Increased trading volume was associated with higher Bitcoin returns. These findings provided insights into the multi-dimensional relationships between investor sentiment, macroeconomic factors, and Bitcoin returns over the long term. Discussion & ConclusionThe findings of this study revealed several important insights about the relationship between investor sentiment and Bitcoin returns: 1) The Fear and Greed index, Euro-Dollar exchange rate, gold prices, Google search volume for "Bitcoin price", and S&P 500 index all had negative long-term relationships with Bitcoin returns. Increases in these variables corresponded with decreases in Bitcoin returns. 2) The American Association of Individual Investors (AAII) index, Investor Happiness index, and Bitcoin Trading Volume index had positive long-term relationships with Bitcoin returns. Increases in these sentiment indicators were associated with higher Bitcoin returns. These results demonstrated the significant influence of investor sentiment on the cryptocurrency market. Emotions and perceptions appeared to play a crucial role in driving Bitcoin returns over the long term. Investors reflected their sentiment in their trading decisions, which in turn impacted price dynamics in the Bitcoin market. This underscored the importance of understanding how various sentiment indicators affected financial markets, including cryptocurrencies. Investor sentiment analysis can provide valuable insights that inform investment decision-making. By monitoring sentiment metrics, investors can make more informed decisions and adjust their portfolios accordingly as sentiment shifts. In conclusion, this study highlighted the multifaceted relationship between investor sentiment and Bitcoin returns. The findings emphasized the need for investors to closely track sentiment indicators when participating in the cryptocurrency market. Further research in this area can yield additional insights into the behavioral aspects of cryptocurrency investment.

In recent years, digital currencies have attracted many investors. Bitcoin is one of those digital currencies that are considered to be trading more than other currencies. A review of Bitcoin prices is indicative of fluctuations in this market, which increases probability of existing bubble. In this regard, using the monthly data for the period from 08/2013 to 01/2018, the bubble of this market and the single or multiple type of bubbles were investigated. The method used in this study is A recursive unit root test, which was used in the form of SADF, RADF and GSADF tests. Based on the RADF test, there were four bubble periods (March 2015-April 2015, Decembar 2015-March 2016, July 2016-January 2017, September 2017-not disappeared) that had single structures. Based on the SADF test, there was a continuous long bubble period (August 2016-not disappeared), which has a single structure and has not yet exploded. Finally, based on the GSADF test, there were three bubble periods (March 2015-May 2015, Decembar 2015-March 2016, July 2016-not disappeared), that two first bubbles have same structure (March 2015-May 2015, Decembar 2015-March 2016) and the third one has multiple structure (July 2016-not disappeared).

The purpose of this article is the pathology of crypto-currency crimes in cyberspace and it intends to use cyberspace data to express a new perspective for criminalizing virtual currencies. The current research is an applied research in terms of the type of research and a qualitative research in terms of the method of implementation and research method, with two qualitative methods of examining scientific documents and content analysis of virtual currencies. The data collection tools are scientific texts and interviews with experts and the statistical community of virtual currency experts. The research data were thematically analyzed and subjected to descriptive analysis. The findings of the research show that the three main categories of structure, decision-making and information are effective on the dependent variable of the research, i.e. the massing of virtual currencies.It is concluded that the most important indicators of the comprehensive system of criminalization of virtual currencies are the development of guidelines and guidelines for the criminalization of digital currencies, and there are traces of money laundering in cryptocurrencies and the identification of effective factors in cyberspace and criminalizing factors of virtual currencies in the research. It is clearly visible. These cases are one of the important factors of disorder and disturbance in economic security, and the failure to perform pathology in the field of virtual currencies, its economic consequences are irreparable, and the hidden crimes resulting from them lead to the reduction of the country's judicial and security system's control over crime in real and virtual space. will be Finally, one of the characteristics of the research is that it is possible to use solutions against illegal activities in this field.

Money is one of the phenomena that are used in the economic life of people for a long time. Despite the simple appearance, this phenomenon is constantly evolving. Since 2009, a new form of money has been created in the world under the title "Cryptocurrency". This exchange facility is out of reach of the banking system and the central bank and is only transmitted through cyberspace. As this kind of money becomes widespread in the economic relations of the world, there is a serious transformation in the nature and functioning of the money. Changes in the nature and function of the economy may have different jurisprudential functions than fiat money. Therefore, it is necessary to accurately identify the jurisprudence system, the nature and economic functions of this phenomenon, and clarify the types of permissible and unlawful rulings in economic relations. In this regard, in this research, at first the cryptocurrency is introduced and then the jurisprudential aspects of the subject were explained by two approaches of individual and governmental jurisprudence, as well as in two levels of transaction and mining. Finally, using the method of multi-stage ijtihad research and the views of the Islamic finance experts and the authorities, imitating the license terms, the use Cryptocurrency has been counted. Accordingly, one of these types of money, called Bitcoin, is adapted to those circumstances and finally the existence of the doubtful of "La Darar and La Darar" has encountered it with illegal forms.

The concept of digital money or electronic currency means encrypted money, in order to facilitate financial affairs and to create money without the presence of intermediaries (banks) and through community members. Digital money does not have any central service or financial institution to control the transfer; because everything is based on peer-to-peer communications. The number of digital money in the world is increasing, including Bitcoin, the most widely used and most valuable electronic currency in the real world. Bitcoin is a public internet system that has introduced a pay-as-you-go payment system and e-money. Bitcoin takes power from users and, from their point of view, Bitcoin is an internet money. It seems that Bitcoin can not be considered as a kind of money; rather, it's a "digital asset. " In order to examine Bitcoin from the point of view of Islamic jurisprudence, it should first be seen whether it is considered as an asset and property? And then, by explaining the nature of money, it should analyze the Bitcoin monetary nature and then examine the issues of Ghorar and loss and disorder in the system and speculation in the Bitcoin network from the Islamic jurisprudence perspective.

Cryptocurrencies, especially Bitcoin, as an emerging and developing phenomenon, have attracted the attention of Islamic scholars, therefore, researches were conducted on its nature and rules from the perspective of Islamic jurisprudence. This article, while discussing the nature of Bitcoin transactions, has investigated the existence of usury, including transactional usury and loan usury, in such exchanges, using analytical-descriptive method and jurisprudential modeling of this cryptocurrency transactions. From the point of view of transactional usury, according to the conditions stated in the validity of this title, including the conditions of exchange and also the validity of the title of sale in the transaction, the ruling of usury in Bitcoin exchanges was investigated and the following results were obtained in summary: From the point of view of some scholars, the nature of Bitcoin is commodity, and from the point of view of others, it is known as credit. In this article, based on both approaches, the possibility of transactional and loan usury was investigated. From the point of view of the authors of the article, according to the view of Bitcoin traders, the nature of Bitcoin is a combination of commodity and credit. As a result, buying and selling it together with excess in exchange for Bitcoin as being the same kind would not be usury due to the absence of measurement and weight conditions. From the point of view of loan usury, since any type of loan with excess is considered usury, there is usury if Bitcoin is considered as commodity. From the credit point of view, usury can be visualized due to the fact that Bitcoin conventionally represents the dollar value.