1,300 research outputs found

    Criminalizing Money Laundering as a Method and Means of Curbing Corruption, Organized Crime, and Capital Flight in Russia

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    In the wake of the post-Soviet privatization in the Russian Federation, corruption and organized crime have flourished, contributing to capital flight, economic instability, and the collapse of Russia\u27s financial system. Over the same period, Russian legislators have worked to reform the legal system in order to facilitate their country\u27s transition to democracy and the rule of law. In 1997, legislative efforts led to the enactment of a new criminal code that emphasizes the rights of the individual as opposed to the power of the government. More recently, several draft bills targeting money laundering activities and banking reform have been introduced in the Russian Parliament in an effort to confront Russia\u27s economic crisis. Government corruption and the close connection between organized crime and the banking industry, however, have led to fierce opposition to the enactment of such reform measures. Consequently, these measures have been stalled. To confront the problems of crime and economic crisis, Russia should enact a comprehensive anti-money laundering system that incorporates provisions similar to those contained in the recent draft bills. In light of the success of the U.S. anti-money laundering regime, the enactment of a similar system in Russia would be a significant step towards confronting organized crime, government corruption, and an ailing economy

    A Comparison of Database Insider Attack Monitoring Approaches using Page Rank Algorithms

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    We compare two database insider attack monitoring approaches that use page rank algorithms: PageRank and Weighted PageRank. By calculating the weight of queries, we predict the users’ pattern in the database system

    GOMA: Supporting Big Data Analytics with a Goal-Oriented Approach

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    CASA Center for the Internet of Everything (C4IoE)

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    The Autoregressive Distributed Lag Extensions And Applications

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    The autoregressive distributed lag (ARDL) framework is comprehensive with both flexible lag orders of dependent and independent variables to explain the dynamic of the responding variable. This thesis extends the ARDL methods to provide practical applications in economic analysis. The extensions include providing bounds of critical values of the additional testing on lagged level independent variables coefficients for the ARDL cointegration test, innovating the ARDL framework for a multivariate unit root test, and proposing ARDL model for Taylor rule studies. Users who are unfamiliar with programming could use the provided critical values to perform the familiar bounds testing procedure to run the cointegration test proposed by McNown et al. (2018). The dynamic and flexible model in the multivariate ARDL unit root test with related time series information helps to gain more statistical power than the univariate framework unit root tests. Besides that, the limitation in the covariate Dickey-Fuller framework that rules out the possibility of cointegration is covered by the ARDL model to avoid power loss caused by model misspecification. Many Taylor rule empirical studies do not follow proper econometric procedures such as unit root, cointegration, and diagnostic tests. Besides, many pieces of evidence show that the Taylor rule regression is unbalanced with mixed integration order variables. Therefore, level estimates without cointegration reported in the studies could give spurious results. The robust standard error is commonly used in empirical studies, but it is helpless to deal with the residual autocorrelation problem, especially if the model includes lagged dependent variable

    Bootstrapping The Autoregressivedistributed Lag Test For Cointegration

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    Objektif tesis ini adalah untuk mengkaji prestasi ujian kointegrasi: Autoregressive- Distributed Lag (ARDL) Bounds Test yang dikembangkan oleh Pesaran et al. (2001). Pendekatan ini menjadi popular dan banyak digunakan dalam dua dekad atas kelebihan super konsistent estimasi dan menangani masalah pemboleh ubah bebas yang berintegrasi campur. Namun, ARDL Bounds Test sentiasa disalahgunakan dalam situasi yang tidak konsisten dengan andaian dalam rangka kerja tersebut. Pendekatan ini menganggap tiada kesan maklum balas di tahap dari pemboleh ubah bersandar ke pemboleh ubah bebas. Ini bermakna, salah satu pemboleh ubah mestilah sebagai weakly exogenous. Estimasi yang terlibat dalam beberapa kemungkinan pemboleh ubah endogenous yang digunakan sebagai regressors akan memberi keputusan yang keliru dan berat sebelah. Walau bagaimanapun, bukti dari simulasi menunjukkan prestasi pendekatan Bounds Test tersebut tidak dipengaruhi oleh andaian masalah endogeneity. The objective of this thesis is to examine the performances of a cointegration test: Autoregressive Distributed Lag (ARDL) bounds test approach developed by Pesaran et al. (2001). This approach gained popularity and is widely used for over two decades due to its advantages of super consistent estimation and dealing with mixed integration order regressors. Nevertheless, the ARDL bounds test is often applied in environments that are inconsistent with the assumptions underlying that framework. This approach assumes that there is no feedback at level from the dependent variable to the regressors. That is, all variables except one must be weakly exogenous. Estimation involving several plausibly endogenous variables as regressors will give biased and misleading results. However, through simulation evidence our results show that the performance of the bounds test approach is not affected by this endogeneity problem

    Learning and Practicing Data Analytics using SAP In-Memory Computing

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    The analysis and organization of Big Data is becoming important in the business industry. Using and understanding ERP (Enterprise Resource Planning) software to interpret Big Data is essential to the evolution of Information System Technology. This research on SAP HANA and SAP Lumira allows us the opportunity to explore

    Increase in airway neutrophils after oral but not inhaled corticosteroid therapy in mild asthma

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    SummaryBackground: Neutrophils, in addition to eosinophils, are prominent in the airways of patients with severe asthma who are usually on long-term oral and inhaled corticosteroid treatment. We determined whether inhaled or oral corticosteroid therapy can induce airway neutrophilia.Methods: We performed two separate placebo-controlled studies in which patients with mild asthma were treated with either prednisolone (30mg per day for 7 days; n=9) or placebo tablets (n=8), or with either inhaled budesonide (800μg twice daily for 4 weeks; n=6) or inhaled placebo (n=6). Fiberoptic bronchoscopy was performed before treatment and at day 7 of oral treatment, and at day 28 of inhaled therapy. Bronchial sections were immunostained with an antibody to major basic protein for eosinophils, and with an antibody to neutrophil elastase for neutrophils. Induced sputum was obtained in the prednisolone study.Results: Neutrophils in airway submucosa increased after prednisolone from median 76 to 140/mm2 (P=0.05); this change was higher than that after placebo (P=0.04). Eosinophils decreased from 24 to 9/mm2 (P=0.03), but this was not significantly different from placebo. Eosinophils and neutrophils, and levels of IL-8 and myeloperoxidase in induced sputum did not change after prednisolone. There was no change in neutrophil counts after budesonide, but the reduction in eosinophils was greater than placebo (P=0.05). Budesonide improved bronchial responsiveness, but prednisolone did not.Conclusion: Corticosteroid therapy by the oral but not inhaled route can induce neutrophil recruitment into the airways of patients with mild asthma. This could explain the increase in airway neutrophils observed in severe asthmatics treated with oral corticosteroids
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