Do the Robertson-SCHR\"{O}DINGER and the Heisenberg Uncertainty Relations Imply a General Physical Principle ?

It is explicitly shown that there exist physical states (normalized to 1) in which the Robertson- Schr\"{o}dinger and Heisenberg uncertainty relations are invalid, namely, the mean values of the physical operators are infinite. Consequently, these relations cannot imply a general physical principle. The explanation by the theory of functional analysis is given : for these states even the definition of the uncertainty notion through the dispersion notion in the probability theory is irrelevant.Comment: 4 pages, LaTeX, no figur

An investigation of customer order flow In the norwegian foreign exchange market

This thesis aimes at examining customer order flow in the Norwegian currency market (NOK/EUR). The key findings suggest heterogeneity among market participants, where non–financial customers’ order flow is the primary information source that drives price movements and foreign banks’ transaction flow provide liquidity in the market. However, the segments’ effect on price is non-permanent. Further evidence indicates that the transaction flow is complimentary to the traditional fundamentals when modeling the exchange rate. The out of sample findings indicate that order flow based models perform better than a random walk and a traditional model for statistical forecasts

Efficiency, investment and bank lending in transition and emerging economies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis studies the economic development in transition and emerging economies with focus on three particular economic issues: production efficiency, physical investment rate and bank lending under bank ownership perspective. The thesis chooses to study transition and emerging economies because they have undergone many important reform processes that may be thought of as experiments of different policy choices which lead to different economic outcomes. The thesis contributes to the literature in several ways. First, it adds to the literature on institutional economics and transition economies by confirming the significant role of institutional quality for efficiency and investment in a panel of transition economies. Better institutions are associated with higher efficiency levels and investment rates in transition economies. Given that investment is one of the key determinants of growth this means good institutions are important for growth in transition economies. Second, the thesis finds that banks of different ownership respond in remarkably different ways to monetary policies, which has important implication for the transmission and effectiveness of monetary policy. It also finds an asymmetric effect of monetary policy on bank lending with regard to the monetary conditions: in easy regime bank lending may not be affected my monetary tightening. This result calls for duly consideration of the ownership structure of the banking system when monetary policy and its effect on credit are studied. In summary, the thesis highlights the importance of institutional settings for economic development in transition and emerging economies

Reinforcement Learning in Stock Trading

Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading

Transistor-Like Spin Nano-Switches: Physics and Applications

Progress in the last two decades has effectively integrated spintronics and nanomagnetics into a single field, creating a new class of spin-based devices that are now being widely used in magnetic memory devices. However, it is not clear if these advances could also be used to build logic devices

Studying machine learning techniques for intrusion detection systems

Intrusion detection systems (IDSs) have been studied widely in the computer security community for a long time. The recent development of machine learning techniques has boosted the performance of the intrusion detection systems significantly. However, most modern machine learning and deep learning algorithms are exhaustive of labeled data that requires a lot of time and effort to collect. Furthermore, it might be late until all the data is collected to train the model. In this study, we first perform a comprehensive survey of existing studies on using machine learning for IDSs. Hence we present two approaches to detect the network attacks. We present that by using a tree-based ensemble learning with feature engineering we can outperform state-of-the-art results in the field. We also present a new approach in selecting training data for IDSs hence by using a small subset of training data combined with some weak classification algorithms we can improve the performance of the detector while maintaining the low running cost

Impact of Self – Congruity and Destination Image on Tourist Loyalty: Evidence from Recreation farm Tourism

Purpose: This paper's objective was to evaluate the impact of self-congruity and destination image on tourist loyalty at recreation farm destinations.    Theoretical framework: The relationship between self-congruity, destination image, and destination loyalty is based on the theory of social distance, have been studied for a long time (Liu et al., 2012). However, numerous questions regarding the unstable relationship between self-congruity and destination loyalty still need to be answered. More studies must evaluate the relationship between self-congruity, destination image, and visitors' loyalty to recreation farm destinations.    Design/methodology/approach: PLS-SEM method was applied. 270 valid questionnaires were qualified for analysis, accounting for 67.5% of the total questionnaires issued and satisfying the sample size required. The questionnaires were distributed and asked directly to visitors visiting the Ba Vi, Hanoi recreation farms from June to July 2022, when Hanoi began to open to visitors after two years closed by the COVID-19 pandemic.       Finding: The analysis found that destination image directly affects self-congruity, and both destination image and self-congruity directly influence visitor satisfaction and loyalty. Satisfaction also has a direct influence on tourist loyalty. The indirect relationships between destination image and self-congruity with tourist loyalty are indicated. Its results confirmed the role of mediators of satisfaction in this study and considered contributing to academic theory.   Research, Practical & Social implications: This study provides recommendations for tourism managers and marketers in improving the image of the destination and increasing satisfaction as well as improving the visitor return rate related to the Recreation Farm Tourism.   Originality/value: This study contributes significantly to the literature because it demonstrated an indirect relationship between destination image, self-congruity, and loyalty through satisfaction.

A STUDY ON PHYSICAL EXERCISES TO IMPROVE PHYSICAL FITNESS FOR FEMALE ATHLETES OF TUG OF WAR IN HO CHI MINH CITY, VIETNAM

The paper used routine methods in the field of physical education and sports to identify 6 assessment tests and 14 physical activities aiming to enhance level of physical fitness for tug of war athletes in Ho Chi Minh City. After the experiment, the results indicated that the 14 physical activities have positive impact on the participants’ fitness level. Article visualizations

Has the U.S.-Vietnam Bilateral Trade Agreement Led to Higher FDI into Vietnam?

In December 2001, a Bilateral Trade Agreement (BTA) came into effect that normalized economic relations between the United States and Vietnam. The resulting surge in trade surpassed most expectations. The impact of the BTA on FDI, however, has been less visible, especially with regard to U.S. FDI into Vietnam. This paper uses new data that accounts for FDI by U.S. subsidiaries resident in third counties to show that U.S. firms have been much more aggressive investors in Vietnam than normally reported in typical bilateral FDI data using Balance of Payments definitions of capital flows. While the U.S. is widely reported as the 11th largest investor into Vietnam, the new data shows that U.S.-related FDI exceeded all other countries in 2004. Although a formal model is not developed, descriptive data supports strongly the conclusion that the BTA has had a major impact on FDI into Vietnam, especially with regard to FDI from U.S. multinationals.FDI; Trade Agreement