General Assessment of the Impact of the War in Ukraine on the Shipping Industry Using Parametric Methods

The ongoing Ukrainian war has introduced significant uncertainty and crisis into the global economy, particularly in financial and stock markets. This study is a part of a larger research project that aims to assess the impact of the conflict between Russia and Ukraine on shipping companies, given the direct influence of rising gas and oil prices on the valuation of freight transport service providers. Economic sanctions were imposed on Russia, leading to the destabilization of the global economy. Industries such as the global shipping and tourism sectors experienced significant declines in share value. Investors began reallocating their portfolios, seeking safer and less risky investments, such as gold stocks. The riskiness of a particular stock can be assessed by various methods, including volatility measurement. This paper focuses on calculating and presenting the volatility value of the shares of A. P. Moller-Maersk, the world\u27s largest operator of container lines and vessels. Additionally, the STOXX50 index, i.e. the Euro Zone stock market index, representing the overall European market, is used for comparison. Another risk measure discussed in this paper is Value at Risk (VaR), a quantitative method used to predict potential cash losses over a certain time period. The parametric method of calculating VaR was used, which assumes the normal distribution of stock value fluctuations. VaR was calculated using historical stock price data of A. P. Moller-Maersk. Findings indicate significant volatility and high-risk environment in the financial markets. The calculated VaR of 27.66% for a 30-day period with the 95% confidence level reflects the substantial potential losses associated with investing in Maersk shares during the crisis, surpassing typical risk levels. In conclusion, the war in Ukraine has disrupted the maritime industry, which was already recovering from the impact of the COVID-19 pandemic. The sanctions imposed on Russia and the war situation in Ukraine have created uncertainty and turbulence in financial markets, prompting investors to seek safer investment options. The study emphasizes the need for continued monitoring of the impact of the war on the global maritime industry. The devastating effects of the war on the sector have significant implications for the global economy, human well-being, and future research in the field

Probabilistic Analysis of Pair Wise Gene Interactions Using Support Vector Clustering

The most difficult challenge in genetic epidemiology is to characterize the gene interactions that affect a complex disease. DNA microarray has made it easier for engineers to study gene expression profiles of numerous genes by describing the complete genomic activity, but extraction of useful data without losing information poses a major challenge. Various clustering algorithms have been applied to these microarray profiles to identify the gene interactions based on various factors such as a stimuli or genes affecting a disease. However, only a few of them have been applied to find the interactions between the genes in the same cluster. Several methods have been used to predict complex gene networks, and they have been largely successful, but it cannot be inferred that the pair of genes interact every time. Gene interactions can be affected by various environmental factors, stimuli, or inactivating genes. This thesis aims to address this challenge by proposing a method that provides a probabilistic analysis of the interaction between a pair of genes. The proposed method uses Support Vector Clustering to classify a pair of genes, and the clusters formed are used to analyze their interaction. The algorithm is tested using yeast microarray data. The results found are validated using biological literature surveys

Probabilistic Analysis of Pair Wise Gene Interactions Using Support Vector Clustering

The most difficult challenge in genetic epidemiology is to characterize the gene interactions that affect a complex disease. DNA microarray has made it easier for engineers to study gene expression profiles of numerous genes by describing the complete genomic activity, but extraction of useful data without losing information poses a major challenge. Various clustering algorithms have been applied to these microarray profiles to identify the gene interactions based on various factors such as a stimuli or genes affecting a disease. However, only a few of them have been applied to find the interactions between the genes in the same cluster. Several methods have been used to predict complex gene networks, and they have been largely successful, but it cannot be inferred that the pair of genes interact every time. Gene interactions can be affected by various environmental factors, stimuli, or inactivating genes. This thesis aims to address this challenge by proposing a method that provides a probabilistic analysis of the interaction between a pair of genes. The proposed method uses Support Vector Clustering to classify a pair of genes, and the clusters formed are used to analyze their interaction. The algorithm is tested using yeast microarray data. The results found are validated using biological literature surveys