Development of ship financing : a study of the 2008 financial crisis

This thesis looks to examine the period before and after the financial crisis of 2008 in order to identify any potential shifts in ship financing. For our period of analysis, we defined the pre period from the start of 2005 until the end of august 2008, while the post period is defined as the period from September 2008 until the end of 2012. In our empirical analysis we have used inferential statistics to test our predictions. The data used have been gathered from two world-renowned shipping information providers, Clarksons and Marine Money. By pooling and later segmenting the provided data, we have created our own database, tailored for our research questions. Our analysis shows that there has indeed been a shift from the traditional financing source of bank loans towards corporate bonds. By the end of 2012, bond issuance stood for almost 45% of ship financing, up 40% from the start of the sample. Such a shift also involved a change in location of funding, with Asia and Scandinavia providing significantly greater number of debt issuances in the aftermath of the financial crisis, while North America, Europe and the Middle East experienced a deterioration of their funding proportions. In addition, the use of public equity markets as means of financing has greatly declined, resulting in a greater reliance on debt in the post period. Given the increased importance of bonds, the authors have also examined this instrument in more detail. Our findings show that bondholders demand higher return and are less willing to engage in long-term commitments in the post period, as a result of the greater market uncertainty. Such an uncertainty has also caused banks to alter their lending practice, with a greater focus on risk mitigation. Our takeaway from our analysis is quite extreme, with a severe change in ship financing over the last eight years. Looking into the future, we do believe that the ship financing picture has changed permanently, but in a less radical way than what we have observed in our sample. We expect bonds to take a larger part in ship financing, nevertheless, we still expect bank loans to be the primary source of capital

A survey of machine learning-based methods for COVID-19 medical image analysis

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.

Serrate RNA effector molecule (SRRT) is associated with prostate cancer progression and is a predictor of poor prognosis in lethal prostate cancer

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRT's potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.Prostate Cancer Foundation Young Investigator Award ; Prostate Cancer Canada ; Canadian Cancer Society (CCS

MCNN-LSTM: Combining CNN and LSTM to classify multi-class text in imbalanced news data

Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN's are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets

NUMERICALLY COMPUTABLE BOUNDS FOR THE RANGE OF VALUES OF INTERVAL POLYNOMIALS

A central problem in interval analysis is the computation of the range of values of an interval polynomial over an interval. This problem has been treated by Dussel and Schmitt [1] and, disregarding the computational cost of their algorithm, solved in a satisfactory manner. In this paper we will discuss two algorithms by Rivlin [4] (see also Cargo and Shiska [2]) where the accuracy of the bounds depend on the amount of work one is willing to do. The first algorithm is based upon the expression of a polynomial in Bernstein polynomials. This algorithm as given by Rivlin [4] is valid for an estimate over the interval [0,1]. We will generalize the algorithm to an arbitrary finite interval and we will show that it is an appropriate algorithm if the width of the interval is not too large. The second algorithm is based upon the mean value theorem. As stated by Rivlin [4] it is valid for the interval [0,1]. We will generalize the algorithm so that it is valid for any finite interval. The algorithms are then generalized to interval arithmetic versions. Finally we compare the algorithms numerically on several polynomials.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]

Development of ship financing : a study of the 2008 financial crisis

This thesis looks to examine the period before and after the financial crisis of 2008 in order to identify any potential shifts in ship financing. For our period of analysis, we defined the pre period from the start of 2005 until the end of august 2008, while the post period is defined as the period from September 2008 until the end of 2012. In our empirical analysis we have used inferential statistics to test our predictions. The data used have been gathered from two world-renowned shipping information providers, Clarksons and Marine Money. By pooling and later segmenting the provided data, we have created our own database, tailored for our research questions. Our analysis shows that there has indeed been a shift from the traditional financing source of bank loans towards corporate bonds. By the end of 2012, bond issuance stood for almost 45% of ship financing, up 40% from the start of the sample. Such a shift also involved a change in location of funding, with Asia and Scandinavia providing significantly greater number of debt issuances in the aftermath of the financial crisis, while North America, Europe and the Middle East experienced a deterioration of their funding proportions. In addition, the use of public equity markets as means of financing has greatly declined, resulting in a greater reliance on debt in the post period. Given the increased importance of bonds, the authors have also examined this instrument in more detail. Our findings show that bondholders demand higher return and are less willing to engage in long-term commitments in the post period, as a result of the greater market uncertainty. Such an uncertainty has also caused banks to alter their lending practice, with a greater focus on risk mitigation. Our takeaway from our analysis is quite extreme, with a severe change in ship financing over the last eight years. Looking into the future, we do believe that the ship financing picture has changed permanently, but in a less radical way than what we have observed in our sample. We expect bonds to take a larger part in ship financing, nevertheless, we still expect bank loans to be the primary source of capital

Practical and theoretical studies in numerical error analysis

Bibliography: p. 74-76