Analysis of Credit Risk and Single / Two Factor Model

Since 2008, businesses and banks must manage and track more risk than ever before. Financial risk management helps companies and banks decrease the risk of investment and trade. Additionally, financial risk management gives a guide on how to forecast and manage the risk efficiently. More specifically, the three major risks are market risk, credit risk, and operational risk. This report will focus on the credit risk: introducing the definition of credit risk, single factor model, the relationship between coefficient and default probability, and the relationship of m coefficient and default probability. Using the single factor model, we will extend the definition and application to the double factor model. Furthermore, the coding will be provided

High speed in-process defect detection in metal additive manufacturing

Additive manufacturing (AM) is defined as the process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing technologies. This fabricating technique is also famously known as ‘3D printing’. Although its entire manufacturing chain is becoming more mature by improved pre-defined design, more accurate heat input and motion system and cleaner in-chamber atmosphere, there are still a number of influential factors that can have a negative impact on the manufacturing process that introduce ‘defects’, which will greatly lessen the density of the parts or even result in failure. For this reason, it is critical to be able to discover them effectively during the manufacturing process. This thesis aims to develop a methodology for the measurement and characterisation of surface texture of AM parts. Typically, optical metrology instruments including focus variation (FV) microscopy and fringe projection (FP) have been used to measure the surface texture of AM samples due to their suitability and reliability in the field of metrology. The thesis also develops optimum filtration methodology to characterise the AM surface by comparing different filters. In the recent decades, machine learning (ML) is presenting a high robustness and applicability in defect detection in comparison to the traditional digital image processing technique. In this thesis, several ML techniques have been investigated into in terms of their suitability for the research based on the processed data secured from the optical measuring instrument. A detailed defect review that collects the information in terms of the defects in LPBF process based on the related research of the global researchers is given. It provides the details about different types of defects and discusses the potential correlation between process parameters and generated defects. ML and AM are both research fields that have developed rapidly in recent decades. In particular, the combination of the two can effectively achieve the purpose of AM parameter optimisation, process control and defect detection. A review of the adaptability of ML to different types of data and its application in feature extraction to achieve in-line or offline defect detection is given. Specifically, it demonstrates how to select proper ML technique given various types of data and how to choose appropriate ML model depending on different forms of defect detection (defect classification and defect segmentation). For data acquisition, the parameters including the magnification of objective lens and illumination source of the optical instrument are optimised to provide accurate and reliable data. Then the surface is pre-processed and filtered with the discovered optimal filtration method. The applicability of different types of machine learning methods for defect detection is also investigated. Results show that principal component analysis may not be a suitable tool for classifying defects if using exclusively whereas convolutional neural network and U-Net (full convolutional network) have shown good performance in correctly classifying defects and segmenting defects from the measured surface. For future work, more measurement instruments which can potentially achieve efficient and accurate metrology can be considered being developed and used, and the variety of samples needs to be increased to provide more types of surface topographies. In addition, how to improve the applicability of PCA in defect classification for AM parts can be studied on and more values of hyperparameters and number of parameters of neural networks can be used to further improve the suitability of the model for the training data

Environmental Factors Override Dispersal-Related Factors in Shaping Diatom and Macroinvertebrate Communities Within Stream Networks in China

Metacommunity theory provides a useful framework to describe the underlying factors (e.g., environmental and dispersal-related factors) influencing community structure. The strength of these factors may vary depending on the properties of the region studied (e.g., environmental heterogeneity and spatial location) and considered biological groups. Here, we examined environmental and dispersal-related controls of stream macroinvertebrates and diatoms in three regions in China using the distance-decay relationship analysis. We performed analyses for the whole stream network and separately for two stream network locations (headwater and downstream sites) to test the network position hypothesis (NPH), which states that the strength of environmental and dispersal-related controls varies between headwater and downstream communities. Community dissimilarities were significantly related to environmental distances, but not geographical distances. These results suggest that communities are structured strongly by environmental filtering, but weakly by dispersal-related factors such as dispersal limitation. More importantly, we found that, at the whole network scale, environmental control was the highest in the regions with highest environmental heterogeneity. Results further showed that the influence of environmental control was strong in both headwaters and downstream sites, whereas spatial control was generally weak in all sites. This suggests a lack of consistent support for the NPH in our studied stream networks. Moreover, we found that local-scale variables relative to basin-scale variables better explained community dissimilarities for diatoms than for macroinvertebrates. This indicates that diatoms and macroinvertebrates responded to environment at different scales. Collectively, these results suggest that the importance of drivers behind the metacommunity assembly varied among regions with different level of environmental heterogeneity and between organism groups, potentially indicating context dependency among stream systems and taxa.Peer reviewe

Localization in GPS denied environment

No abstract available

The emerging role of cellular senescence in renal diseases

Cellular senescence represents the state of irreversible cell cycle arrest during cell division. Cellular senescence not only plays a role in diverse biological events such as embryogenesis, tissue regeneration and repair, ageing and tumour occurrence prevention, but it is also involved in many cardiovascular, renal and liver diseases through the senescence-associated secretory phenotype (SASP). This review summarizes the molecular mechanisms underlying cellular senescence and its possible effects on a variety of renal diseases. We will also discuss the therapeutic approaches based on the regulation of senescent and SASP blockade, which is considered as a promising strategy for the management of renal diseases

Absorption-based algorithm for satellite estimating the particulate organic carbon concentration in the global surface ocean

Particulate organic carbon (POC) in the surface ocean contributes to understanding the global ocean carbon cycle system. The surface POC concentration can be effectively detected using satellites. In open oceans, the blue-to-green band ratio (BG) algorithm is often used to obtain global surface ocean POC concentrations. However, POC concentrations are underestimated in waters with complex optical environments. To generate a more accurate global POC mapping in the surface ocean, we developed a new ocean color algorithm using a mixed global-scale in situ POC dataset with the concentration ranging from 11.10 to 4389.28 mg/m3. The new algorithm (a-POC) was established to retrieve the POC concentration using the strong relationship between the absorption coefficient at 490 nm (a(490)) and POC, in which a(490) was from the Ocean Color Climate Change Initiative (OC-CCI) v5.0 suite. Afterward, the a-POC algorithm was applied to OC-CCI v5.0 data for special regions and the global ocean. The performances of the a-POC algorithm and the BG algorithm were compared by combining the match-ups of satellite data and in situ dataset. The results showed that the statistical parameters of the a-POC algorithm were similar to those of the BG algorithm in the Atlantic oligotrophic gyre regions, with a median absolute percentage deviation (MAPD) value of 22.04%. In the eastern coastal waters of the United States and the Chesapeake Bay, the POC concentration retrieved by the a-POC algorithm was highly consistent with the match-ups, and MAPD values were 33.06% and 26.11%. The a-POC algorithm was also applied to the Ocean and Land Color Instrument (OLCI) data pre-processed with different atmospheric correction algorithms to evaluate the universality. The result showed that the a-POC algorithm was robust and less sensitive to atmospheric correction than the BG algorithm