The continuum of HIV care is highly complex. It includes prevention, testing, patient care, treatment, and support services. This project will help Providence Health Care utilise its limited resources to provide the best treatment and care for people living with HIV in Vancouver. Care for HIV patients includes antiretroviral therapy, treatment of co-morbidities, monitoring clinical markers of disease progression (CD4 count and viral load), and support services to ensure treatment adherence and retention in care.
User intention prediction of event data, collected during the business process, has become an important topic in Web Analysis and Business Intelligence. Commercial organizations have realized its importance for providing cost-effective opportunities to improve their decision-making in digital marketing strategies. We aim to develop and implement a statistical prediction model to make the prediction of user intention, using the retroactive video tracking data, while the anonymous customer navigates on the website.
Solar cells which convert solar energy directly into electricity are among one of the most viable solutions to the worlds foreseeable energy crisis and global environmental issues. One key strategy to improve the efficiency of solar cells is to enhance the overlap between their absorption spectra and the solar spectrum. When two or more subcells with distinct and complementary absorption spectra are stacked, the tandem solar cells are created and a broader range of the solar spectrum can be absorbed and more solar energy can be harvested.
Monte Carlo simulation methods are commonly used as a risk management tool to estimate the risk exposure of financial asset portfolios. However, the traditional brute-force Monte Carlo (BFMC) method is often very timeconsuming, which makes it difficult to serve the risk management needs of modern insurance industry. An alternative approach, the least-squares Monte Carlo (LSMC) method, could substantially reduce the computation cost by fitting a proxy function of liabilities using simple nonlinear regression methods.
In recent years, improvements in technology provide the opportunity for investors to use computer algorithms to produce low-cost guidance on possible portfolio investment mixes and strategies. This project is directed at the research and development of one such Robo- Advisor algorithm based on forward-looking scenario optimization, in order to determine the efficacy of the strategy. Here optimal portfolios are selected based on investors views on future scenarios, goals and risk tolerance.
Natural disasters that are associated to the atmosphere (known as atmospheric perils) such as hurricanes, tornadoes and hail, flooding, drought, and wildfire, caused over $100 billion in damage throughout the world in 2015. Insurance companies often cannot afford to be responsible when such catastrophes occur, and so they purchase insurance to protect themselves (called reinsurance) from these large risks.
The goal of this project is to create a statistical model to forecast the future price of steel, which will rely on sector indexes and material prices. We will identify which variable has the most explanatory power. Multiple models will be created to identify the one that performs best. In order to increase the accuracy of the information generated by the model, risk forecasting will be added. The resulting model is meant to aid internal buyers in decision making. As our partner buys over 100 M USD worth of steel annually, an improvement in profits will be of great benefit to him.
Traditional insurance models build on the assumption of independence of risks. One of the main causes of the recent financial crisis, this assumption has facilitated the quantification of risks for decades, but it has often lead to risks' under-estimation and as a result under-pricing. Importantly, one of the prime pillars of the novel concept of Enterprise Risk Management is the requirement that insurance companies have a clear understanding of risks' interconnections within the risk portfolios. However, modeling dependence is not an easy call.
Machine learning is the concept where a computer can be trained to recognize data and predict future outcomes based on the trends that exist in the data. This method of analysis has not been used on engine data, specifically in-line oil. Oil is an engines lifeblood and a lot of data can be collected and engine health can be predicted based on these measurements. This project aims to deploy machine learning concepts in the area of engine failure prediction. A special sensor equipped with the machine learning algorithm will be able to report all vital signs of an engine in a matter of minutes.
Due to the current economic downturn, especially the lower crude oil price, the drilling success rate become the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Deep Treasure Corp wishes that through the combination of mature hydrocarbon prediction techniques and new research results in seismic inversion, the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided in Roncott field, which will improve the success rate in drilling.