Haque, Waqar

Policy mobility and transfer play a role in larger policy development and implementation processes, as actors look elsewhere for policy solutions to local issues. Conducted in collaboration with the Nadleh Whut’en First Nation and guided by an Indigenous methodology based on the Too Declaration with support from constructivist grounded theory, this research explores the mobilization and transfer of the Yinka Dene Water Law. Conversations with participants representing First Nations and interest groups reveal that the Water Law is being mobilized by individuals and groups at a variety of scales, and use varies from adoption and implementation, to inquiring about its transfer. This research also discusses factors influencing Water Law transfer, including shared policy problems and its function as a communication tool. There are few empirical examples of policy transfer processes between Indigenous contexts. This research contributes to filling this gap in the policy transfer literature by exploring such transfer between First Nations, and advances Nadleh Whut’en’s stewardship and implementation related objectives.

We examine the relationship between stock returns and components of idiosyncratic volatility—two volatility and two covariance terms— derived from the decomposition of stock returns variance. The portfolio analysis result shows that volatility terms are negatively related to expected stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. These relationships are robust to controlling for risk factors such as size, book-to-market ratio, momentum, volume, and turnover. Furthermore, the results of Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.

Diabetes is one of the most common chronic diseases in the world. Diabetic patients are also more susceptible to develop additional comorbidities over time even causing death. This makes it essential to identify the risk of developing comorbidities as early as possible for effective diabetes management and to reduce the burden on healthcare system. Large volumes of clinical data which has been collected over the years has potential to be translated into meaningful information to enable healthcare professionals gain insights into diabetic patient comorbidities. This research has two key contributions. First, an interactive diabetes dashboard is developed in which the data is integrated and shown in the form of visually appealing charts, graphs and tables. The dashboard displays aggregated results with drilldown capabilities to allow navigation at finer granularities of various metrics. Second, predictive models are built to forecast the likelihood of one of the three common comorbidities for diabetic patients – Benign Hypertension, Congestive Heart Failure, and Acute Renal Failure. The models use advanced data mining algorithms such as Logistic Regression, Neural Network, CHAID, Bayesian Network, Random Forest and Ensemble. Results from these models are also incorporated into an interactive assessment tool that has the ability to take user input and predict the likelihood of one of these comorbidities. Northern Health (NH) dataset consisting exclusively of diabetic patients is used for this research.

Tracking the effects of air pollution from industries is important for developing management strategies under changing emissions. However, computational tools for air pollution assessment often do not elucidate modeling uncertainty, making it difficult for environmental policy-makers to know how much confidence to put in model results, which also hampers aspects that may need improving. This study examined how the WRF-SMOKE-CMAQ modeling system with various planetary boundary-layer (PBL) schemes and atmospheric datasets mimics the local meteorology, air quality and acidic deposition at 1 km horizontal resolution over the industrializing Terrace-Kitimat Valley of northwestern British Columbia. Quantitative and qualitative correspondence of model outputs with observational data varied with station location, the nature of pollutant emissions, and quantity of chemical species. Valid model outputs were used to delineate present compliance with objectives on ambient fine particulate matter, and baseline exceedance of critical loads of sulfur and nitrogen deposition for the forest ecosystem. Spatial impacts of anticipated industrial emissions on the environment were also assessed. An additional 15 tonnes day-1 permissible SO2 emission from an aluminum smelter in Kitimat was projected to result in 50–88 % increase in aerial exceedance of the limit for protection of lichen, and 37–67 % increase in spatial exceedance of acidic deposition to soils. Cumulatively, 16–18 km2 of plant habitat, and 10–11 km2 of soil in an area contiguous with the smelter site will likely be damaged by its SO2 emission under the latest regulation. Should two Liquefied Natural Gas projects commence operations, cumulative NOx concentrations are expected to remain below harmful levels, while pre-existing areal exceedance of nitrogen deposition will barely increase (0–1 km2). An additional 4 km2 area will be exposed to SO2 concentrationsiii that are directly harmful to vegetation, while 13–14 km2 total area with an average of 29.7–35.0 kg ha-1 yr-1 excess sulfur deposition was estimated. These projections assumed all future emissions of NOx, SO2 and other air pollutants will be from elevated point sources.

Suicide is a global health issue that involves the biological, social, cultural, spiritual, and psychological state of an individual in addition to many other factors which interact and lead a person to Suicidal Ideation (SI) and Suicide Attempt (SA). Over the last decade, with the advent of large medical databases, there has been a tremendous rise in the use of Business Intelligence (BI) in the healthcare sector. Healthcare uses BI tools to transform raw data into meaningful information to extract the potential value of historical data. Timely diagnoses of mental health problems can assist experts to address it at an early stage and enhance patient’s quality of life. There is a critical need to examine the fundamental psychological well-being issues among the worldwide population, which may develop into more complex issues, if not considered at an early period. This research focuses on two main components: Data visualization and Predictive model. First, a mental health dashboard is created using an end to end approach in which mental health data is pre-processed, integrated, and visualized in the form of several reports. These reports display the aggregated results in visually appealing formats (i.e., graphs, tables, pie charts, and line graphs) and allow navigation to finer granularity reports via drill down and drill through reports. Second, a predictive model is built to forecasts Suicide Attempts (SA). Ontario Mental Health Reporting System (OMHRS) database obtained from CIHI (Canadian Institute for Health Information) is used to train and test the predictive model. This model uses advanced data mining algorithms, including Artificial Neural Networks, Decision trees, and regression. The outcomes of different data mining algorithms are compared with actual values to determine the accuracy of the model. In addition, a web form is created, which takes input from the user and calculates the probability of SA for a given patient. The objective of this research is to provide a better understanding of trends, outliers, and patterns to enable healthcare providers to make more informed decisions and decrease mortality rate due to suicide.

Due to an exponential increase in number of electronic documents and easy access to information on the Internet, the need for text summarization has become obvious. An ideal summary contains important parts of the original document, eliminates redundant information and can be generated from single or multiple documents. There are several online text summarizers but they have limited accessibility and generate somewhat incoherent summaries. We have proposed a Graph-based Automatic Summarizer (GAUTOSUMM), which consists of a pre-processing module, control features and a post-processing module. For evaluation, two datasets, Opinosis and DUC 2007 are used and generated summaries are evaluated using ROUGE metrics. The results show that GAUTOSUMM outperforms the online text summarizers in eight out of ten topics both in terms of the summary quality and time performance. A user interface has also been built to collect the original text and the desired number of sentences in the summary.