Perils of Heavy Rainfall: Displacement and Resettlement Driven by Floods

Monsoon is typically a season to rejoice in South Asia because it cools off July\u27s hot summer weather. In the poetry of Sufi mystic Shah Abdul Latif Bhitai, the monsoon represents a time of abundance, and his verses are prayers of abundance for Sindh and the entire world as rainfall is indeed a much-awaited season to cast off dry spells of the desert. However, in the past few years, climate change has led to heavy floods and massive displacement of poor people in Sindh. This year, floods even reached Karachi\u27s urban city, the biggest metropolis of Pakistan, causing the displacement of 500,000 families and more than 1.2 million people. Amidst the outbreak of COVID-19, the displaced families face an even greater risk of being affected by the region\u27s spreading virus in 2020. The soundscape composition, Pitfalls of Heavy Rainfall, is based on field recordings collected from July to September 2020 in Karachi, my hometown. After teaching at Semester at Sea\u27s Spring 2020 voyage that unexpectedly ended in South Africa, I, as a temporary resident, was denied entry to Canada and continued teaching at the University of Alberta\u27s Faculty of Extension remotely. Going back to Pakistan after five years and living in Canada for more than 8-10 years as an international student, I have been experiencing displacement and reverse cultural shock alongside transformations due to COVID-19. I experienced a renewed appreciation of home and shelter after a growing sense of displacement due to COVID. This piece makes us vigilant of the inequities around us between children who can enjoy the rain and those who have to escape their homes to find shelter elsewhere. Rainfall does not make us all abundant. While some children have to save their goats and livestock and find another refuge because their houses have completely drowned, some children are still fortunate to be excited by the monsoon and singing to the skies and heavy wind

Health behaviors among Indian and Pakistani people living in the United States.

Cardiovascular disease is the first and third leading cause of death among men and women respectively in the United States (Centers for Disease Control, 2011a). According to the National Heart Lung and Blood Institute, within the next 10 to 15 years, Asian-Indians will account for 40 to 60% of people around the world with cardiovascular disease, of which 12% will be in the U.S. Asian Indians have been identified as one group who has a higher rate of cardiovascular disease compared to other minorities. There has been little research conducted identifying reasons why Asian Indians have higher rates of cardiovascular disease. These rates have severe public health and financial implications. The purpose of this research was to determine what health behaviors may lead to this high prevalence of cardiovascular disease. The hypothesis was that lack of physical activity and length of time living in the U.S. contribute the most to a higher risk of morbidity, mortality, and cardiovascular disease among people from India and Pakistan living in the United States for at least 10 years. A survey comprised of questions about physical activity, cardiovascular health, food and vegetable consumption, and level of acculturation was given to a group of Indian and Pakistani people residing in the U.S. for at least 10 years. This study was a pilot study and had a descriptive design. The significance or alpha level for this study was set at 0.05. Pearson's correlation tests were used to assess relationships between multiple variables; no significance was found in this cohort. Independent samples t-tests were used to assess differences in variables among men and women; no significance was found in this cohort; however, a trend towards significance was found when looking for differences between men and women's acculturation scores. Very little research exits related to cardiovascular risk within this cohort; the findings from this study will help enhance the body of knowledge in this area

Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score

Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sample summary-data MR design. We show a linear model for the observed associations approximately holds in a wide variety of settings when all the genetic variants satisfy the exclusion restriction assumption, or in genetic terms, when there is no pleiotropy. In this scenario, we derive a maximum profile likelihood estimator with provable consistency and asymptotic normality. However, through analyzing real datasets, we find strong evidence of both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model of complex traits that is recently proposed in genetics. We model the systematic pleiotropy by a random effects model, where no genetic variant satisfies the exclusion restriction condition exactly. In this case we propose a consistent and asymptotically normal estimator by adjusting the profile score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted profile score. We demonstrate the robustness and efficiency of the proposed methods using several simulated and real datasets.Comment: 59 pages, 5 figures, 6 table

Vesyla-II: An Algorithm Library Development Tool for Synchoros VLSI Design Style

High-level synthesis (HLS) has been researched for decades and is still limited to fast FPGA prototyping and algorithmic RTL generation. A feasible end-to-end system-level synthesis solution has never been rigorously proven. Modularity and composability are the keys to enabling such a system-level synthesis framework that bridges the huge gap between system-level specification and physical level design. It implies that 1) modules in each abstraction level should be physically composable without any irregular glue logic involved and 2) the cost of each module in each abstraction level is accurately predictable. The ultimate reasons that limit how far the conventional HLS can go are precisely that it cannot generate modular designs that are physically composable and cannot accurately predict the cost of its design. In this paper, we propose Vesyla, not as yet another HLS tool, but as a synthesis tool that positions itself in a promising end-to-end synthesis framework and preserving its ability to generate physically composable modular design and to accurately predict its cost metrics. We present in the paper how Vesyla is constructed focusing on the novel platform it targets and the internal data structures that highlights the uniqueness of Vesyla. We also show how Vesyla will be positioned in the end-to-end synchoros synthesis framework called SiLago

E-Bayesian and Hierarchical Bayesian Estimation of Hazard rate for Kumaraswamy Distribution

In this article, with the use of the Bayesian, Expected Bayesian, and H-Bayesian estimation methods, the shape parameter and hazard rate of the Kumaraswamy distribution are calculated. Three separate loss functions the Squared Error loss function(SELF), the Precautionary loss function(PLF), and the Asymmetry loss function(ALF) along with an informative prior the Gamma prior are used to provide the estimates . The definitions and properties of the proposed estimators are given. Monte Carlo simulation is used to compare all of the estimates in terms of mean square error (MSE). Taking data from the real world, different estimation methodologies’ efficacy has also been studied. Numerical analyses show that the E-Bayesian estimates outperform the Bayesian and Hierarchical estimates