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Trade Liberalization, Poverty, Income Inequality in India
No full textThis paper is a study on the impact of India's trade liberalization as part of its economic reforms and structural adjustment programs initiated during the 1990s on poverty and income inequality. Major reforms: 1. Trade liberalization 2. Financial liberalization 3. Privatization 3. Tax reforms 4. Inflation control measures 5. Foreign investment 6. Agriculture developmen
Gambler's Fallacy: A Gambler's Dilemma
No full textThe gambler’s fallacy, also known as the Monte Carlo fallacy or the fallacy of the maturity of chances, is the mistaken belief that, if something happens more frequently than normal during a given period, it will happen less frequently in the future. It may also be stated as the belief that, if something happens less frequently than normal during a given period, it will happen more frequently in the future. In situations where the outcome being observed is truly random and consists of independent trials of a random process, this belief is false. The fallacy can arise in many situations, but is most strongly associated with gambling, where it is common among players
Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection
No full textEarly prediction of disease outbreaks and seasonal epidemics such as Influenza may reduce their impact on daily lives. Today, the web can be used for surveillance of diseases.Search engines and Social Networking Sites can be used to track trends of different diseases more quickly than government agencies such as Center of Disease Control and Prevention(CDC). Today, Social Networking Sites (SNS) are widely used by diverse demographic populations. Thus, SNS data can be used effectively to track disease outbreaks and provide necessary warnings. Although the generated data of microblogging sites is valuable for real time analysis and outbreak predictions, the volume is huge. Therefore, one of the main challenges in analyzing this huge volume of data is to find the best approach for accurate analysis in an efficient time. Regardless of the analysis time, many studies show only the accuracy of applying different machine learning approaches. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. The aim of this study is to propose an efficient and accurate framework that uses SNS data to track disease outbreaks and provide early warnings, even for newest outbreaks accurately. The presented framework of outbreak prediction consists of three main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module utilizes the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText and six conventional machine learning algorithms, are evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers have been trained and tested using a pre-labeled dataset of flu-related and unrelated Twitter postings. The selected text classifier is then used to classify over 8,400,000 tweet documents. The flu-related documents are then mapped ona weekly basis using a mapping module. Lastly, the mapped results are passed together with historical Center for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. The evaluation of flu tweet classification shows that FastText together with the extracted features, has achieved accurate results with anF-measure value of 89.9% in addition to its efficiency. Therefore, FastText has been chosen to be the classification module to work together with the other modules in the proposed framework, including the linear regression module, for flu trend predictions. The prediction results are compared with the available recent data from CDC as the ground truth and show a strong correlation of 96.2%
Visualization and Analysis of Air Pollution in US East Coast Cities
No full textAir pollution has negative impact on human health and leads to many chronic diseases. U.S. Environmental Protection Agency (EPA) has been closely monitoring the air pollution using its ground stations in various locations around the nation. The collected data has been included in its air pollution database and made publically available in its website. The detailed daily air pollutant concentrations (e.g. PM2.5, PM10, SO2, CO, Pb, NO2, Ozone) can be downloaded in Excel format. In this poster, we visualize and analyze the air pollution in the US East Coast in the past years using Tableau software. Such visualization allows us to observe the trend of air pollution and its transmission pattern in major cities of the east coast. The correlations between air pollution and various conditions (e.g. traffic, season, location) are discussed. The influence of various terrain conditions to the PM2.5 pollutant diffusion is explored. The visualization and analysis of air pollution data helps better understand its mechanism and distribution pattern
The Impact of the Financial Reform in China
No full textChina has issued many policies to implement financial liberalization such as deregulation in the bank sector, refinements in financial markets, and allowing more freedom for Chinese and foreign investors to participate and interact domestically and overseas (Lee 2012). The purpose of these policies is to connect Chinese financial market with global markets closely. Here, we study the impact of the monetary policy issued by the People’s Bank of China in Nov. 2016 for allowing global investors to access the Chinese stock markets directly
A Memoir of No Memory: Rethinking Self Analysis and Navigating Medical Narratives
Get PDFFaculty Research Day 2018: Faculty Competitive Poster WinnerMy sabbatical project explores the themes of health, wellness, identity, and creative voice. In a series of essays, I am investigating and writing about life events—both traumatic and ordinary—and their effects on memory, personal psychology, and the choices I make as a writer. The first of these essays describes and analyzes an illness I endured in 1992. I spent over three months in a "prolonged coma," and an additional six months in rehabilitation. This research project brought me back into that world of hospitals, tests, diagnoses, and jargon. Having no concrete recollection of those summer months, I began with one central question: How can I write a memoir about something I can’t remember? My explorations became the focus of The Comet's Tail: A Memoir of No Memory (Homebound Publications, 2018). The challenges of subjectivity forced me to question my duty as a memoirist and whether impartiality is ever really possible. I had to piece together three threads—journal entries written before I got sick, medical notes transcribed at the time, and emerging memories after the event. Examining these various accounts forced me to confront new questions of perspective and documentation. How much was true record and how much was inaccurate recollection of witnesses or imaginative invention? How is memory formed, and what is the role of trauma in our ability to reconstruct and understand the past
Efficient Actor Recovery Paradigm For Wireless Sensor And Actor Networks
No full textWireless sensor networks (WSNs) are becoming widely used worldwide. Wireless Sensor and Actor Networks (WSANs) represent a special category of WSNs wherein actors and sensors collaborate to perform specific tasks. WSANs have become one of the most preeminent emerging type of WSNs. Sensors with nodes having limited power resources are responsible for sensing and transmitting events to actor nodes. Actors are high-performance nodes equipped with rich resources that have the ability to collect, process, transmit data and perform various actions. WSANs have a unique architecture that distinguishes them from WSNs. Due to the characteristics of WSANs, numerous challenges arise. Determining the importance of factors usually depends on the application requirements. The actor nodes are the spine of WSANs that collaborate to perform the specific tasks in an unsubstantiated and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power fatigue of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. It is essential to keep inter-actor connectivity in order to insure network connectivity. Thus, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). For network recovery process from actor node failure, optimal re-localization and coordination techniques should take place. In this work, we propose an efficient actor recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balances the network performance. The packet is handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets (Either from actor or sensor). This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, we compare the performance of our proposed work with state-of the art localization algorithms. Our experimental results show superior performance in regards to network life, residual energy, reliability, sensor/ actor recovery time and data recovery
Interview with James H. Halsey and Julia Halsey
No full textAn undated interview with James H. Halsey and Julia Halsey on the history of the University of Bridgeport
Accessory Piriformis Muscle
No full textEmily Scholl, Michael Kellner, David R. Terfera, and Kevin R. Kelliher's poster discussing the piriformis muscle.Faculty Research Day 2018: Doctoral Student Poster 2nd Plac