Behara, Ravi

This empirical study examines decision-making in project selection in the face of overwhelming flood infrastructure needs and inadequate resources, particularly in vulnerable communities. The motivation for this study is to explore the interconnectedness between socioeconomic dimensions and environmental risks in the decision-making process for selecting projects. The study evaluates the Palm Beach County project selection framework and the impact of multi-criteria decision-making on project selection by proposing a new framework. The new project selection framework emphasizes the integration of flood risk and social vulnerability index criteria to evaluate the relationship between the new criteria in the decision-making framework and project selection.
The analysis is comprised of 24 models grouped into three distinct groups and compared using paired t-tests. The analysis reveals that of the three groups, the group which incorporates both flood risks and social vulnerability criteria consistently outperforms the others, demonstrating its effectiveness in providing a more equitable investment for vulnerable communities that are more susceptible to floods. The findings provide valuable insights and recommendations for practitioners and scholars, emphasizing the need for a theoretical framework with objectivity to guide optimal infrastructure investments for decision makers.

This is an explanatory research study. The purpose of the research is to evaluate performance of the traveling industry through statistical analysis. In the developing world of global economies, people travel on a regular basis. Travelers tend to complain about the Hotel Industry and how it is manipulating prices for their clientele. AirB&B is disrupting the hotel industry by allowing private owners to rent out their properties for short periods of time with less risk. Currently, we have obtained data to prove that AirB&B is satisfying their customers better than regular hotel industry. <br/>We will be comparing how does the number of bedrooms, the accommodations, the price and minimum stay requirements affecting the overall satisfaction of the traveling clientele. We will attempt to compare this data to how the room type effects the reviews and overall satisfaction to all other hotel ratings. <br/>

On March 11, 2011 there was an underwater earthquake off the Japanese coastline, and the resulting
tsunami impacted Japanese firms. That impact also rippled through supply chains across the globe.
The electronics industry was significantly impacted because Japan plays a prominent role in the global
electronics industry. This study investigates the impact of the disruption caused by the Tsunami in the
global electronic industry supply chain. Specifically, Bloomberg financial data was analyzed to
understand the relationships involved in this supply chain, and then used to investigate the impact on
key companies in that chain. The study linked companies in Japan, Taiwan, and the United States, and
analyzed the impact of the Tsunami on their immediate and short term stock prices. Results showed
the expected immediate negative impact on the stock prices of the companies involved. But it was
interesting to note an increased dampening effect was evident as the distance from the location of
impact increased. Further, the study also identified a surprising second negative impact on stock prices
of the companies in the supply chain. We propose that it is due to temporal memory of the markets to
disruptive events. Further research directions are proposed.

This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results.