Optimizing the Structure and Scale of Urban Water Infrastructure: Integrating Distributed Systems

Large-scale, centralized water infrastructure has provided clean drinking water, wastewater treatment, stormwater management and flood protection for U.S. cities and towns for many decades, protecting public health, safety and environmental quality. To accommodate increasing demands driven by population growth and industrial needs, municipalities and utilities have typically expanded centralized water systems with longer distribution and collection networks. This approach achieves financial and institutional economies of scale and allows for centralized management. It comes with tradeoffs, however, including higher energy demands for longdistance transport; extensive maintenance needs; and disruption of the hydrologic cycle, including the large-scale transfer of freshwater resources to estuarine and saline environments.While smaller-scale distributed water infrastructure has been available for quite some time, it has yet to be widely adopted in urban areas of the United States. However, interest in rethinking how to best meet our water and sanitation needs has been building. Recent technological developments and concerns about sustainability and community resilience have prompted experts to view distributed systems as complementary to centralized infrastructure, and in some situations the preferred alternative.In March 2014, the Johnson Foundation at Wingspread partnered with the Water Environment Federation and the Patel College of Global Sustainability at the University of South Florida to convene a diverse group of experts to examine the potential for distributed water infrastructure systems to be integrated with or substituted for more traditional water infrastructure, with a focus on right-sizing the structure and scale of systems and services to optimize water, energy and sanitation management while achieving long-term sustainability and resilience

A Study on the Effects of Exception Usage in Open-Source C++ Systems

Exception handling (EH) is a feature common to many modern programming languages, including C++, Java, and Python, that allows error handling in client code to be performed in a way that is both systematic and largely detached from the implementation of the main functionality. However, C++ developers sometimes choose not to use EH, as they feel that its use increases complexity of the resulting code: new control flow paths are added to the code, "stack unwinding'' adds extra responsibilities for the developer to worry about, and EH arguably detracts from the modular design of the system. In this thesis, we perform an exploratory empirical study of the effects of exceptions usage in 2721 open source C++ systems taken from GitHub. We observed that the number of edges in an augmented call graph increases, on average, by 22% when edges for exception flow are added to a graph. Additionally, about 8 out of 9 functions that may propagate a throw from another function. These results suggest that, in practice, the use of C++ EH can add complexity to the design of the system that developers must strive to be aware of

Using Neural Networks to Develop a New Model to Screen Applicants for Apartment Rentals

Credit scoring is a mathematical means of summarizing a consumer\u27s credit and financial history into a three-digit number. This number provides an easy means of identifying and sorting consumer behavior into categories based on their financial history. To select applicants for loans and to set interest rates on loans, banks and financial institutions routinely use credit scoring. Auto insurance companies also use scoring to decide which consumers will be offered auto insurance and to set the price for auto insurance. Despite success in these two industries, scoring does not appear to be effective in the apartment rental industry in picking desirable applicants for apartment rental. The first phase of this research analyzed the results of using six commercially available credit scores applied in one apartment complex to the task of selecting applicants. This part of the analysis answered the research question: How effective are commercially available credit scores in predicting applicant financial behavior when renting an apartment? This research determined that these six scores are not predictive and possible explanations are given. Phase two of this research used neural networks to develop a new model using both credit data and other lifestyle data about the applicant. The hypothesis was that the addition of this lifestyle data would improve accuracy in selecting apartment rental applicants over currently available models based only on credit data. This part of the analysis answered the research question: How is the prediction accuracy of a new neural network based credit scoring model improved by adding lifestyle data to the credit report data? This research indicates that accuracy is greatly improved. Three variables were found to be most predictive for the apartment rental decision and these were a) percentage of satisfactory accounts in the applicant\u27s credit file, b) total applicant income, and c) driving record of the applicant. Four areas were suggested for future study and these are a) understanding the underlying human behavior differences that influence apartment financial decisions, b) addition of fuzzy logic techniques to the neural network, c) expanding the number of commercial credit models tested and size of the data set and d) effect of geography on model prediction accuracy. This dissertation also examined U.S. information policy and addressed consumer privacy considerations when using non-credit data to select applicants

Data mining industry : emerging trends and new opportunities

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2000."May 2000."Includes bibliographical references (leaves 170-179).by Walter Alberto Aldana.M.Eng