Framework for Life Cycle Assessment of Complete Streets Projects

Caltrans 65A0527 Task Order 034.3A multitude of goals have been stated for complete streets including non-motorized travel safety, reduced costs and environmental burdens, and creation of more livable communities, or in other words, the creation of livable, sustainable and economically vibrant communities. A number of performance measures have been proposed to address these goals. Environmental life cycle assessment (LCA) quantifies the energy, resource use, and emissions to air, water and land for a product or a system using a systems approach. One gap that has been identified in current LCA impact indicators is lack of socio-economic indicators to complement the existing environmental indicators. To address the gaps in performance metrics, this project developed a framework for LCA of complete streets projects, including the development of socio-economic impact indicators that also consider equity. The environmental impacts of complete streets were evaluated using LCA information for a range of complete street typologies. A parametric sensitivity analysis approach was performed to evaluate the impacts of different levels of mode choice and trip change. Another critical question addressed was what are different social goals (economic, health, safety, etc.) that should be considered and how to consider equity in performance metrics for social goals. This project lays the foundation for the creation of guidelines for social and environmental LCAs for complete streets

A comparison of machine learning techniques for survival prediction in breast cancer

<p>Abstract</p> <p>Background</p> <p>The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established <it>70-gene signature</it>.</p> <p>Results</p> <p>We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection.</p> <p>Conclusions</p> <p>Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.</p