In biology, the protein structure alignment problem answers the question of how similar two proteins are. Proteins with strong physical similarities in their tertiary (folded) structure often have similar functions, so understanding physical similarity could be a key to developing protein-based medical treatments. One of the models for protein structure alignment is the maximum contact map overlap (CMO) model. The CMO model of protein structure alignment can be cast as a maximum clique problem on an appropriately defined graph. We exploit properties of these protein-based maximum clique problems to develop specialized preprocessing techniques and show how they can be used to more quickly solve contact map overlap instances to optimality.

Each year, more than $3 billion is wagered on the NCAA Division I men's basketball tournament. Most of that money is wagered in pools where the object is to correctly predict winners of each game, with emphasis on the last four teams remaining (the Final Four). In this paper, we present a combined logistic regression/Markov chain (LRMC) model for predicting the outcome of NCAA tournament games given only basic input data. Over the past 6 years, our model has been significantly more successful than the other common methods such as tournament seedings, the AP and ESPN/USA Today polls, the RPI, and the Sagarin and Massey ratings.

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We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes.

When cargo carriers form an alliance, sharing network capacity in order to improve profitability, a key issue is how to provide incentive for carriers to make decisions that are optimal for the alliance as a whole. We propose a mechanism that allocates both alliance resources and profits by appropriately setting prices for the resources. Clearly the behavior of an individual carrier within the alliance, and the impact of resource prices on this behavior, is important to understand. We analyze the performance of our mechanism using two different modeling approaches, and find that the behavioral model used can significantly impact alliance recommendations. More specifically, the choice of model impacts the feasibility of routing decisions made by individual carriers, as well as the properties of profit allocations that may be defined by the mechanism. Our proposed behavioral model, which makes use of more realistic control parameters than the alternative model studied, is promising with regard to both considerations. Finally, experimental results for alliances comprised of two and three carriers are analyzed; it is determined that the benefit associated with collaborating increases with network size and fleet capacity, and depending on the characteristics of demand, fleet capacity is a more important factor.

Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004-2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9 million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD 2007 ). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies.air pollution | electricity generation | health impacts | externalities | energy policy I n 2013, coal was used to produce 39% of the electricity in the United States (1), the largest portion of generation from any fuel type. During combustion, electricity generation from fossil fuels, such as coal, produces large quantities of primary gaseous pollutants, such as sulfur dioxide (SO 2 ) and nitrogen oxide (NO X ), which are major contributors to air pollution. These gaseous emissions interact with the atmosphere downwind of source emissions, forming several secondary air pollutants, including sulfate-based fine particulates less than 2.5 μm in aerodynamic diameter (PM 2.5 ) and ozone (O 3 ). Sulfate-based PM 2.5 comprises an estimated average of 24% of the ambient PM 2.5 in the United States (2), and can be controlled, in part, by a reduction in SO 2 emissions. Increased PM 2.5 concentrations cause increased mortality and asthma rates, as well as nonfatal heart attacks, emergency room visits, and hospital visits (3).Previous studies have integrated air pollution impacts into energy system models, but these studies lacked heterogeneous hourly and seasonal temporal pollutant formation. Muller et al. (9) to assess the health impacts of major emissions sectors in the United States. These studies have all made important contributions to the quantitative understanding of the health impacts of air pollution from electricity, transportation, and industrial systems...

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