USAir: Balancing Terminal Facilities and Runway Capacity at Pittsburgh

As a result of expansion and acquisition, USAir has experienced major growth in flight operations. In an effort to accommodate this expansion, a major construction project, called the Midfield Terminal project, is underway at USAir\u27s major hub, Greater Pittsburgh International Airport (PIT). The Midfield Terminal will result in a 60 percent increase in gate capacity for USAir and lower operating costs due to its location. However, increased gates infer increased flight frequencies. PIT already operates near capacity during peak periods and runway expansion has only been discussed. This paper evaluates the conditions of USAir at PIT with regard to the lack of landing facilities and gate expansion. There will be a dire need for additional runway capacity at PIT if USAir is to take advantage of the additional gates and the cost savings associated with those gates. Suggestions are made to avoid a critical imbalance of airport facilities

Coordinated Defects in Hepatic Long Chain Fatty Acid Metabolism and Triglyceride Accumulation Contribute to Insulin Resistance in Non-Human Primates

Non-Alcoholic fatty liver disease (NAFLD) is characterized by accumulation of triglycerides (TG) in hepatocytes, which may also trigger cirrhosis. The mechanisms of NAFLD are not fully understood, but insulin resistance has been proposed as a key determinant

Prediction in Marketing Using the Support Vector Machine

Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction differs markedly from that of standard parametric models. We explore these differences and benchmark the SVM's prediction hit-rates against those from the multinomial logit model. Because there are few applications of the SVM in marketing, we develop a framework to position it against current modeling techniques and to assess its weaknesses as well as its strengths.automated modeling, choice models, kernel transformations, multinomial logit model, predictive models, support vector machine