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Indiana, PA, United States

Indiana University of Pennsylvania is a public university in Indiana County, Pennsylvania, USA. Along with West Chester University of Pennsylvania, it is one of the two largest universities in the Pennsylvania State System of Higher Education and thus the commonwealth's fourth or fifth largest public university. As of fall 2013, IUP had 12,471 undergraduates and 2,257 graduate students attending the university. The university is 55 miles northeast of Pittsburgh. It is governed by a local Council of Trustees and the Board of Governors of the Pennsylvania State System of Higher Education. IUP has branch campuses at Punxsutawney, Northpointe, and Monroeville. IUP is accredited by Middle States Association of Colleges and Universities, NCATE, and AACSB. A research-intensive institution, the university has been included in the 2013 list of "Best Northeastern" schools by The Princeton Review, and IUP's Eberly College of Business was included in the list of "Best Business Schools" in the northeast. Wikipedia.

Rodger J.A.,Indiana University of Pennsylvania
Expert Systems with Applications | Year: 2013

In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold. © 2013 Elsevier B.V. All rights reserved. Source

Gondolf E.W.,Indiana University of Pennsylvania
Aggression and Violent Behavior | Year: 2011

In the midst of the debate over batterer program effectiveness, several alternative approaches have been promoted: psychodynamic treatment for attachment disorders, diversified programming for batterer types, motivational techniques addressing readiness to change, specialized counseling for African-American men, and couples counseling for mutual violence. A critical overview of the research on these alternative approaches exposes weak or insufficient supporting evidence. There is also strong generic evidence for the predominant cognitive-behavioral approach in batterer programs, and a focus on system implementation might account for improved outcomes. While the innovations are encouraging, an "evidence-based practice" for batterers has yet to be clearly established. © 2011 Elsevier Ltd. Source

This paper reports on a new integrated vehicle health maintenance system (IVHMS) based on fault detection and feedback. A fuzzy multi-sensor data fusion Kalman model was used to help reduce IVHMS failure risk. The IVHMS was tested, and sensors with and without faults were identified. The results demonstrate that multi-sensor data fusion based on fault detection and fuzzy Kalman feedback is an effective method of reducing risk in an IVHMS. Use of the fuzzy Kalman filter approach reduced the time needed to perform complex matrix manipulations to control higher order systems in the IVHMS. Moreover, the approach was able to capture the nonlinearity of engine operations under the influence of various anomalies. © 2012 Elsevier Ltd. All rights reserved. Source

Because supply chains are complex systems prone to uncertainty, statistical analysis is a useful tool for capturing their dynamics. Using data on acquisition history and data from case study reports, we used regression analysis to predict backorder aging using National Item Identification Numbers (NIINs) as unique identifiers. More than 56,000 NIINs were identified and used in the analysis. Bayesian analysis was then used to further investigate the NIIN component variables. The results indicated that it is statistically feasible to predict whether an individual NIIN has the propensity to become a backordered item. This paper describes the structure of a Bayesian network from a real-world supply chain data set and then determines a posterior probability distribution for backorders using a stochastic simulation based on Markov blankets. Fuzzy clustering was used to produce a funnel diagram that demonstrates that the Acquisition Advice Code, Acquisition Method Suffix Code, Acquisition Method Code, and Controlled Inventory Item Code backorder performance metric of a trigger group dimension may change dramatically with variations in administrative lead time, production lead time, unit price, quantity ordered, and stock. Triggers must be updated regularly and smoothly to keep up with the changing state of the supply chain backorder trigger clusters of market sensitiveness, collaborative process integration, information drivers, and flexibility. © 2014 Elsevier Ltd. All rights reserved. Source

Rodger J.A.,Indiana University of Pennsylvania
Expert Systems with Applications | Year: 2014

This paper addresses the problem of predicting demand for natural gas for the purpose of realizing energy cost savings. Daily monitoring of a rooftop unit wireless sensor system provided feedback for a decision support system that supplied the demand for the required number of million cubic feet of natural gas used to control heating, ventilation, and air conditioning systems. The system was modeled with artificial neural networks (ANNs). Data on the consumption of the system were collected for 111 days beginning September 21, 2012. The input/output data were used to train the ANN. The ANN approximated the data very well, showing that it can be used to predict demand for natural gas. A fuzzy nearest neighbor neural network statistical model consisting of four components was used. The predictive models were implemented by comparing regression, fuzzy logic, nearest neighbor, and neural networks. In addition, to optimize natural gas demand, we used the fuzzy regression nearest neighbor ANN model cost function to investigate the variables of price, operating expenses, cost to drill new wells, cost to turn gas on, oil price and royalties. © 2013 Elsevier Ltd. All rights reserved. Source

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