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Miller D.H.,US Ecology | Kreis Jr. R.G.,US Ecology | Huang W.-C.,U.S. Navy | Xia X.,CSC Inc.
Biological Invasions | Year: 2010

A Lake Michigan Ecosystem Model (LM-Eco) that includes a detailed description of trophic levels and their interactions was developed for Lake Michigan. The LM-Eco model constitutes a first step toward a comprehensive Lake Michigan ecosystem productivity model to investigate ecosystem-level responses and effects within the lower food web of the lake. The effect of the invasive species Bythotrephes longimanus on individual zooplankton species was investigated based upon extensive field data collected at multiple locations in Lake Michigan during the 1994-1995 Lake Michigan Mass Balance Study. Field data collected at 15 sampling stations within Lake Michigan over a series of 8 sampling cruises throughout a 2 year period demonstrated that over 65% of zooplankton species exhibited a decline with the occurrence of Bythotrephes in the sample. The LM-Eco model was successfully applied to simulate the trends of Bythotrephes and zooplankton abundance as observed in the collected field data. Model simulations allowed for examination of interactions between the invader Bythotrephes and native zooplankton groups on a 5 km by 5 km resolution throughout Lake Michigan. Analysis was completed as a time series specific to individual field sampling locations within the lake, and also on a lake-wide scale. © 2010 US Government.

Rohret D.,CSC Inc.
Proceedings of the 10th International Conference on Cyber Warfare and Security, ICCWS 2015 | Year: 2015

The International Council on Systems Engineering's Resilient System's Working Group defines resiliency as, 'the capability of a system with specific characteristics before, during, and after a disruption to absorb the disruption, recover to an acceptable level of performance, and sustain that level for an acceptable period of time' [INCOSE, 2013]. An operational resiliency model describes a measurement process while demonstrating the scope of achieving resiliency through a dynamic process that includes anticipation of negative effects, withstanding the affects, recovery, and network evolution. In order to maintain the command and control (C2) advantage during military operations and throughout cyberspace, the measure of functional resiliency must be quantified for integrated and operational systems to provide network defenders and military decision makers the level of capability (to recover) following a significant cyber incident or a catastrophic natural event. To achieve functional resonance, and vet potential and future threats, technologies competing for network resources must be identified and stressed to determine their role in resiliency and the potential affect they will have on operational systems during an aggressive cyber attack. Through network analysis, based on actual adversarial research and case studies, adaptive analysis teams collect the necessary data to determine a systems' resonance characteristics, specifically, interdependent technologies and processes that can negatively affect a single system or an enterprise network. The traditional role of a vulnerability analysis team is to identify and exploit every vulnerable system or process in order to expose and mitigate weaknesses for the purpose of creating a more viable network. This scope is narrow and confined to a limited range of requirements or technologies based on a similarly narrow set of objectives and goals. To compound the problems associated with obtaining an acceptable resilient posture for a specific system or an enterprise network is the IT industry's misconception that resiliency is tantamount to bandwidth and not a measurement of capability. Network managers attempt to solve poor resiliency by installing more network appliances (redundancy) and adding additional bandwidth; both costly and often ineffective. It is paramount that network managers first identify their current resiliency and associated functional resonance issues prior to initiating corrective actions. The intent of this research is to identify current methods of measuring or achieving acceptable resilience for an enterprise network, identify shortfalls in acquiring accurate and actionable data, and the incorrect application of mitigations that result in no or little resiliency enhancement. The author outlines a process to accurately measure a networks resiliency posture, which will lead to effective mitigations and enhancements allowing for a rapid and cost-effective recovery of functionality.

Goldberg R.J.,Aurora University | Smits G.,CSC Inc. | Wiseman A.C.,Aurora University
Transplantation | Year: 2010

Background.: The degree to which recipient/donor (R/D) size mismatching leads to nephron underdosing and worse kidney allograft survival remains poorly defined, particularly in the setting of preexisting nephron loss such as the expanded criteria donor (ECD). Methods.: We performed a retrospective analysis of 69,737 deceased donor transplants followed by a subset analysis of ECD transplants using data from the Scientific Registry of Transplant Recipients from 1992 to 2005. Ratios of R/D body surface area (BSA) were used to estimate nephron disparity and segregate pairs. Results.: In the entire cohort, severe BSA disparity (R/D BSA>1.38 m) was associated with slightly worse 10-year unadjusted graft survival (35% for severe BSA disparity vs. 39% in pairs of comparable size, P<0.0001). In multivariate analysis, BSA disparity was associated with a 15% increased risk of graft loss (hazard ratio 1.15, P<0.0001). Within ECD cohorts, severe BSA disparity was associated with a decrease in 10-year unadjusted graft survival of greater magnitude than the overall cohort (10% for severe BSA disparity vs. 22% in pairs of comparable size, P<0.0004). On multivariate analysis, severe R/D BSA disparity was associated with worse allograft survival similar to the entire cohort (hazard ratio 1.18, P=0.04). Conclusions.: Recipients receiving kidneys from substantially smaller donors have a statistically higher rate of graft loss that is more pronounced in ECD kidneys. Although severe R/D size disparity is an independent risk factor for graft loss, the magnitude of this risk requires consideration in the context of other risk factors for the graft loss and the hazards of dialysis. © 2010 by Lippincott Williams & Wilkins.

Chandra Katari A.,GENPACT India Pvt. Ltd | Umar Shaik N.,CSC Inc. | Rao Pasupuleti V.,P.A. College
International Journal of Applied Environmental Sciences | Year: 2013

In this paper we discussed a brief empirical application of ARIMA in various time intervals of US inflation data and provided recommendations on the time limit for ARIMA. Traditional time series models such as ARIMA models have been proven to be inadequate for modeling long or short range dependence. In this context we introduced MCARIMA methodology and its applications of fine-tuning of ARIMA residuals using Markov Chain to improve prediction accuracy and to decide the time interval for better forecasting. © Research India Publications.

Chandra Katari A.,GENPACT India Pvt. Ltd | Umar Shaik N.,CSC Inc. | Rao Pasupuleti V.,P.A. College
International Journal of Applied Environmental Sciences | Year: 2013

In this study, we use different techniques to predict the probability of customer payment behavior for accounts receivables (in the form of invoices) in finance business. Our goal in this paper is to develop a statistical method that yields predictive distributions for delinquency occurrence of the customer. For these purpose two different approaches, logistic regression and discriminant analysis are used and we also applied both methods in the case of unified and sitespecific scenarios. Results are presented and discussed to choose the best model. Using simple logistic regression and discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Goodness of fit of the logistic regression model will be examined by using likelihood ratio test & wald's test. For discriminant function Eigen values (λ), canonical correlation eta (η), wilks' lambda (Λ) and chi-square test (χ2) are used for goodness of fit test. © Research India Publications.

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