Arrival Time Reliability in Strategic User Equilibrium

Networks and Spatial Economics - Although traffic assignment models remain heavily utilized globally for the planning and evaluation of new transport infrastructure, commonly applied assignment...     

Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion

Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess the local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during the period 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data.     

Structuring targeted surveillance for monitoring disease emergence by mapping observational data onto ecological process

An efficient surveillance system is a crucial factor in identifying, monitoring and tackling outbreaks of infectious diseases. Scarcity of data and limited amounts of economic resources require a targeted effort from public health authorities. In this paper, we propose a mathematical method to identify areas where surveillance is critical and low reporting rates might leave epidemics undetected. Our approach combines the use of reference-based susceptible–exposed–infectious models and observed reporting data; We propose two different specifications, for constant and time-varying surveillance, respectively. Our case study is centred around the spread of the raccoon rabies epidemic in the state of New York, using data collected between 1990 and 2007. Both methods offer a feasible solution to analyse and identify areas of intervention.     

Treatment of metalworking fluids: development of a bioconsortium for the treatment of nanofiltration permeate

Traditional treatment methods of waste metalworking fluids produce an aqueous phase containing toxic components and with a high Chemical Oxygen Demand (COD). In this paper, a biological solution is proposed to further reduce both the effluent COD and its toxicity. A method to develop a bioconsortium from microbes found in the waste metalworking fluid is proposed. The development took place in three phases: feasibility, bioconsortium development and optimisation. Flask tests have been used to show the feasibility of using the metalworking fluid indigenous microbial community for the degradation of the nanofiltration permeate of the metalworking fluid. A suspended bioreactor allowed the development of a better‐adapted consortium. Finally, a fixed bed bioreactor inoculated with the developed bioconsortium was set up and run for 8 months to test the bioconsortium's robustness and to optimise the biological process. A bioconsortium was successfully developed using a simple method and a 90% reduction in the original nanofiltration COD level was achieved by the fixed bed bioreactor. Copyright © 2005 Society of Chemical Industry     

Analysis of helical slow-wave structures for space TWTs using 3-D electromagnetic simulators

This paper presents a detailed analysis of traveling wave tube (TWT) helical slow-wave structures (SWSs) using three-dimensional (3-D) electromagnetic simulators. The main advantages of this approach are its capability to take into account the real 3-D geometry of this complex structure, and consequently to eliminate the approximations used in the analytical approach. In addition, it provides higher accuracy than experimental results. Two different methods have been applied (finite elements and finite integration) to predict the cold parameters (phase velocity, coupling impedance, and attenuation) at a level of accuracy higher than the usual practice. An extended analysis has been performed to guarantee the stability and accuracy of the results, as well as to study the sensitivity of the results to the discretization. This paper has shown that both methods, as implemented in two commercially available simulators, are perfectly suitable for the accurate modeling of such complex geometries.     

Optimal Long-Term Infrastructure Maintenance Planning Accounting for Traffic Dynamics

Abstract:   Periodic infrastructure maintenance is crucial for a safe and efficient transportation system. Numerous decision models for the maintenance planning problem have been proposed in the literature. However, to the best of our knowledge, no model exists that simultaneously accounts for traffic dynamics and is intended for long-term planning purposes. This article addresses this gap in the literature. A mixed-integer bi-level program is introduced that minimizes the long-term maintenance cost as well as the total system travel time. For the solution approach we utilize a genetic algorithm in conjunction with mesoscopic traffic simulation. The model is illustrated via a numerical example.     

Computational humor

In an effort to focus on tractable problems, computational natural language understanding systems have typically addressed language phenomena that are amenable to combinatorial approaches using static and stereotypical semantic representations. Although such approaches are adequate for much of language, they're not easily extended to capture humans' more creative language interpretation capacities. This paper proposes a model of humor comprehension based on frame-shifting within a simulation-based natural-language-understanding system.     

THE CONSTRUCTION OF A PUN GENERATOR FOR LANGUAGE SKILLS DEVELOPMENT

Since the early 1990s, there have been a number of small-scale computer programs that automatically constructed simple verbal jokes (puns), but none of these were fully developed systems that could be used for a practical application. We describe the building and testing of the STANDUP program – a large-scale, robust, interactive, user-friendly pun-generator (inspired by Binsted's JAPE program), which is aimed at allowing children, particularly those with communication disabilities, to develop their linguistic skills. The STANDUP system was designed in consultation with potential users and suitable experts, was rigorously engineered using public-domain linguistic data, and has a special purpose, child-friendly, graphical user interface. The software was tested successfully with real users (children with complex communication needs).     

Use of urine biomarkers to evaluate menstrual function in healthy premenopausal women

A total of 403 healthy, premenopausal women, residing near Santa Clara, California, were recruited from a large health care plan in California for a study of menstrual function. After a telephone interview, participants collected daily urine samples and recorded bleeding and other information in diaries. Data were collected during 1990-1991. Urine samples were analyzed for creatinine and for estradiol and progesterone metabolites by enzyme-linked immunoassay. Computer algorithms were developed to derive menstrual segment length, ovulatory status, day of ovulation, and other parameters from the urine and diary data. (We use "segment" rather than "cycle" to avoid implying that normal cycling occurred.) The average length of participation was 141 (standard deviation, 45) days. The mean segment length was 28.8 (standard deviation, 4.4) days; follicular phase length, 16.0 (standard deviation, 4.4) days; and luteal phase length, 12.9 (standard deviation, 1.7) days; 19 (4.7%) women experienced anovulatory episodes. In exploratory multivariate analyses, important associations included the following: age of > or = 35 years with decreased segment and follicular phase lengths; heavier weight (upper quartile) with anovulation and increased follicular phase and decreased luteal phase lengths; Hispanic ethnicity with anovulation and increased segment length; and past difficulty in achieving pregnancy with anovulation and increased length and variability of segments and follicular phases. Urine biomarkers can be used successfully to evaluate menstrual function in epidemiologic studies.