Network State Estimation and Prediction for Real-Time Traffic Management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.     

Factors affecting air sparging remediation systems using field data and numerical simulations

Field data from five air sparging sites were used to assess the effect of several soil, contaminant, and air sparging system factors on the removal time and associated costs required to reach specified clean-up criteria. Numerical simulations were also performed to better assess the field data and to expand the data sets beyond the five field sites. Ten factors were selected and evaluated individually over a range of values based on information from practitioners and the literature. Trends in removal time and removal cost to reach a specified clean-up criterion were analyzed to ascertain the conditions controlling contaminant removal with variations in each factors' value. A linear sensitivity equation was used to quantify system dynamics controlling the observed contaminant removal trends for each factor. Factors found most critical across all field sites in terms of removal time and/or cost were contaminant type, sparge pulsing schedule, number of wells, maximum biodecay rate, total soil porosity, and aquifer organic carbon content. Factors showing moderate to low effect included the depth of the sparge point below the water table, air injection rate/pressure, horizontal air conductivity, and anisotropy ratio. At each field site, subsurface coverage of sparged air, sparged air residence time, contaminant equilibrium in the system, contaminant phase distribution, oxygen availability to microbes, and contaminant volatility seem to control the system responses and were affected by one or more of the 10 factors evaluated.     

High-resolution photometric optical monitoring for thin-film deposition

Real-time monitoring of thin-film deposition with high resolution is important for precise fabrication of thin-film devices in a technological environment with ever-increasing demands for smaller size and better performance. Using photometry, we were able to achieve a real-time optical monitoring resolution of film thickness that is comparable with a single atomic layer scale (i.e., subnanometer). Filtering noise efficiently and compensating for sources of error by use of an appropriate model produced this high resolution. The procedure proved reliable and can be useful in the thin-film-deposition industry.     

Reliable fabrication technologies for optical resonant filters

A reliable technology for optical resonant filters can be useful for optoelectronics technologies, optical communications systems, and biomedical applications. Positioning the resonance at a specific target wavelength is challenging because of the sensitivity of placing a narrow spectral filtering window tothe error factors that are associated with the fabrication and characterization processes. We describe and prove experimentally two fabrication approaches to overcome this challenge.     

Characteristics of the medical waste generated at the Jordanian hospitals

Recently, Jordan has witnessed increased amounts of medical waste generated at different healthcare facilities. This has resulted in issuance of Medical Waste Regulation that aimed at regulating the management processes of such hazardous waste. To provide information on medical waste generation rates, composition and statistical characteristics, a comprehensive sampling survey was initiated after a regulatory definition of the medical waste was established. Hospitals from Public, private and educational categories were covered by the survey. This paper presents the findings of the survey. The average generation rates ranged from 0.29 to 1.36 kg/bed/day, while in terms of patient numbers it is from 0.36 to 0.87 kg/patient/day. The total daily amount of medical waste generated at the Jordanian hospitals was estimated to be 6 tones/day. The daily amounts of medical waste generated at King Abdullah University Hospital were found to follow a log normal probability distribution. Physical composition analysis of the medical waste, which conducted based on the categories identified by the Jordanian Regulation, indicated that the infectious waste category is the highest, followed by sharps category and finally pathological, cytotoxic and pharmaceutical categories were the lowest. The study concluded that all hospitals covered by the survey are practicing segregation of hazardous medical waste from general medical waste. However, the segregation process in some hospitals is still inefficient and there is a potential for improvements toward minimizing the hazardous medical waste generation.     

Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava

Water temperature directly affects the physical, biological and chemical characteristics of the river and determines the fitness and life of all aquatic organisms. It has direct and indirect effects on nearly all aspects of stream ecology. Accurately estimating water temperature is a complex problem. The purpose of this article is to analyze the relationship between the air and water temperature of the River Drava by constructing an artificial neural network (ANN) model and choosing appropriate network architectures for the River Drava’s daily river water temperature as well as demonstrating its application in improving the interpretation of the results. A linear regression model, as well as a stochastic model are also constructed and compared to ANN models consisting of a multilayer perceptron neural network and a radial basis function network. The results indicate that the ANN models are much better models and that ANNs are powerful tools that can be used for the estimation of daily mean river temperature.     

On a multivariable extension of Jacobi matrix polynomials

The classical Jacobi matrix polynomials only for commutative matrices were first studied by Defez et al. [E. Defez, L. Jódar, A. Law. Jacobi matrix differential equation, polynomial solutions and their properties, Comput. Math. Appl. 48 (2004) 789–803]. The main aim of this paper is to construct a multivariable extension with the help of the classical Jacobi matrix polynomials (JMPs). Generating matrix functions and recurrence relations satisfied by these multivariable matrix polynomials are derived. Furthermore, general families of multilinear and multilateral generating matrix functions are obtained and their applications are presented.