Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

Comparative study of dissolved organic matter from groundwater and surface water in the Florida coastal Everglades using multi-dimensional spectrofluorometry combined with multivariate statistics

Dissolved organic matter (DOM) in groundwater and surface water samples from the Florida coastal Everglades were studied using excitation–emission matrix fluorescence modeled through parallel factor analysis (EEM-PARAFAC). DOM in both surface and groundwater from the eastern Everglades S332 basin reflected a terrestrial-derived fingerprint through dominantly higher abundances of humic-like PARAFAC components. In contrast, surface water DOM from northeastern Florida Bay featured a microbial-derived DOM signature based on the higher abundance of microbial humic-like and protein-like components consistent with its marine source. Surprisingly, groundwater DOM from northeastern Florida Bay reflected a terrestrial-derived source except for samples from central Florida Bay well, which mirrored a combination of terrestrial and marine end-member origin. Furthermore, surface water and groundwater displayed effects of different degradation pathways such as photodegradation and biodegradation as exemplified by two PARAFAC components seemingly indicative of such degradation processes. Finally, Principal Component Analysis of the EEM-PARAFAC data was able to distinguish and classify most of the samples according to DOM origins and degradation processes experienced, except for a small overlap of S332 surface water and groundwater, implying rather active surface-to-ground water interaction in some sites particularly during the rainy season. This study highlights that EEM-PARAFAC could be used successfully to trace and differentiate DOM from diverse sources across both horizontal and vertical flow profiles, and as such could be a convenient and useful tool for the better understanding of hydrological interactions and carbon biogeochemical cycling.

Molecular characterization of proteinaceous material in the Florida coastal Everglades

Dissolved organic nitrogen (DON) is the least known component of the nitrogen cycle, in part as a result of the lack of adequate analytical methods for its molecular characterization. In this study proteinaceous material in DON, collected at six geomorphologically different sites in the Florida coastal Everglades, was characterized by amino acid analysis and protein gel electrophoresis. The amino acid composition of the samples suggests that the canal DON was more degraded and subject to higher microbial inputs than the mangrove marshwater and marine end-member stations. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) results supported this observation as distinctly different protein profiles were obtained for the canal waters compared to samples collected at other stations. These preliminary results highlight the potential of combining amino acid and intact protein analysis to fingerprint the sources of DON in different aquatic environments, and show SDS-PAGE as a potentially useful method to characterize DON.

One-dimensional and two-dimensional polyacrylamide gel electrophoresis: a tool for protein characterisation in aquatic samples

Dissolved organic nitrogen (DON) represents the least understood part of the nitrogen cycle. Due to recent methodological developments, proteins now represent a potentially characterisable fraction of DON at the macromolecular level. We have applied polyacrylamide gel electrophoresis to characterise proteins in samples from a range of aquatic environments in the Everglades National Park, Florida, USA. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) showed that each sample has a complex and characteristic protein distribution. Some proteins appeared to be common to more than one site, and these might derive from dominant higher plant vegetation. Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) provided better resolution; however, strong background hindered interpretation. Our results suggest that the two techniques can be used in parallel as a tool for protein characterisation: SDS-PAGE to provide a sample-specific fingerprint and 2D-PAGE to focus on the characterisation of individual protein molecules.

Research Note: Biometric Technology Applications and Trends in Hotels

The purpose of this study is to investigate the biometrics technologies adopted by hotels and the perception of hotel managers toward biometric technology applications. A descriptive, cross sectional survey was developed based on extensive review of literature and expert opinions. The population for this survey was property level executive managers in the U.S. hotels. Members of American Hotel and Lodging Association (AHLA) were selected as the target population for this study. The most frequent use of biometric technology is by hotel employees in the form of fingerprint scanning. Cost still seems to be one of the major barriers to adoption of biometric technology applications. The findings of this study showed that there definitely is a future in using biometric technology applications in hotels in the future, however, according to hoteliers; neither guests nor hoteliers are ready for it fully.