Soil remediation time to achieve clean-up goals I: influence of soil water content

The current models are not simple enough to allow a quick estimation of the remediation time. This work reports the development of an easy and relatively rapid procedure for the forecasting of the remediation time using vapour extraction. Sandy soils contaminated with cyclohexane and prepared with different water contents were studied. The remediation times estimated through the mathematical fitting of experimental results were compared with those of real soils. The main objectives were: (i) to predict, through a simple mathematical fitting, the remediation time of soils with water contents different from those used in the experiments; (ii) to analyse the influence of soil water content on the: (ii1) remediation time; (ii2) remediation efficiency; and (ii3) distribution of contaminants in the different phases present into the soil matrix after the remediation process. For sandy soils with negligible contents of clay and natural organic matter, artificially contaminated with cyclohexane before vapour extraction, it was concluded that (i) if the soil water content belonged to the range considered in the experiments with the prepared soils, then the remediation time of real soils of similar characteristics could be successfully predicted, with relative differences not higher than 10%, through a simple mathematical fitting of experimental results; (ii) increasing soil water content from 0% to 6% had the following consequences: (ii1) increased remediation time (1.8–4.9 h, respectively); (ii2) decreased remediation efficiency (99–97%, respectively); and (ii3) decreased the amount of contaminant adsorbed onto the soil and in the non-aqueous liquid phase, thus increasing the amount of contaminant in the aqueous and gaseous phases.

Remediation of sandy soils contaminated with hydrocarbons and halogenated hydrocarbons by soil vapour extraction

This paper presents the study of the remediation of sandy soils containing six of the most common contaminants (benzene, toluene, ethylbenzene, xylene, trichloroethylene and perchloroethylene) using soil vapour extraction (SVE). The influence of soil water content on the process efficiency was evaluated considering the soil type and the contaminant. For artificially contaminated soils with negligible clay contents and natural organic matter it was concluded that: (i) all the remediation processes presented efficiencies above 92%; (ii) an increase of the soil water content led to a more time-consuming remediation; (iii) longer remediation periods were observed for contaminants with lower vapour pressures and lower water solubilities due to mass transfer limitations. Based on these results an easy and relatively fast procedure was developed for the prediction of the remediation times of real soils; 83% of the remediation times were predicted with relative deviations below 14%.

Soil remediation time to achieve clean-up goals II: influence of natural organic matter and water contents

This work reports a relatively rapid procedure for the forecasting of the remediation time (RT) of sandy soils contaminated with cyclohexane using vapour extraction. The RT estimated through the mathematical fitting of experimental results was compared with that of real soils. The main objectives were: (i) to predict the RT of soils with natural organic matter (NOM) and water contents different from those used in experiments; and (ii) to analyse the time and efficiency of remediation, and the distribution of contaminants into the soil matrix after the remediation process, according to the soil contents of: (ii1) NOM; and (ii2) water. For sandy soils with negligible clay contents, artificially contaminated with cyclohexane before vapour extraction, it was concluded that: (i) if the NOM and water contents belonged to the range of the prepared soils, the RT of real soils could be predicted with relative differences not higher than 12%; (ii1) the increase of NOM content from 0% to 7.5% increased the RT (1.8–13 h) and decreased the remediation efficiency (RE) (99–90%) and (ii2) the increase of soil water content from 0% to 6% increased the RT (1.8–4.9 h) and decreased the RE (99–97%). NOM increases the monolayer capacity leading to a higher sorption into the solid phase. Increasing of soil water content reduces the mass transfer coefficient between phases. Concluding, NOM and water contents influence negatively the remediation process, turning it less efficient and more time consuming, and consequently more expensive.

The effect of Parkinson's disease on time estimation as a function of stimulus duration range and modality

The present research sought to investigate the role of the basal ganglia in timing of sub- and supra-second intervals via an examination of the ability of people with Parkinson's disease (PD) to make temporal judgments in two ranges, 100-500 ms, and 1-5 s. Eighteen nondemented medicated patients with PD were compared with 14 matched controls on a duration-bisection task in which participants were required to discriminate auditory and visual signal durations within each time range. Results showed that patients with PD exhibited more variable duration judgments across both signal modality and duration range than controls, although closer analyses confirmed a timing deficit in the longer duration range only. The findings presented here suggest the bisection procedure may be a useful tool in identifying timing impairments in PD and, more generally, reaffirm the hypothesised role of the basal ganglia in temporal perception at the level of the attentionally mediated internal clock as well as memory retrieval and/or decision-making processes. (c) 2007 Elsevier Inc. All rights reserved.

Real-time estimation of transmission line impedance based on modal analysis theory

The objective of this paper is to show a methodology to estimate the longitudinal parameters of transmission lines. The method is based on the modal analysis theory and developed from the currents and voltages measured at the sending and receiving ends of the line. Another proposal is to estimate the line impedance in function of the real-time load apparent power and power factor. The procedure is applied for a non-transposed 440 kV three-phase line. © 2011 IEEE.

Towards execution time estimation in abstract machine-based languages

Abstract machines provide a certain separation between platformdependent and platform-independent concerns in compilation. Many of the differences between architectures are encapsulated in the speciflc abstract machine implementation and the bytecode is left largely architecture independent. Taking advantage of this fact, we present a framework for estimating upper and lower bounds on the execution times of logic programs running on a bytecode-based abstract machine. Our approach includes a one-time, programindependent proflling stage which calculates constants or functions bounding the execution time of each abstract machine instruction. Then, a compile-time cost estimation phase, using the instruction timing information, infers expressions giving platform-dependent upper and lower bounds on actual execution time as functions of input data sizes for each program. Working at the abstract machine level makes it possible to take into account low-level issues in new architectures and platforms by just reexecuting the calibration stage instead of having to tailor the analysis for each architecture and platform. Applications of such predicted execution times include debugging/veriflcation of time properties, certiflcation of time properties in mobile code, granularity control in parallel/distributed computing, and resource-oriented specialization.

Using combined static analysis and profiling for logic program execution time estimation

Predicting statically the running time of programs has many applications ranging from task scheduling in parallel execution to proving the ability of a program to meet strict time constraints. A starting point in order to attack this problem is to infer the computational complexity of such programs (or fragments thereof). This is one of the reasons why the development of static analysis techniques for inferring cost-related properties of programs (usually upper and/or lower bounds of actual costs) has received considerable attention.

Towards execution time estimation for logic programs via static analysis and profiling

Effective static analyses have been proposed which infer bounds on the number of resolutions or reductions. These have the advantage of being independent from the platform on which the programs are executed and have been shown to be useful in a number of applications, such as granularity control in parallel execution. On the other hand, in distributed computation scenarios where platforms with different capabilities come into play, it is necessary to express costs in metrics that include the characteristics of the platform. In particular, it is specially interesting to be able to infer upper and lower bounds on actual execution times. With this objective in mind, we propose an approach which combines compile-time analysis for cost bounds with a one-time profiling of the platform in order to determine the valúes of certain parameters for a given platform. These parameters calíbrate a cost model which, from then on, is able to compute statically time bound functions for procedures and to predict with a significant degree of accuracy the execution times of such procedures in the given platform. The approach has been implemented and integrated in the CiaoPP system.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.

Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F-0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F-0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (D-LR) appeared to be an effective way to predict whether F-0 immigrants could be identified for a particular pair of populations using a given set of markers.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.