Numerical modelling of the propagation of fast landslides using the finite element method

This paper proposes a two‐dimensional (2D) model for the analysis of the propagation of fast landslides involving a fluidized material such as debris and mud flows, flowslides and avalanching flows. The model is based on the Navier–Stokes depth‐integrated equations. To incorporate the effect of steep slopes and centrifugal forces due to the high velocities characterizing the flowslides and the bed curvature, a curvilinear system of reference is used. The corresponding equations of motion are complemented by depth‐averaged constitutive equations and bed friction laws. The resulting set of differential equations are solved using the two‐step Taylor–Galerkin algorithm. This algorithm has been used by the authors to solve hydraulic and dam‐break problems using the finite element method. Owing to the importance of the source term compared to the advection component, the proposed algorithm follows a splitting scheme using a fourth‐order Runge–Kutta method for integrating the friction and slope components. The performance of the overall approach has been checked in a number of examples. The analysis of the results provides insights into the key elements of the model and shows the adequacy of the method to solve real problems where merging and splitting of the flow occur. Copyright © 2004 John Wiley & Sons, Ltd.     

Investigation of the impact of changes in demand forecasting method on the financial performance of an electricity supply undertaking

Peak demand forecasts obtained from six different univariate forecasting methods, under a range of conditions, were used to drive a capacity acquisition model of a large electrical supply system; and the resulting physical and financial performance of the model was observed for each set of forecasts. The results obtained are discussed in the context of their implications for the choice of load forecasting method used in capacity acquisition planning by a power supply undertaking.     

Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.     

TCP/IP网络的动态模型描述

拥塞现象成为TCP/IP网络发展面临的一个重要问题。因此，拥塞控制对TCP/IP网络的鲁棒性和稳定性具有重要作用。目前，网络拥塞控制策略主要包括两类：端到端的控制机制，如TCP拥塞控制算法；网络内部的主动队列管理(AQM)策略。但由于缺乏对网络系统动态特性的了解，这些拥塞控制策略大都基于专家经验，并没有建立完整的理论分析框架。为此，本文从数据流的角度出发，通过建立网络基本单元状态方程模型来实现IP网络系统的数学解析模型，然后用混杂系统来描述TCP带有拥塞控制策略的数据传输过程，建立了TCP/IP网络的动态模型，为网络系统中动态性能的分析、拥塞控制策略的设计奠定了基础。实验结果表明，该数学模型与NS仿真实验的结果相一致。     

Modeling Tetrachloroethylene Decomposition in Photosonolysis Reactor

A mathematical model was developed for the decomposition of halogenated compounds in water and applied for tetrachloroethylene (PCE) in a flow-through photosonolysis reactor. To develop the model, a series of differential equations were formulated based on the principles of conservation of mass and mass action for hypothetical parent and daughter species, which were then solved analytically. The model sensitivity analysis was performed to determine if the model gives intuitive results. In support of the modeling effort, experiments were conducted. In a flow-through bench-scale reactor, simulated groundwater contaminated with PCE was irradiated with ultraviolet light and ultrasound concurrently in the presence of titanium dioxide (TiO2). The model was calibrated against the concentrations of PCE, trichloroethylene, methylene chloride, and chloride ion, obtained from the experiments. The proposition for the rate constants is based on fitting the model result to the experimental data. The model was further verified by comparing the model results against a different set of experimental data. The model results against available data indicated good agreement within experimental variations. The modeling suggests that PCE is more susceptible to the C-C double bond cleavage than the C-Cl bond cleavage. The results also support the assumptions that chlorinated methane compounds can be degraded at higher rates than chlorinated ethylene and that the values of the degradation rate constants increase with a decreasing number of chlorine atoms attached to the hydrocarbon compound.     

Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources

Awareness of the construction environment can be improved by automatic three-dimensional (3D) sensing and modeling of job sites in real time. Commercially available 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model dynamic objects in real time in an environment comprised of moving humans, equipment, and materials. Emerging prototype video range cameras offer an alternative by facilitating affordable, wide field of view, dynamic object tracking at frame rates better than 1?Hz (real time). This paper describes a methodology to model, detect, and track the position of static and moving objects in real time, based on data obtained from video range cameras. Experiments with this technology have produced results that indicate that video rate 3D data acquisition and analysis of construction environments can support effective modeling, detection, and tracking of project resources. This approach to job site awareness has inherent value and broad application. In combination with effective management practices and other sensing techniques, this technology has the potential to significantly improve safety on construction job sites.