The sensitivity of estimates of regression to the mean

Estimations of the effectiveness of remedial treatments in road safety analysis are frequently bedevilled by the problem of regression to the mean (RTM). The number of accidents x observed at a site in the “before” period is a “noisy” quantity: x is Poisson distributed about an (unknown) true mean m for that site, so that x = m + e. Sites selected for treatment tend to have a positive random error component e, which will on average be zero in the “after” period, even if no treatment is applied.Methods for estimating RTM usually require some assumption about the underlying (prior) between-site distribution of the true means f₀(m): for example, in the empirical Bayes method, a gamma distribution is assumed. The paper considers the impact of different assumptions for this distribution and, indeed, whether any distributional form needs to be assumed. Using Markov Chain Monte Carlo methods, a variety of distributional forms are assumed for f₀(m) and applied to each of a number of real data sets, including that from a major study on the effectiveness of speed cameras. It is shown that, in some cases, the size of the estimated RTM effect can be quite sensitive to the choice of distribution.     

Predictive models of forest logging residues in Romanian spruce and beech forests

Forest logging residues are systematically left after exploitation. In Romania, logging residues were traditionally used by population for fuel but have not been considered at large scale for industrialization. The estimation of the resource needed a more accurate assessment and the development of devoted biomass models for large-scale applications. Our study aims at estimating the amount of logging residues based on direct biomass measurements for the two main species of Romanian Carpathian forests: Norway spruce and beech. A country-scale field measurement campaign resulted in the sampling of 100 Norway spruce and 74 beech trees. Models of logging residues biomass were developed for both species. The amount of potential logging residues per tree was greater in beech than in Norway spruce. The models developed, nonlinear by essence, showed that diameter-based equations enable the evaluation of individual logging residues potential. Using tree height as an additional independent variable did not improve the models. The models fitted were applied to yield tables in order to estimate the resource potential in spruce and beech stands for each productivity class, and its dynamic during the production cycle. The calculations proved that the potential amount of logging residue is larger in spruce stands. The amount in beech is very sensitive to the productivity class, unlike in spruce stands. The potential biomass produced during early thinnings is however greater in beech stands than in spruce ones. A more systematic and organized collecting of residues could offer a fast answer to the need of increasing renewable energy share.     

Novel models of intertemporal valuation: Past and future outcomes.

Temporal discounting refers to the reduction in the present subjective value of an outcome as a function of the temporal distance to that outcome. Though a number of mathematical models have been proposed to describe this time/value relationship, this search has largely excluded insights from the literature on memory decay. This study examines the utility of memory decay models by comparing the fits of 4 of these models to fits from established temporal discounting models using past and future temporal discounting data. These results (a) suggest that a single model describes valuation of both future and past outcomes; (b) indicate the exponential-power model, from memory decay literature, is statistically superior in fitting discounting data from both past and future outcomes; and (c) support the advancing perspective of the psychological interconnectedness of the future and past. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Water Treatment Units Residence-Time Distributions as Probabilistic Functions

A probabilistic approach to obtain theoretical residence-time distribution (RTD) functions for series of reactors with possible stagnation, bypassing, and recycle is presented. It is shown that most known RTD functions can be obtained from probability arguments alone, thus avoiding abstract Laplace transform mathematical techniques and providing additional physical insight. Several new RTD models for reactors in series are derived, based exclusively on using the binomial probability distribution to describe the passage of a particle through the treatment train with bypassing and stagnation possible at each individual reactor. The proposed RTD models are validated with travel time computer simulation of a large number of particles through the series of reactors. A MSExcel based computer procedure was programmed to obtain the nonideal flow parameters by minimizing the squared sum of the differences between tracer test data and the derived unit’s RTD function. The least squares parameter estimation procedure was used to fit theoretical RTDs to tracer data collected from real water treatment units with different hydraulic behavior at two locations in Mexico.     

Fertigation in Furrows and Level Furrow Systems. II: Field Experiments, Model Calibration, and Practical Applications

Furrow fertigation can be an interesting practice when compared to traditional overland fertilizer application. In the first paper of this series, a model for furrow fertigation was presented. The simulation model combined overland water flow (Saint-Venant equations), solute transport (advection-dispersion), and infiltration. Particular attention was paid to the treatment of junctions present in level furrow systems. In this paper, the proposed model is validated using five furrow fertigation evaluations differing in irrigation discharge, fertilizer application timing, and furrow geometry. Model parameters for infiltration and roughness were estimated using error minimization techniques. The error norm was based on observed and simulated values of advance time, flow depth, and fertilizer concentration. Model parameters could be adequately predicted from just one discharge experiment, although the use of more experiments resulted in decreased error. The validated model was applied to the simulation of a level furrow system from the literature. The model adequately reproduced irrigation advance and flow depth. Fertigation events differing in application timing were simulated to identify conditions leading to adequate fertilizer uniformity.     

Particle Image Velocimetry Study of Flow near Trashrack Models

This paper provides results of an experimental study of turbulent flow near trashrack models that are comprised of an array of three rectangular bars. The bar thickness, bar depth, and center-to-center spacing were maintained constant. The flow characteristics were studied by aligning the bars with the approach flow and conducting measurements at three different approach freestream velocities. Subsequently, the freestream velocity was kept constant and detailed measurements were conducted for four different bar inclinations relative to the approach flow. For each test condition, a high-resolution particle image velocimetry (PIV) technique was used to conduct detailed velocity measurements in streamwise-spanwise planes at middepth of flow. From these measurements, isocontours and profiles of the mean velocities, turbulence intensities, Reynolds shear stress, and production term in the transport equation for the turbulent kinetic energy were obtained to study the flow characteristics around and downstream of the aligned and inclined bars. Flow characteristics near hydroelectric station trashracks are important for efficient turbine operation and reduction of fish entrainment.     

3D scene retrieval and recognition with Depth Gradient Images

The intention of the strategy proposed in this paper is to solve the object retrieval problem in highly complex scenes using 3D information. In the worst case scenario the complexity of the scene includes several objects with irregular or free-form shapes, viewed from any direction, which are self-occluded or partially occluded by other objects with which they are in contact and whose appearance is uniform in intensity/color. This paper introduces and analyzes a new 3D recognition/pose strategy based on DGI (Depth Gradient Images) models. After comparing it with current representative techniques, we can affirm that DGI has very interesting prospects.The DGI representation synthesizes both surface and contour information, thus avoiding restrictions concerning the layout and visibility of the objects in the scene. This paper first explains the key concepts of the DGI representation and shows the main properties of this method in comparison to a set of known techniques. The performance of this strategy in real scenes is then reported. Details are also presented of a wide set of experimental tests, including results under occlusion, performance with injected noise and experiments with cluttered scenes of a high level of complexity.     

Model-based prognostics of gear health using stochastic dynamical models

In this paper we present a statistical approach to estimating the time in which an operating gear will reach a critical stage. The approach relies on measured vibration signals. From these signals features are first extracted and then their evolution over time is predicted. This is done based on a dynamic model that relates hidden degradation phenomena to measured outputs. The Expectation-Maximization algorithm is used to estimate the parameters of the underlying state-space model on line. The time to reach the safety alarm threshold is determined by estimating the distribution of the remaining useful life using the estimated linear model. The results obtained on a pilot test bed are presented.     

USGS Streamflow Data and Modeling Sand-Bed Rivers

United States Geological Survey streamflow data are commonly used for hydraulic model calibration and boundary conditions. The transitory nature of sand-bed rivers’ bathymetry is problematic for the traditional automated stream gauging methods used by the USGS. This note seeks to assess the limitations of streamflow measurements for use in hydraulic models. An overview of USGS rating-curve development and use is presented with a focus on the specific challenges of sand-bed rivers. Measurements from three consecutive USGS gauges for a storm event on the Rio Grande in Albuquerque, New Mexico, illustrate the outlined problems with rating curves. These gauges are utilized to study the impact of uncertainty in rating-curve discharges on hydraulic model results. A one dimensional hydraulic model of the study reach indicates up to 25% reduction in the calculated flow depth if questionable rating-curve discharges are used as model input. Recommendations for using USGS streamflow data in hydraulic models are outlined.     

A dimensionless approach for the urban-scale evaluation of domestic rainwater harvesting systems for toilet flushing and garden irrigation

A dimensionless methodology to evaluate the water saving obtainable from large-scale implementation of domestic rain water harvesting (RWH) systems in urban areas is presented. The methodology combines the use of regressive relationships for water saving evaluation based on the results of the dimensionless rainwater tank water balance and of catchment-wide information obtained from geospatial databases. The adopted RWH scheme included internal use of rainwater for toilet flushing and external use for garden irrigation. An application to a portion of the city of Rome, Italy showed the methodology to allow systematic and accurate evaluation of RWH system performance at the selected urban scale. Results pointed out high water saving potential for toilet flushing ranging between 38–65% for tank sizes within 1–50 m³. Furthermore, more than one third of the systems provided water saving benefit for irrigation larger than 20% by using a 50 m³ tank.