Limitations of instantaneous water quality sampling in surface-water catchments: Comparison with near-continuous phosphorus time-series data.

The validity of load estimates from intermittent, instantaneous grab sampling is dependent on adequate spatial coverage by monitoring networks and a sampling frequency that re?ects the variability in the system under study. Catchments with a ?ashy hydrology due to surface runoff pose a particular challenge as intense short duration rainfall events may account for a signi?cant portion of the total diffuse transfer of pollution from soil to water in any hydrological year. This can also be exacerbated by the presence of strong background pollution signals from point sources during low flows. In this paper, a range of sampling methodologies and load estimation techniques are applied to phosphorus data from such a surface water dominated river system, instrumented at three sub-catchments (ranging from 3 to 5 km2 in area) with near-continuous monitoring stations. Systematic and Monte Carlo approaches were applied to simulate grab sampling using multiple strategies and to calculate an estimated load, Le based on established load estimation methods. Comparison with the actual load, Lt, revealed signi?cant average underestimation, of up to 60%, and high variability for all feasible sampling approaches. Further analysis of the time series provides an insight into these observations; revealing peak frequencies and power-law scaling in the distributions of P concentration, discharge and load associated with surface runoff and background transfers. Results indicate that only near-continuous monitoring that re?ects the rapid temporal changes in these river systems is adequate for comparative monitoring and evaluation purposes. While the implications of this analysis may be more tenable to small scale ?ashy systems, this represents an appropriate scale in terms of evaluating catchment mitigation strategies such as agri-environmental policies for managing diffuse P transfers in complex landscapes.

Enhancement Factor for Gas Absorption in a Finite Liquid Layer. Part 1: Instantaneous Reaction in a Liquid in Plug Flow

An approximate analysis of gas absorption with instantaneous reaction in a liquid layer of finite thickness in plug flow is presented. An approximate solution to the enhancement factor for the case of unequal diffusivities between the dissolved gas and the liquid reactant has been derived and validated by numerical simulation. Depending on the diffusivity ratio of the liquid reactant to the dissolved gas (?), the enhancement factor tends to be either lower or higher than the prediction of the classical enhancement factor equation based on the penetration theory (Ei,pen) at Fourier numbers typically larger than 0.1. An empirical correlation valid for all Fourier numbers is proposed to allow a quick estimation of the enhancement factor, which describes the prediction of the approximate solution and the simulation data with a relative error below 5?% under the investigated conditions (? = 0.34, Ei,pen = 21000).

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).

Fluence dependence of the surface roughness of InP after N2 + bombardment

InP(1 0 0) surfaces were sputtered under ultrahigh vacuum conditions by 5 keV N2+ ions at an angle of incidence of 41° to the sample normal. The fluence, φ, used in this study, varied from 1 × 10¹⁴ to 5 × 10¹⁸ N2+ cm^-2. The surface topography was investigated using field emission scanning electron microscopy (FE-SEM) and atomic force microscopy (AFM). At the lower fluences (φ ≤ 5 × 10¹⁶ N2+ cm^-2) only conelike features appeared, similar in shape as was found for noble gas ion bombardment of InP. At the higher fluences, ripples also appeared on the surface. The bombardment-induced topography was quantified using the rms roughness. This parameter showed a linear relationship with the logarithm of the fluence. A model is presented to explain this relationship. The ripple wavelength was also determined using a Fourier transform method. These measurements as a function of fluence do not agree with the predictions of the Bradley-Harper theory. © 2004 Elsevier B.V. All rights reserved.

An adaptive motion model for person tracking with instantaneous head-pose features

This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous `intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.