Statistical inference in functional semiparametric spatial autoregressive model

<abstract><p>Semiparametric spatial autoregressive model has drawn great attention since it allows mutual dependence in spatial form and nonlinear effects of covariates. However, with development of scientific technology, there exist functional covariates with high dimensions and frequencies containing rich information. Based on high-dimensional covariates, we propose an interesting and novel functional semiparametric spatial autoregressive model. We use B-spline basis function to approximate the slope function and nonparametric function and propose generalized method of moments to estimate parameters. Under certain regularity conditions, the asymptotic properties of the proposed estimators are obtained. The estimators are computationally convenient with closed-form expression. For slope function and nonparametric function estimators, we propose the residual-based approach to derive its pointwise confidence interval. Simulation studies show that the proposed method performs well.</p></abstract>

Estimation in Partial Functional Linear Spatial Autoregressive Model

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when response variables are dependence spatial variables. In this paper, we introduce a new partial functional linear spatial autoregressive model which explores the relationship between a scalar dependence spatial response variable and explanatory variables containing both multiple real-valued scalar variables and a function-valued random variable. By means of functional principal components analysis and the instrumental variable estimation method, we obtain the estimators of the parametric component and slope function of the model. Under some regularity conditions, we establish the asymptotic normality for the parametric component and the convergence rate for slope function. At last, we illustrate the finite sample performance of our proposed methods with some simulation studies.

Nanoparticle Assisted EOR during Sand-Pack Flooding: Electrical Tomography to Assess Flow Dynamics and Oil Recovery

Silica nanoparticles have been shown to exhibit many characteristics that allow for additional oil to be recovered during sand-pack flooding experiments. Additionally various imaging techniques have been employed in the past to visually compare flooding procedures including x-ray computed tomography and magnetic resonance imaging; however, these techniques require the sample to be destroyed or sliced after the flooding experiment finishes. Electrical resistance tomography (ERT) overcomes these limitations by offering a non-destructive visualization method allowing for online images to be taken during the flooding process by the determination of spatial distribution of electrical resistivity, thus making it suitable for sand-packs. During the scope of this research a new sand-pack system and methodology was created which utilized ERT as a monitoring tool. Two concentrations, 0.5 wt% and 1.0 wt%, of SiO2 nanoparticles were compared with runs using only brine to compare the recovery efficiency and explore the ability of ERT to monitor the flooding process. Electrical resistance tomography was found to be an effective tool in monitoring local recovery efficiency revealing 1.0 wt% SiO2 to be more effective than 0.5 wt% and brine only runs during the scope of this research. A new method involving the slope function in excel was used to compare the effects of nanofluids on resistivity trends also revealing information about the rate of recovery against time. SiO2 nanofluid recovery mechanisms such interfacial tension reduction and viscosity enhancement were then considered to explain why the nanofluids resulted in greater oil recovery.

Genetic control of transition from juvenile to mature wood with respect to microfibril angle (MFA) in Norway spruce (Picea abies) and lodgepole pine (Pinus contorta)

Genetic control of microfibril angle (MFA) transition from juvenile to mature was evaluated in Norway spruce and lodgepole pine. Increment cores were collected at breast height from 5,618 trees in two 21-year-old Norway spruce progeny trials in southern Sweden, and from 823 trees in two 34-35 – year-old lodgepole pine progeny trials in northern Sweden. Radial variations in MFA from pith to bark were measured for each core using SilviScan. To estimate MFA transition from juvenile to mature, a threshold level of MFA 20° was considered and six different regression functions were fitted to the MFA profile of each tree after exclusion of outliers, following three steps. The narrow-sense heritability estimates (h2) obtained for MFA transition were highest based on the slope function, ranging from 0.21 to 0.23 for Norway spruce and from 0.34 to 0.53 for lodgepole pine, while h2 were mostly non-significant based on the logistic function, under all exclusion methods. Results of this study indicate that it is possible to select for an earlier MFA transition from juvenile to mature in Norway spruce and lodgepole pine selective breeding programs, as the genetic gains (∆G) obtained in direct selection of this trait were very high in both species.

An Improved Dynamic Friction Model Using a Data-Based Approach

A significant challenge associated with the development of precision motion control systems is the identification and modeling of friction. In particular, dynamic (presliding) friction is often difficult to accurately model in both the time domain and frequency domain simultaneously. We present a data-based modification to an existing friction model, known as the Dahl Dynamic Hysteresis Model (DHM), which incorporates an empirical friction slope function to provide a more accurate representation of arbitrarily shaped hysteresis curves. This data-based approach avoids the added complexity of identifying or fitting model parameters, and can be implemented with a simple look up table. Simulation results are validated with measured friction data collected from an experimental testbed. We show that the data-based approach significantly improves the friction model accuracy in both the time and frequency domains.

THE DOSE-RESPONSE HILL SLOPE USED IN THE AQUATIC TOXICITY MODULE OF OIL SPILL MODELS SHOULD BE MUCH STEEPER

ABSTRACT One useful tool for assessing the potential adverse effects of oil spills on aquatic and marine environments are computational models that calculate the transport, fate and effects of substances. The use of computational models is authorized by 43 CFR 11, and has been codified in the Natural Resources Damage Assessment for Coastal and Marine Environments (NRDAM/CME). The code in the NRDAM/CME is incorporated into modules that compute how a spilled substance moves through a marine environment and affects biological resources. This paper describes an apparent error in one module of the NRDAM code, the one that calculates the mortality rate of exposed organisms to concentrations that are less than the medial lethal concentration (LC50) of a mixture of dissolved petroleum hydrocarbons. Based on a review of the original publication, we have concluded the original mean “hill slopes” range from 4.34 to 24.16, which are much greater than the slope (functions) of 1.1 to 1.7 that are used in a module of the NRDAM/CME code. The use of a probit slope function as a logistic slope has the effect of overestimating the mortality rate for marine species exposed to low hydrocarbon concentrations and underestimating mortality of marine species exposed to concentrations greater than the LC50. The “shallow” slope function also calculates a much lower threshold concentration at which adverse effects are expected, than is supported by slope values indicated by the original source. The error in the slope of this module may also explain, in part, why field personnel have not observed fish kills when the NRDAM/CME has predicted mortalities occurred.

A Multi-Slope Sliding-Mode Control Approach for Single-Phase Inverters under Different Loads

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.