How Porosity and Permeability Vary Spatially With Grain Size, Sorting, Cement Volume, and Mineral Dissolution In Fluvial Triassic Sandstones: The Value of Geostatistics and Local Regression

Although it is well known that sandstone porosity and permeability are controlled by a range of parameters such as grain size and sorting, amount, type, and location of diagenetic cements, extent and type of compaction, and the generation of intergranular and intragranular secondary porosity, it is less constrained how these controlling parameters link up in rock volumes (within and between beds) and how they spatially interact to determine porosity and permeability. To address these unknowns, this study examined Triassic fluvial sandstone outcrops from the UK using field logging, probe permeametry of 200 points, and sampling at 100 points on a gridded rock surface. These field observations were supplemented by laser particle-size analysis, thin-section point-count analysis of primary and diagenetic mineralogy, quantitiative XRD mineral analysis, and SEM/EDAX analysis of all 100 samples. These data were analyzed using global regression, variography, kriging, conditional simulation, and geographically weighted regression to examine the spatial relationships between porosity and permeability and their potential controls. The results of bivariate analysis (global regression) of the entire outcrop dataset indicate only a weak correlation between both permeability porosity and their diagenetic and depositional controls and provide very limited information on the role of primary textural structures such as grain size and sorting. Subdividing the dataset further by bedding unit revealed details of more local controls on porosity and permeability. An alternative geostatistical approach combined with a local modelling technique (geographically weighted regression; GWR) subsequently was used to examine the spatial variability of porosity and permeability and their controls. The use of GWR does not require prior knowledge of divisions between bedding units, but the results from GWR broadly concur with results of regression analysis by bedding unit and provide much greater clarity of how porosity and permeability and their controls vary laterally and vertically. The close relationship between depositional lithofacies in each bed, diagenesis, and permeability, porosity demonstrates that each influences the other, and in turn how understanding of reservoir properties is enhanced by integration of paleoenvironmental reconstruction, stratigraphy, mineralogy, and geostatistics.

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.

Predictive models for deterioration of concrete structures

Permeation characteristics and fracture strength are the fundamental properties of concrete that influence the initiation and extent of damage and can form the basis by which deterioration can be predicted. The relationship between these properties and deterioration mechanisms is discussed along with the different models representing their interaction with the environment. Mehta presented a holistic model of the deterioration of concrete based on the environmental action on the microstructure of concrete. Using a similar approach, a detailed investigation on the causes of concrete deterioration is used to develop a macro-model for each mechanism relating to the physical properties of concrete. A single interaction model is then presented for all types of deterioration, emphasizing the permeation properties of concrete. Data from an in situ investigation of concrete bridges in Northern Ireland is used to validate this model. This is followed by a micro-predictive model which includes an ionic transport sub-model, a deterioration sub-model and a structural sub-model and affords quantitative prediction of the deterioration of concrete structures. The quantitative predictive capabilities of the micro-model are demonstrated with the use of reported experimental data.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Common KIF6 Polymorphism Increases Vulnerability to Low-Density Lipoprotein Cholesterol: Two Meta-Analyses and a Meta-Regression Analysis

Background: We sought to determine if a common polymorphism can influence vulnerability to LDL cholesterol, and thereby influence the clinical benefit derived from therapies that reduce LDL cholesterol.

Determining propeller induced erosion alongside quay walls in harbours using Artificial Neural Networks

In shallow waters, such as those found close to berth structures, the wash from a manoeuvring ship’s propeller can cause erosion of the seabed. This erosion can be increased if the wash intersects a berth structure. A number of researchers have undertaken model studies and used regression analysis to develop predictive relationships for the scouring action. This paper presents an experimental investigation with Artificial Neural Networks (ANN’s), used to analyse the results. The purpose of using ANN’s was to examine the prediction accuracy of the Networks in comparison with previous regression analysis methods. ANN’s were found to provide a more accurate method of predicting propeller wash scour than the equations presented by previous investigators.

Predictive position and force control for shape memory alloy cylinders

In this paper, a linear lightweight electric cylinder constructed using shape memory alloy (SMA) is proposed. Spring SMA is used as the actuator to control the position and force of the cylinder rod. The model predictive control algorithm is investigated to compensate SMA hysteresis phenomenon and control the cylinder. In the predictive algorithm, the future output of the cylinder is computed based on the cylinder model, and the control signal is computed to minimize the error and power criterion. The cylinder model parameters are estimated by an online identification algorithm. Experimental results show that the SMA cylinder is able to precisely control position and force by using the predictive control strategy though the hysteresis effect existing in the actuator. The performance of the proposed controller is compared with that of a conventional PID controller