Keele University

About: Keele University is a education organization based out in Newcastle-under-Lyme, United Kingdom. It is known for research contribution in the topics: Population & Stars. The organization has 11318 authors who have published 26323 publications receiving 894671 citations. The organization is also known as: Keele University.

Classification of airborne laser scanning data using JointBoost

Abstract: The demands for automatic point cloud classification have dramatically increased with the wide-spread use of airborne LiDAR. Existing research has mainly concentrated on a few dominant objects such as terrain, buildings and vegetation. In addition to those key objects, this paper proposes a supervised classification method to identify other types of objects including power-lines and pylons from point clouds using a JointBoost classifier. The parameters for the learning model are estimated with various features computed based on the geometry and echo information of a LiDAR point cloud. In order to overcome the shortcomings stemming from the inclusion of bare ground data before classification, the proposed classifier directly distinguishes terrain using a feature step-off count. Feature selection is conducted using JointBoost to evaluate feature correlations thus improving both classification accuracy and operational efficiency. In this paper, the contextual constraints for objects extracted by graph-cut segmentation are used to optimize the initial classification results obtained by the JointBoost classifier. Our experimental results show that the step-off count significantly contributes to classification. Seventeen effective features are selected for the initial classification results using the JointBoost classifier. Our experiments indicate that the proposed features and method are effective for classification of airborne LiDAR data from complex scenarios.

The Novel Selected-ion Flow Tube Approach to Trace Gas Analysis of Air and Breath

Abstract: We present an overview of the development and use of our selected-ion flow tube (SIFT) technique as a sensitive, quantitative method for the rapid, real-time analysis of the trace gas content of atmospheric air and human breath, presenting some pilot data from various research areas in which this method will find valuable application. We show that it is capable of detecting and quantifying trace gases, in complex mixtures such as breath, which are present at partial pressures down to about 10 parts per billion. Following discussions of the principles involved in this SIFT method of analysis, of the experiments which we have carried out to establish its quantitative validity, and of the air and breath sampling techniques involved, we present sample data on the detection and quantification of trace gases on the breath of healthy people and of patients suffering from renal failure and diabetes. We also show how breath ammonia can be accurately quantified from a single breath exhalation and used as an indicator of the presence in the stomach of the bacterium Helicobacter pylori. Health and safety applications are exemplified by analyses of the gases of the gases of cigarette smoke and on the breath of smokers. The value of this analytical method in environmental science is demonstrated by the analyses of petrol vapour, car exhaust emissions and the trace organic vapours detected in town air near a busy road. Final examples of the value of this analytical method are the detection and quantification of the gases emitted from crushed garlic and from breath following the chewing of a mint, which demonstrate its potential in food and flavour research. Throughout the paper we stress the advantages of this SIFT method compared to conventional mass spectrometry for trace gas analysis of complex mixtures, emphasizing its selectivity, sensitivity and real-time analysis capability. Finally, we note that whilst the current SIFT is strictly laboratory based, both transportable and portable instruments are under construction and development. These instruments will surely extend the application of this analytical technique into more areas and allow greater exploitation of their on-line and real-time features.

Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data

Abstract: Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies. Performance of the models was assessed in terms of estimability (percentage of meta-analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted. Alternatively, an HSROC model that assumes a symmetric SROC curve (by excluding the shape parameter) can be used if the HSROC model is the chosen meta-analytic approach. In the absence of heterogeneity, fixed effect equivalent of the models can be applied.

The use of quantitative and qualitative criteria in the measurement of performance in small firms

Abstract: Concern has been expressed, over the years, about the financial management strategies adopted by small firms, but very little is known about these practices. Business performance measures are an important element of these financial management strategies. The paper discusses the findings from research carried out in the UK examining the quantitative and qualitative criteria in the measurement of performance in small firms. Semi‐structured interviews were carried out with 20 owner‐managers from both manufacturing and service sectors. Orthodox theory assumes that the objective of the firm is to maximise profits, and it follows that the performance measures advocated are largely based upon this theory. However, research has shown that small firms pursue a range of goals. It was, therefore, not surprising to find that owner‐managers of small firms used a variety of measures and indicators to assess business performance. Profit measures were found to be less important than conventional views suggest. In particular, cash flow indicators were considered to be critical. Other performance measures adopted by owner‐managers include the quality of inputs and outputs and intangible indicators.