Application of remote sensing and GIS in mineral resource mapping - An overview

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Age determination in individual wild-caught Drosophila serrata using pteridine concentration

Fluorescence spectrophotometry can reliably detect levels of the pteridine 6-biopterin in the heads of individual Drosophila serrata Malloch 1927. Pteridine content in both laboratory and field captured flies is typically a level of magnitude higher than the minimally detectable level (mean(lab)=0.54 units, mean(field)=0.44 units, minimum detectable level=0.01 units) and can be used to predict individual age in laboratory populations with high certainty (r(2)=57%). Laboratory studies of individuals of known age ( from 1 to 48 days old) indicate that while pteridine level increases linearly with age, they also increase in a linear manner with rearing temperature and ambient light levels, but are independent of sex. As expected, the longevity of laboratory-reared males ( at least 48 days) is higher than the range of predicted ages of wild-caught males based on individual pteridine levels (40 days). However, the predictive equation based on pteridine level alone suggested that a number of wild-caught males were less than 0 days old, and the 95% confidence for these predictions based on the inverse regression broad. The age of the oldest wild-caught male is to fall within the range of 2 to 50 days. The effects of temperature and light intensity determined in laboratory study (effect sizes omega(2)=14.3 and respectively) suggests that the calibration of the prediction equation for field populations would significantly improved when combined with fine scaled studies of habitat temperature and light conditions. ability to determine relative age in individual wild-caught D. serrata presents great opportunities for a variety evolutionary studies on the dynamics of populations.

Non-linear principal components analysis: an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia, Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore. three geographical areas with unique environmental characteristics could be identified. Copyright (c) 2005 John Wiley & Sons, Ltd.

Using multivariate analysis to predict the behaviour of soils under effluent irrigation

Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).

A class of self-stabilizing MCA learning algorithms

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.

Mixtures of probabilistic principal component analysers

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal regression is the posterior mean and its computation scales as O(n³), where n is the sample size. We show that the optimal m-dimensional linear model under a given prior is spanned by the first m eigenfunctions of a covariance operator, which is a trace-class operator. This is an infinite dimensional analogue of principal component analysis. The importance of Hilbert space methods to practical statistics is also discussed.

AC self-bearing motors with a bridge configured winding

Magnetic levitation bearings eliminate friction, wear and the need for lubrication and so have high speed capability and potential for vibration control. One noteworthy development in the realm of magnetic levitation is the self-bearing or bearingless motor - an electromagnetic machine that supports its own rotor by way of magnetic forces generated by windings on its stator. Accordingly, various winding schemes have been proposed to accomplish the task of force production. This thesis proposes a novel concept of winding based on a bridge connection for polyphase self-bearing rotating electrical machines with the following advantages: • the connection uses a single set of windings and thus power loss is relatively low when compared with self-bearing motors with conventional dual set of windings. • the motor and levitation controls are segregated such that only one motor inverter is required for the normal torque production and levitation forces are produced by using auxiliary power supplies of relatively low current and voltage rating. The usual way of controlling the motor is retained. • there are many variant winding schemes to meet special needs. • independent power supplies for levitation control offer redundancy for fault tolerance. This thesis dwells specifically on the conceptual design and implementation of the proposed single set of windings scheme. The new connection has been verified to exhibit characteristics of a self-bearing motor via coupled-field finite element analysis: results are crosschecked analytically. Power loss and other aspects such as cost, design implementation are compared to support the newly proposed connection as a potential alternative to present designs.

Feminist Post-structuralist discourse analysis: a new theoretical and methodological approach?

This chapter serves three very important functions within this collection. First, it aims to make the existence of FPDA better known to both gender and language researchers and to the wider community of discourse analysts, by outlining FPDA’s own theoretical and methodological approaches. This involves locating and positioning FPDA in relation, yet in contradistinction to, the fields of discourse analysis to which it is most often compared: Critical Discourse Analysis (CDA) and, to a lesser extent, Conversation Analysis (CA). Secondly, the chapter serves a vital symbolic function. It aims to contest the authority of the more established theoretical and methodological approaches represented in this collection, which currently dominate the field of discourse analysis. FPDA considers that an established field like gender and language study will only thrive and develop if it is receptive to new ways of thinking, divergent methods of study, and approaches that question and contest received wisdoms or established methods. Thirdly, the chapter aims to introduce some new, experimental and ground-breaking FPDA work, including that by Harold Castañeda-Peña and Laurel Kamada (same volume). I indicate the different ways in which a number of young scholars are imaginatively developing the possibilities of an FPDA approach to their specific gender and language projects.

Exploiting sensitivity of nonorthogonal joint diagonalisation as a security mechanism in steganography

We have recently proposed the framework of independent blind source separation as an advantageous approach to steganography. Amongst the several characteristics noted was a sensitivity to message reconstruction due to small perturbations in the sources. This characteristic is not common in most other approaches to steganography. In this paper we discuss how this sensitivity relates the joint diagonalisation inside the independent component approach, and reliance on exact knowledge of secret information, and how it can be used as an additional and inherent security mechanism against malicious attack to discovery of the hidden messages. The paper therefore provides an enhanced mechanism that can be used for e-document forensic analysis and can be applied to different dimensionality digital data media. In this paper we use a low dimensional example of biomedical time series as might occur in the electronic patient health record, where protection of the private patient information is paramount.

A novel detection method for determining a surgeon's fatigue and hand tremor

The thesis presents new methodology and algorithms that can be used to analyse and measure the hand tremor and fatigue of surgeons while performing surgery. This will assist them in deriving useful information about their fatigue levels, and make them aware of the changes in their tool point accuracies. This thesis proposes that muscular changes of surgeons, which occur through a day of operating, can be monitored using Electromyography (EMG) signals. The multi-channel EMG signals are measured at different muscles in the upper arm of surgeons. The dependence of EMG signals has been examined to test the hypothesis that EMG signals are coupled with and dependent on each other. The results demonstrated that EMG signals collected from different channels while mimicking an operating posture are independent. Consequently, single channel fatigue analysis has been performed. In measuring hand tremor, a new method for determining the maximum tremor amplitude using Principal Component Analysis (PCA) and a new technique to detrend acceleration signals using Empirical Mode Decomposition algorithm were introduced. This tremor determination method is more representative for surgeons and it is suggested as an alternative fatigue measure. This was combined with the complexity analysis method, and applied to surgically captured data to determine if operating has an effect on a surgeon’s fatigue and tremor levels. It was found that surgical tremor and fatigue are developed throughout a day of operating and that this could be determined based solely on their initial values. Finally, several Nonlinear AutoRegressive with eXogenous inputs (NARX) neural networks were evaluated. The results suggest that it is possible to monitor surgeon tremor variations during surgery from their EMG fatigue measurements.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Salmonella in companion animals

In an increasingly hygiene concerned society, a major barrier to pet ownership is the perceived role of companion animals in contributing to the risk of exposure to zoonotic bacterial pathogens, such as Salmonella. Manifestations of Salmonella can range from acute gastroenteritis to perfuse enteric fever, in both humans and dogs. Dogs are heavily associated with asymptomatic carriage of Salmonella as the microorganism can persist in the lower intestines of this host which can be then excreted into the environment. Studies in to the asymptomatic carriage of Salmonella in dogs are somewhat dated and there is limited UK data. The current UK carriage rate in dogs was investigated in a randomised dog population and it was revealed that the carriage rate in this population was very low with only one household dog positive for the carriage of Salmonella enterica arizonae (0.2%), out of 490 dogs sampled. Salmonella serotypes share phenotypic and genotypic similarities which are captured in epidemiological typing methods. Therefore, in parallel to the epidemiological investigations, a panel of clinical canine (VLA, UK) and human (Aston University, UK) Salmonella isolates were profiled based on their phenotypic and genotypic characteristics; using API 20E, Biolog Microbial ID System, antibiotic sensitivity testing and PFGE, respectively. Antibiotic sensitivity testing revealed a significant difference between the canine and human isolates with the canine group demonstrating a higher resistance to the panel of antibiotics tested. Further metabolic capabilities of the strains were tested using the Biolog Microbial ID System, which reveal no clear association between the two host groups. However, coupled with Principle Component Analysis two canine isolates were discriminated from the entire population on the basis of a high up-regulation of two carbohydrates. API 20E testing revealed no association between the two host groups. A PFGE harmonised protocol was used to genotypically profile the strains. A dendrogram depicting PFGE profiles of the panel of Salmonella isolates was performed where similarities were calculated by Dice coefficient and represented by UPGMA clustering. Clustering of the profiles from canine isolates and human isolates (HPA, UK) was diverse representing a natural heterogeneity of the genus, additionally, no clear clustering of the isolates was observed between host groups. Clustering was observed with isolates from the same serotype, independent of host origin. Host adaption is a common phenomenon in certain Salmonella serotypes, for example S. Typhi in humans and S. Dublin in cattle. It was of interest to investigate potential host adaptive or restricted strains for canine host by performing adhesion and invasion assays on Dog Intestinal Epithelial Cells (DIECs) (WALTHAM®, UK) and human CaCo-2 (HPA, UK) cell lines. Salmonella arizonae and Enteritidis from an asymptomatic dog and clinical isolate, respectively, demonstrated a significantly high proportion of invasion in DIEC in comparison to human CaCo-2 cells and other tested Salmonella serotypes. This may be suggestive of a potential host restrictive strain as their ability to invade the CaCo-2 cell line was significantly lower than the other serotypes. In conclusion to this thesis the investigations carried out suggest that asymptomatic carriage of Salmonella in UK dogs is low however the microorganism remains as a zoonotic and anthroponotic pathogen based on phenotypic and genotypic characterisation however there may be potential for particular serotype to become host restricted as observed in invasion assays

Reasons for non-adherence to antiretroviral therapy:patients' perspectives provide evidence of multiple causes

The objective of the study was to define common reasons for non-adherence (NA) to highly active antiretroviral therapy (HAART) and the number of reasons reported by non-adherent individuals. A confidential questionnaire was administered to HIV-seropositive patients taking proteinase inhibitor based HAART. Median self-reported adherence was 95% (n = 178, range = 60-100%). The most frequent reasons for at least 'sometimes' missing a dose were eating a meal at the wrong time (38.2%), oversleeping (36.3%), forgetting (35.0%) and being in a social situation (30.5%). The mean number of reasons occurring at least 'sometimes' was 3.2; 20% of patients gave six or more reasons; those reporting the lowest adherence reported a significantly greater numbers of reasons (ρ = - 0.59; p < 0.001). Three factors were derived from the data by principal component analysis reflecting 'negative experiences of HAART', 'having a low priority for taking medication' and 'unintentionally missing doses', accounting for 53.8% of the variance. On multivariate analysis only the latter two factors were significantly related to NA (odds ratios 0.845 and 0.849, respectively). There was a wide spectrum of reasons for NA in our population. The number of reasons in an individual increased as adherence became less. A variety of modalities individualized for each patient are required to support patients with the lowest adherence.