Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Validation and application of a reporter gene assay for the determination of estrogenic endocrine disruptor activity in milk

Endocrine disruptors (EDs) are compounds known to interfere with the endocrine system by disturbing the action or pathways of natural hormones which may lead to infertility or cancer.Our diet is considered to be one of the main exposure routes to EDs. Since milk and dairy products are major components of our diet they should be monitored for ED contamination. Most assays developed to date utilise targeted, chromatography based methods which lack information on the biological activity and mixture effects of the monitored compounds.A biological reporter gene assay (RGA) was developed to assess the total estrogen hormonal load in milk. It has been validated according to EU decision 2002/657/EC. Analytes were extracted by liquid-liquid extraction with acetonitrile followed by clean up on a HLB column which yielded good recovery and small matrix effects. The method has been shown to be estrogen specific, repeatable and reproducible, with covariance values below 20%. In conclusion, this method enables the detection of low levels of estrogen hormonal activity in milk with a detection capability of 36pgg EEQ and has been successfully applied in testing a range of milk samples. © 2014 Elsevier Ltd.

Physical activity, sedentary behaviour and fetal macrosomia in uncomplicated pregnancies: A prospective cohort study

Objective: to explore maternal energy balance, incorporating free living physical activity and sedentary behaviour, in uncomplicated pregnancies at risk of macrosomia.

Methods: a parallel-group cross-sectional analysis was conducted in healthy pregnant women predicted to deliver infants weighing Z4000 g (study group) or o4000 g (control group). Women were recruited in a 1:1 ratio from antenatal clinics in Northern Ireland. Women wore a SenseWears Body Media Pro3 physical activity armband and completed a food diary for four consecutive days in the third trimester. Physical activity was measured in Metabolic Equivalent of Tasks (METs) where 1 MET¼1 kcal per kilogram of body weight per hour. Analysis of covariance (ANCOVA) was employed using the General Linear Model to adjust for potential confounders.

Findings: of the 112 women recruited, 100 complete datasets were available for analysis. There was no significant difference in energy balance between the two groups. Intensity of free living physical activity (average METs) of women predicted to deliver macrosomic infants (n¼50) was significantly lower than that of women in the control group (n¼50) (1.3 (0.2) METs (mean, standard deviation) versus 1.2 (0.2) METs; difference in means 0.1 METs (95% confidence interval: 0.19, 0.01); p¼0.021). Women predicted to deliver macrosomic infants also spent significantly more time in sedentary behaviour (r1 MET) than the control group (16.1 (2.8) hours versus 13.8 (4.3) hours; 2.0 hours (0.3, 3.7), p¼0.020).

Key conclusions and implications for practice: although there was no association between predicted fetal macrosomia and energy balance, those women predicted to deliver a macrosomic infant exhibited increased sedentary behaviour and reduced physical activity in the third trimester of pregnancy. Professionals caring for women during pregnancy have an important role in promoting and supporting more active lifestyles amongst women who are predicted to deliver a macrosomic infant given the known associated risks.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

A technique for optimizing the efficiency of the sub-map method for large-scale simultaneous localization and mapping (SLAM) is proposed. It optimizes the benefits of the sub-map technique to improve the accuracy and consistency of an extended Kalman filter (EKF)-based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing the sub-map technique in SLAM. Such issues include the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error-correcting process were engaged to optimize the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.

Kernel-based local order estimation of nonlinear nonparametric systems

We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NARX), which may have different local dimensions at different points. By minimizing the kernel-based local information criterion introduced in this paper, the strongly consistent estimates for the local orders of the NARX system at points of interest are obtained. The modification of the criterion and a simple procedure of searching the minimum of the criterion, are also discussed. The theoretical results derived here are tested by simulation examples.

Supervised walking in comparison with fitness training for chronic back pain in physiotherapy: results of the SWIFT single-blinded randomized controlled trial (ISRCTN17592092)

Effectiveness of brief/minimal contact self-activation interventions that encourage participation in physical activity (PA) for chronic low back pain (CLBP >12 weeks) is unproven. The primary objective of this assessor-blinded randomized controlled trial was to investigate the difference between an individualized walking programme (WP), group exercise class (EC), and usual physiotherapy (UP, control) in mean change in functional disability at 6 months. A sample of 246 participants with CLBP aged 18 to 65 years (79 men and 167 women; mean age ± SD: 45.4 ± 11.4 years) were recruited from 5 outpatient physiotherapy departments in Dublin, Ireland. Consenting participants completed self-report measures of functional disability, pain, quality of life, psychosocial beliefs, and PA were randomly allocated to the WP (n = 82), EC (n = 83), or UP (n = 81) and followed up at 3 (81%; n = 200), 6 (80.1%; n = 197), and 12 months (76.4%; n = 188). Cost diaries were completed at all follow-ups. An intention-to-treat analysis using a mixed between-within repeated-measures analysis of covariance found significant improvements over time on the Oswestry Disability Index (Primary Outcome), the Numerical Rating Scale, Fear Avoidance-PA scale, and the EuroQol EQ-5D-3L Weighted Health Index (P < 0.05), but no significant between-group differences and small between-group effect sizes (WP: mean difference at 6 months, 6.89 Oswestry Disability Index points, 95% confidence interval [CI] -3.64 to -10.15; EC: -5.91, CI: -2.68 to -9.15; UP: -5.09, CI: -1.93 to -8.24). The WP had the lowest mean costs and the highest level of adherence. Supervised walking provides an effective alternative to current forms of CLBP management.

The impact of rehabilitation using removable partial dentures and functionally orientated treatment on oral health-related quality of life: a randomised controlled clinical trial

OBJECTIVES: This study aimed to compare two different tooth replacement strategies for partially dentate older patients; namely functionally orientated treatment according to the principles of the shortened dental arch (SDA) and conventional treatment using removable partial dentures (RPDs) using a randomised controlled clinical trial. The primary outcome measure for this study was impact on oral health-related quality of life (OHRQoL) measured using the short form of the oral health impact profile (OHIP-14).

METHODS: Patients aged 65 years and older were randomly allocated to two different treatment groups: the RPD group and the SDA group. For the RPD group each patient was restored to complete arches with cobalt-chromium RPDs used to replace missing teeth. For the SDA group, patients were restored to a premolar occlusion of 10 occluding pairs of natural and replacement teeth using resin bonded bridgework (RBB). OHRQoL was measured using the OHIP-14 questionnaire administered at baseline, 1 month, 6 months and 12 months after treatment intervention.

RESULTS: In total, 89 patients completed the RCT: 44 from the RPD group and 45 from the SDA group. Analysis using a mixed model of covariance (ANCOVA) illustrated that treatment according to the SDA concept resulted in significantly better mean OHIP-14 scores compared with RPD treatment (p<0.05). This result was replicated in both treatment centres used in the study.

CONCLUSIONS: In terms of impact on OHRQoL, treatment based on the SDA concept achieved significantly better results than that based on RPDs 12 months after treatment intervention (trial registration no. ISRCTN26302774).

CLINICAL SIGNIFICANCE: Functionally orientated treatment delivery resulted in significantly better outcomes compared to removable dentures in terms of impact on OHRQoL.

Comparison of functionally orientated tooth replacement and removable partial dentures on the nutritional status of partially dentate older patients: a randomised controlled clinical trial

OBJECTIVES: The aims of this study were to conduct a randomised controlled clinical trial (RCT) of partially dentate older adults comparing functionally orientated treatment based on the SDA concept with conventional treatment using RPDs to replace missing natural teeth. The two treatment strategies were evaluated according to their impact on nutritional status measured using haematological biomarkers.

METHODS: A randomised controlled clinical trial (RCT) was conducted of partially dentate patients aged 65 years and older (Trial Registration no. ISRCTN26302774). Each patient provided haematological samples which were screened for biochemical markers of nutritional status. Each sample was tested in Cork University Hospital for serum Albumin, serum Cholesterol, Ferritin, Folate, Vitamin B12 and 25-hydroxycholecalciferol (Vitamin D).

RESULTS: A mixed model analysis of covariance (ANCOVA) indicated that for Vitamin B12 (p=0.9392), serum Folate (p=0.5827), Ferritin (p=0.6964), Albumin (p=0.8179), Serum Total Cholesterol (p=0.3670) and Vitamin D (p=0.7666) there were no statistically significant differences recorded between the two treatment groups. According to the mixed model analysis of covariance (ANCOVA) for Vitamin D there was a significant difference between levels recorded at post-operative time points after treatment intervention (p=0.0470). There was an increase of 7% in 25-hydroxycholecalciferol levels recorded at 6 months compared to baseline (p=0.0172). There was no further change in recorded levels at 12 months (p=0.6482) and these increases were similar within the two treatment groups (p>0.05).

CONCLUSIONS: The only measure which illustrated consistent significant improvements in nutritional status for either group were Vitamin D levels. However no significant difference was recorded between the two treatment groups.

CLINICAL SIGNIFICANCE: Functionally orientated prosthodontic rehabilitation for partially dentate older patients was no worse than conventional removable partial dentures in terms of impact on nutritional status.

Caries incidence following tooth replacement for partially dentate elders

Objective: To compare caries incidence following two different tooth replacement strategies for partially dentate older patients; namely functionally orientated treatment according to the principles of the Shortened Dental Arch (SDA) and conventional treatment using Removable Partial Dentures (RPDs). Method:A randomised controlled clinical trial (RCT) was conducted of partially dentate patients aged 65 years and older. Patients were randomly allocated to two different treatment groups: the RPD group and the SDA group. Each member of the RPD group was restored to complete arches with cobalt-chromium RPDs used to replace missing teeth. Patients in the SDA group were restored to a shortened arch of 10 occluding pairs of natural and replacement teeth using adhesive bridgework. All of the operative treatment was completed by a single operator. Caries incidence was measured over a 2-year period following treatment intervention and recorded using the International Caries and Detection System (ICDAS). Result:In total, 89 patients completed the RCT (45 SDAs and 44 RPDs). Patients in the RPD group recorded a significantly higher incidence of new carious lesions (p<0.001) and recurrent carious lesions (p<0.001) compared to the SDA group. A mixed model of covariance (ANCOVA) revealed that treatment group (p<0.001) and co-morbidity (p<0.001) were significant predictors of caries incidence. Conclusion:Two years after provision of prosthodontic treatment there was a significantly higher incidence of new and recurrent caries lesions in subjects provided with RPDs compared with SDA treatment. This will have a significant impact on the ongoing maintenance costs for these two treatment groups.

Real-time temperature estimation for power MOSFETs considering thermal aging effects

This paper presents a novel real-time power-device temperature estimation method that monitors the power MOSFET's junction temperature shift arising from thermal aging effects and incorporates the updated electrothermal models of power modules into digital controllers. Currently, the real-time estimator is emerging as an important tool for active control of device junction temperature as well as online health monitoring for power electronic systems, but its thermal model fails to address the device's ongoing degradation. Because of a mismatch of coefficients of thermal expansion between layers of power devices, repetitive thermal cycling will cause cracks, voids, and even delamination within the device components, particularly in the solder and thermal grease layers. Consequently, the thermal resistance of power devices will increase, making it possible to use thermal resistance (and junction temperature) as key indicators for condition monitoring and control purposes. In this paper, the predicted device temperature via threshold voltage measurements is compared with the real-time estimated ones, and the difference is attributed to the aging of the device. The thermal models in digital controllers are frequently updated to correct the shift caused by thermal aging effects. Experimental results on three power MOSFETs confirm that the proposed methodologies are effective to incorporate the thermal aging effects in the power-device temperature estimator with good accuracy. The developed adaptive technologies can be applied to other power devices such as IGBTs and SiC MOSFETs, and have significant economic implications. 

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

Inference with multinomial data: why to weaken the prior strength

This paper considers inference from multinomial data and addresses the problem of choosing the strength of the Dirichlet prior under a mean-squared error criterion. We compare the Maxi-mum Likelihood Estimator (MLE) and the most commonly used Bayesian estimators obtained by assuming a prior Dirichlet distribution with non-informative prior parameters, that is, the parameters of the Dirichlet are equal and altogether sum up to the so called strength of the prior. Under this criterion, MLE becomes more preferable than the Bayesian estimators at the increase of the number of categories k of the multinomial, because non-informative Bayesian estimators induce a region where they are dominant that quickly shrinks with the increase of k. This can be avoided if the strength of the prior is not kept constant but decreased with the number of categories. We argue that the strength should decrease at least k times faster than usual estimators do.

Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

Geostatistical Fisher discriminant analysis

A geostatistical version of the classical Fisher rule (linear discriminant analysis) is presented.This method is applicable when a large dataset of multivariate observations is available within a domain split in several known subdomains, and it assumes that the variograms (or covariance functions) are comparable between subdomains, which only differ in the mean values of the available variables. The method consists on finding the eigen-decomposition of the matrix W-1B, where W is the matrix of sills of all direct- and cross-variograms, and B is the covariance matrix of the vectors of weighted means within each subdomain, obtained by generalized least squares. The method is used to map peat blanket occurrence in Northern Ireland, with data from the Tellus
survey, which requires a minimal change to the general recipe: to use compositionally-compliant variogram tools and models, and work with log-ratio transformed data.