Earthmoving trucks condition level prediction using neural networks

Purpose: The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model’s performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

Design/methodology/approach: An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Findings: Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Originality/value: Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.

The TORCH (towards a revolution in COPD health) survival study protocol

Only long-term home oxygen therapy has been shown in randomised controlled trials to increase survival in chronic obstructive pulmonary disease (COPD). There have been no trials assessing the effect of inhaled corticosteroids and long-acting bronchodilators, alone or in combination, on mortality in patients with COPD, despite their known benefit in reducing symptoms and exacerbations. The "TOwards a Revolution in COPD Health" (TORCH) survival study is aiming to determine the impact of salmeterol/fluticasone propionate (SFC) combination and the individual components on the survival of COPD patients. TORCH is a multicentre, randomised, double-blind, parallel-group, placebo-controlled study. Approximately 6,200 patients with moderate-to-severe COPD were randomly assigned to b.i.d. treatment with either SFC (50/500 microg), fluticasone propionate (500 microg), salmeterol (50 microg) or placebo for 3 yrs. The primary end-point is all-cause mortality; secondary end-points are COPD morbidity relating to rate of exacerbations and health status, using the St George's Respiratory Questionnaire. Other end-points include other mortality and exacerbation end-points, requirement for long-term oxygen therapy, and clinic lung function. Safety end-points include adverse events, with additional information on bone fractures. The first patient was recruited in September 2000 and results should be available in 2006. This paper describes the "TOwards a Revolution in COPD Health" study and explains the rationale behind it.

Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study

In this study, 137 corn distillers dried grains with solubles (DDGS) samples from a range of different geographical origins (Jilin Province of China, Heilongjiang Province of China, USA and Europe) were collected and analysed. Different near infrared spectrometers combined with different chemometric packages were used in two independent laboratories to investigate the feasibility of classifying geographical origin of DDGS. Base on the same dataset, one laboratory developed a partial least square discriminant analysis model and another laboratory developed an orthogonal partial least square discriminant analysis model. Results showed that both models could perfectly classify DDGS samples from different geographical origins. These promising results encourage the development of larger scale efforts to produce datasets which can be used to differentiate the geographical origin of DDGS and such efforts are required to provide higher level food security measures on a global scale.

Climatic signals in the life histories of insects:the distribution and abundance of heather psyllids (Strophingia spp.) in the UK

1. The population density and age structure of two species of heather psyllid Strophingia ericae and Strophingia cinereae, feeding on Calluna vulgaris and Erica cinerea, respectively, were sampled using standardized methods at locations throughout Britain. Locations were chosen to represent the full latitudinal and altitudinal range of the host plants.

2. The paper explains how spatial variation in thermal environment, insect life-history characteristics and physiology, and plant distribution, interact to provide the mechanisms that determine the range and abundance of Strophingia spp.

3. Strophingia ericae and S. cinereae, despite the similarity in the spatial distribution patterns of their host plants within Britain, display strongly contrasting geographical ranges and corresponding life-history strategies. Strophingia ericae is found on its host plant throughout Britain but S. cinereae is restricted to low elevation sites south of the Mersey-Humber line and occupies only part of the latitudinal and altitudinal range of its host plant. There is no evidence to suggest that S. ericae has reached its potential altitudinal or latitudinal limit in the UK, even though its host plant appears to reach its altitudinal limit.

4. There was little difference in the ability of the two Strophingia spp. to survive shortterm exposure to temperatures as low as - 15 degrees C and low winter temperatures probably do not limit distribution in S. cinereae.

5. Population density of S. ericae was not related to altitude but showed a weak correlation with latitude. The spread of larval instars present at a site, measured as an index of instar homogeneity, was significantly correlated with a range of temperature related variables, of which May mean temperature and length of growing season above 3 degrees C (calculated using the Lennon and Turner climatic model) were the most significant. Factor analysis did not improve the level of correlation significantly above those obtained for single climatic variables. The data confirmed that S. ericae has a I year life cycle at the lowest elevations and a 2 year life cycle at the higher elevations. However, there was no evidence, as previously suggested, for an abrupt change from a one to a 2 year life cycle in S. ericae with increasing altitudes or latitudes.

6. By contrast with S. ericae, S. cinereae had an obligatory 1 year life cycle, its population decreased with altitude and the index of instar homogeneity showed little correlation with single temperature variables. Moreover, it occupied only part of the range of its host plant and its spatial distribution in the UK could be predicted with 96% accuracy using selected variables in discriminant analysis.

7. The life histories of the congeneric heather psyllids reflect adaptations that allow them to exploit host plants with different distributions in climatic and thereby geographical space. Strophingia ericae has the flexible life history that enables it to exploit C. vulgaris throughout its European boreal temperate range. Strophingia cinereae has a less flexible life history and is adapted for living on an oceanic temperate host. While the geographic ranges of the two Strophingia spp. overlap within the UK, the psyllids appear to respond differently to variation in their thermal environment.

Metabolomics reveals novel biomarkers of illegal 5-nitromimidazole treatment in pigs. Further evidence of drug toxicity uncovered

The aim of the study was to investigate the potential of a metabolomics platform to distinguish between pigs treated with ronidazole, dimetridazole and metronidazole and non-medicated animals (controls), at two withdrawal periods (day 0 and 5). Livers from each animal were biochemically profiled using UHPLC–QTof-MS in ESI+ mode of acquisition. Several Orthogonal Partial Least Squares-Discriminant Analysis models were generated from the acquired mass spectrometry data. The models classified the two groups control and treated animals. A total of 42 ions of interest explained the variation in ESI+. It was possible to find the identity of 3 of the ions and to positively classify 4 of the ionic features, which can be used as potential biomarkers of illicit 5-nitroimidazole abuse. Further evidence of the toxic mechanisms of 5-nitroimidazole drugs has been revealed, which may be of substantial importance as metronidazole is widely used in human medicine.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

Discrimination of honey of different floral origins by a combination of various chemical parameters

Abstract Honey is a high value food commodity with recognized nutraceutical properties. A primary driver of the value of honey is its floral origin. The feasibility of applying multivariate data analysis to various chemical parameters for the discrimination of honeys was explored. This approach was applied to four authentic honeys with different floral origins (rata, kamahi, clover and manuka) obtained from producers in New Zealand. Results from elemental profiling, stable isotope analysis, metabolomics (UPLC-QToF MS), and NIR, FT-IR, and Raman spectroscopic fingerprinting were analyzed. Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine which technique or combination of techniques provided the best classification and prediction abilities. Good prediction values were achieved using metabolite data (for all four honeys, Q² = 0.52; for manuka and clover, Q² = 0.76) and the trace element/isotopic data (for manuka and clover, Q² = 0.65), while the other chemical parameters showed promise when combined (for manuka and clover, Q² = 0.43).

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Nonparametric Estimation of Data Dimensionality Prior to Data Compression: the case of the Human Development Index

In many applications in applied statistics researchers reduce the complexity of a data set by combining a group of variables into a single measure using factor analysis or an index number. We argue that such compression loses information if the data actually has high dimensionality. We advocate the use of a non-parametric estimator, commonly used in physics (the Takens estimator), to estimate the correlation dimension of the data prior to compression. The advantage of this approach over traditional linear data compression approaches is that the data does not have to be linearized. Applying our ideas to the United Nations Human Development Index we find that the four variables that are used in its construction have dimension three and the index loses information.

Analysis of epistasis in linked regions in the Irish study of high-density schizophrenia families

Epistasis may be important in the etiology of schizophrenia. Analysis of epistasis has been important in the positional cloning of a gene involved in the etiology of type II diabetes mellitus. We investigated the importance of epistasis among six linked regions in 268 multiplex pedigrees in the Irish Study of High-Density Schizophrenia Families (ISHDSF) by computing pairwise correlations between nonparametric linkage scores for narrow, intermediate, and broad diagnostic definitions. The linked regions were on chromosomes 2, 4, 5, 6, 8, and 10. No correlation reached our a priori level of statistical significance. Using this statistical approach, we did not find evidence of important epistatic effects among these six regions in the ISHDSF.

Fingerprint Volatile Organic Compounds (VOC) analysis in Goat’s Milk and Halloumi Cheese as Marker for Authentication of Region of Origin

Goats’ milk is responsible for unique traditional products such as Halloumi cheese. The characteristics of Halloumi depend on the original features of the milk and on the conditions under which the milk has been produced such as feeding regime of the animals or region of production. Using a range of milk (33) and Halloumi (33) samples collected over a year from three different locations in Cyprus (A, Anogyra; K, Kofinou; P, Paphos), the potential for fingerprint VOC analysis as marker to authenticate Halloumi was investigated. This unique set up consists of an in-injector thermo desorption (VOCtrap needle) and a chromatofocusing system based on mass spectrometry (VOCscanner). The mass spectra of all the analyzed samples are treated by multivariate analysis (Principle component analysis and Discriminant functions analysis). Results showed that the highland area of product (P) is clearly identified in milks produced (discriminant score 67%). It is interesting to note that the higher similitude found on milks from regions “A” and “K” (with P being distractive; discriminant score 80%) are not ‘carried over’ on the cheeses (higher similitude between regions “A” and “P”, with “K” distinctive). Data have been broken down into three seasons. Similarly, the seasonality differences observed in different milks are not necessarily reported on the produced cheeses. This is expected due to the different VOC signatures developed in cheeses as part of the numerous biochemical changes during its elaboration compared to milk. VOC however it is an additional analytical tool that can aid in the identification of region origin in dairy products.

Reliable survival analysis based on the Dirichlet Process

We present a robust Dirichlet process for estimating survival functions from samples with right-censored data. It adopts a prior near-ignorance approach to avoid almost any assumption about the distribution of the population lifetimes, as well as the need of eliciting an infinite dimensional parameter (in case of lack of prior information), as it happens with the usual Dirichlet process prior. We show how such model can be used to derive robust inferences from right-censored lifetime data. Robustness is due to the identification of the decisions that are prior-dependent, and can be interpreted as an analysis of sensitivity with respect to the hypothetical inclusion of fictitious new samples in the data. In particular, we derive a nonparametric estimator of the survival probability and a hypothesis test about the probability that the lifetime of an individual from one population is shorter than the lifetime of an individual from another. We evaluate these ideas on simulated data and on the Australian AIDS survival dataset. The methods are publicly available through an easy-to-use R package.