JPEG Image Steganalysis Using Multivariate PDF Estimates With MRF Cliques

Blind steganalysis of JPEG images is addressed by modeling the correlations among the DCT coefficients using K -variate (K = 2) p.d.f. estimates (p.d.f.s) constructed by means of Markov random field (MRF) cliques. The reasoning of using high variate p.d.f.s together with MRF cliques for image steganalysis is explained via a classical detection problem. Although our approach has many improvements over the current state-of-the-art, it suffers from the high dimensionality and the sparseness of the high variate p.d.f.s. The dimensionality problem as well as the sparseness problem are solved heuristically by means of dimensionality reduction and feature selection algorithms. The detection accuracy of the proposed method(s) is evaluated over Memon's (30.000 images) and Goljan's (1912 images) image sets. It is shown that practically applicable steganalysis systems are possible with a suitable dimensionality reduction technique and these systems can provide, in general, improved detection accuracy over the current state-of-the-art. Experimental results also justify this assertion.

Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, Gene Set Analysis (GSA) approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations, or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results: In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA, indeed, captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.

Trace Element Abundance and Renal Disease

This research investigates the relationship between elevated trace elements in soils, stream sediments and stream water and the prevalence of Chronic Kidney Disease (CKD). The study uses a collaboration of datasets provided from the UK Renal Registry Report (UKRR) on patients with renal diseases requiring treatment including Renal Replacement Therapy (RRT), the soil geochemical dataset for Northern Ireland provided by the Tellus Survey, Geological Survey of Northern Ireland (GSNI) and the bioaccessibility of Potentially Toxic Elements (PTEs) from soil samples which were obtained from the Unified Barge Method (UBM). The relationship between these factors derives from the UKRR report which highlights incidence rates of renal impaired patients showing regional variation with cases of unknown aetiology. Studies suggest a potential cause of the large variation and uncertain aetiology is associated with underlying environmental factors such as the oral bioaccessibility of trace elements in the gastrointestinal tract.
As previous research indicates that long term exposure is related to environmental factors, Northern Ireland is ideally placed for this research as people traditionally live in the same location for long periods of time. Exploratory data analysis and multivariate analyses are used to examine the soil, stream sediments and stream water geochemistry data for a range of key elements including arsenic, lead, cadmium and mercury identified from a review of previous renal disease literature. The spatial prevalence of patients with long term CKD is analysed on an area basis. Further work includes cluster analysis to detect areas of low or high incidences of CKD that are significantly correlated in space, Geographical Weighted Regression (GWR) and Poisson kriging to examine locally varying relationship between elevated concentrations of PTEs and the prevalence of CKD.

A multivariate analysis of beta diversity across organisms and environments

We examined variability in hierarchical beta diversity across ecosystems, geographical gradients, and organism groups using multivariate spatial mixed modeling analysis of two independent data sets. The larger data set comprised reported ratios of regional species richness (RSR) to local species richness (LSR) and the second data set consisted of RSR: LSR ratios derived from nested species-area relationships. There was a negative, albeit relatively weak, relationship between beta diversity and latitude. We found only relatively subtle differences in beta diversity among the realms, yet beta diversity was lower in marine systems than in terrestrial or freshwater realms. Beta diversity varied significantly among organisms' major characteristics such as body mass, trophic position, and dispersal type in the larger data set. Organisms that disperse via seeds had highest beta diversity, and passively dispersed organisms showed the lowest beta diversity. Furthermore, autotrophs had lower beta diversity than organisms higher up the food web; omnivores and carnivores had consistently higher beta diversity. This is evidence that beta diversity is simultaneously controlled by extrinsic factors related to geography and environment, and by intrinsic factors related to organism characteristics.

On the Multivariate Gamma-Gamma (ΓΓ) Distribution with Arbitrary Correlation and Applications in Wireless Communications

The statistical properties of the multivariate GammaGamma (ΓΓ) distribution with arbitrary correlation have remained unknown. In this paper, we provide analytical expressions for the joint probability density function (PDF), cumulative distribution function (CDF) and moment generation function of the multivariate ΓΓ distribution with arbitrary correlation. Furthermore, we present novel approximating expressions for the PDF and CDF of the su m of ΓΓ random variables with arbitrary correlation. Based on this statistical analysis, we investigate the performance of radio frequency and optical wireless communication systems. It is noteworthy that the presented expressions include several previous results in the literature as special cases.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

The determination of optimal processing conditions for PLA and PLA/CaCO3 in twin screw extrusion using DoE and multivariate analysis

Biodegradable polymers, such as PLA (Polylactide), come from renewable resources like corn starch and if disposed of correctly, degrade and become harmless to the ecosystem making them attractive alternatives to petroleum based polymers. PLA in particular is used in a variety of applications including medical devices, food packaging and waste disposal packaging. However, the industry faces challenges in melt processing of PLA due to its poor thermal stability which is influenced by processing temperatures and shearing.
Identification and control of suitable processing conditions is extremely challenging, usually relying on trial and error, and often sensitive to batch to batch variations. Off-line assessment in a lab environment can result in high scrap rates, long lead times and lengthy and expensive process development. Scrap rates are typically in the region of 25-30% for medical grade PLA costing between €2000-€5000/kg.
Additives are used to enhance material properties such as mechanical properties and may also have a therapeutic role in the case of bioresorbable medical devices, for example the release of calcium from orthopaedic implants such as fixation screws promotes healing. Additives can also reduce the costs involved as less of the polymer resin is required.
This study investigates the scope for monitoring, modelling and optimising processing conditions for twin screw extrusion of PLA and PLA w/calcium carbonate to achieve desired material properties. A DAQ system has been constructed to gather data from a bespoke measurement die comprising melt temperature; pressure drop along the length of the die; and UV-Vis spectral data which is shown to correlate to filler dispersion. Trials were carried out under a range of processing conditions using a Design of Experiments approach and samples were tested for mechanical properties, degradation rate and the release rate of calcium. Relationships between recorded process data and material characterisation results are explored.