introduction: the accumulation of heavy metals (hms) in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. hms may be considered as the most important soil pollutants, because they are not biodegradable and their physical movement through the soil profile is relatively limited. therefore, root uptake process may p...

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques(1,4,5,6,7). Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their re...

One of the characteristics of environmental data, many of them and the complex relationships between them. To reduce the number variables, different statistical methods exist. Multivariate statistics is used extensively in environmental science. It helps ecologists discover structure and previous relatively objective summary of the primary features of the data for easier comprehension. However,...

Two multivariate calibration methods are compared for the simultaneous chromatographic determination and separation of Sulfamethoxazole (SMX) and Phthalazine (PHZ) by High Performance Liquid Chromatography (HPLC). Multivariate calibration techniques such as Classical Least Squares (CLS) and Inverse Least Squares (ILS) were introduced into HPLC to determine the quantification by using UV d...

In 2002, Suresh Chari, Rao Josyula and Pankaj Rohatgi presented a very powerful method, known as the Template Attack, to infer secret values processed by a microcontroller, by analysing its power-supply current, generally known as its side-channel leakage. This attack uses a profiling step to compute the parameters of a multivariate normal distribution from the leakage of a training device, and...

In fMRI data analysis, univariate techniques have been used to detect activation regions. In this study, we propose and compare an alternative approach, multivariate techniques, to extract meaningful activation patterns from fMRI data. When multivariate techniques such as PCA, rPCA, sICA, tICA, and FA were applied to the simulated fMRI-like data, only rPCA and FA extracted meaningful patterns i...

The performance of techniques for evaluating univariate volatility forecasts are well understood. In the multivariate setting however, the efficacy of the evaluation techniques is not developed. Multivariate forecasts are often evaluated within an economic application such as portfolio optimisation context. This paper aims to evaluate the efficacy of such techniques, along with traditional stat...