conclusions independent variables such as sex, age, hdl-c, sys-bp, bmi, smoking, cholesterol and hs-crp were considered as influencing variables, and education, marital status, disease history, height, waist circumference, diastolic blood pressure and low density lipoprotein were removed from the models. background cardiovascular disease is considered as one of the most common diseases, causing...

background and objectives: evaluating the performance of clinical units is critical for effective managementof health settings. certain assessment of clinical variables for performance analysis is not always possible,calling for use of uncertainty theory. this study aimed to develop and evaluate an integrated independentcomponent analysis-fuzzy-data envelopment analysis approach to accurate the...

the aim of this investigation was to prepare, characterize and optimize the aceclofenac proniosomes using central composite design and carry out stability studies. three independent variables selected were molar ratio of drug to lipid (x1), surfactant loading (x2) and volume of hydration (x3). based on central composite design, 16 batches of proniosomes were prepared by slurry method and evalua...

In this paper, a general elastoplastic-damage constitutive model considering the effect of strain rate has been developed. The derivation of this model has been cast into the irreversible thermodynamics with internal variables within the fundamentals of Continuum Damage Mechanics (CDM). The rate effect has been involved as an additional term into the plastic yield surface (dynamic plastic yield...

In this work we show how temporal structures in time series can be used in the framework of independent component analysis assuming the signals arise from Markov chains with finite order. Taking into account the past of the underlying processes, by using time embedding vectors, not only instantaneous independent but also uncoupled sources can be found. As a result signals which are gaussian dis...

Independent component analysis (ICA) is an unsupervised technique for blind source separation, and the ICA algorithms using nongaussianity as the measure of mutual independence have been also used for projection pursuit or visualization of multivariate data for knowledge discovery in databases (KDD). However, in real applications, it is often the case that we fail to extract useful latent varia...