in this study, an optimization model is proposed to design a global supply chain (gsc) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of economic cooperation organization trade agreement (ecota) is...

bipolar disorder is a mental disease that can be presented as irritable mood with affective storms, mixed symptoms of depression and mania, rapid cycles, emotional labiality and irritability during all episodes. â confirmed positive familial history of the disease is the single most robust risk factor for developing the illness. this report presents 5.5 years-old girl with the symptoms of bipol...

the first purpose of this study is to present an alternative robust model in order to describe ruminal degradation kinetics of forages and to minimize the fitting problems. for this purpose, the korkmaz-uckardes (ku) model, which has a logarithmic structure, was developed. the second purpose of this study is to estimate, by using the korkmaz-uckardes (ku)model, the parameters tp (the time to pr...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

Two robust approaches to principal component analysis and factor analysis are presented. The different methods are compared, and properties are discussed. As an application we use a large geochemical data set which was analyzed in detail by univariate (geo-)statistical methods. We explain the advantages of applying robust multivariate methods.

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of original training data. Consequentially, so-called source classifier, trained on available labelled data, deteriorates test, target, Domain adaptive classifiers aim combat this problem, but typically assume some particular form domain shift. Most are not robust violations shift assumptions...