Clinical neuropsychological aspects of motor disorders in patients with post-CVA epilepsy

The study included 22 patients with poststroke epilepsy (group 1) and 30 patients with stroke without epilepsy (group 2). A bilateral decrease of the time of central conduction (TCC) through pyramidal path (both on the paretic and intact side) was revealed in group 1. This was not observed in patients of group 2 with similar gravity of motor disorders (p < 0.01). Tendency to TCC decrease was also observed in patients without epileptic disorders by day 5-6 after ischemic stroke. However, TCC values were increased in such patients by day 10-14. Low indices persisted for a long time in patients with poststroke. There was also an increase of delta index of facilitation (the difference between TCC in rest and in muscle effort). It was also found that motor disorders in the group of patients with poststroke epilepsy were characterised by higher muscular tone than in poststroke patients with the same degree of the paresis.     

The comparative activity of natural and synthetic enterosorbents in experimental hyperlipidemia]

In-vitro experiments have shown that dietary fibre microcrystalline cellulose and lignin (polyphepan) possess high activity in binding sodium salt of cholic acid. This action is comparable with that of pectin and metamucile. Hypolipidemic effect of microcrystalline cellulose, polyphepan and synthetic enterosorbents carbonates was also seen in hyperlipidemic rats. It is concluded that microcrystalline cellulose and polyphepan hold promise as a dietary supplement for hyperlipidemic patients.     

Computing of high breakdown regression estimators without sorting on graphics processing units

We present an approach to computing high-breakdown regression estimators in parallel on graphics processing units (GPU). We show that sorting the residuals is not necessary, and it can be substituted by calculating the median. We present and compare various methods to calculate the median and order statistics on GPUs. We introduce an alternative method based on the optimization of a convex function, and show its numerical superiority when calculating the order statistics of very large arrays on GPUs.     

Weakly Monotonic Averaging Functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions. It is also a limiting property that results in many important nonmonotonic averaging functions being excluded from the theoretical framework. This work proposes a definition for weakly monotonic averaging functions, studies some properties of this class of functions, and proves that several families of important nonmonotonic means are actually weakly monotonic averaging functions. Specifically, we provide sufficient conditions for weak monotonicity of the Lehmer mean and generalized mixture operators. We establish weak monotonicity of several robust estimators of location and conditions for weak monotonicity of a large class of penalty‐based aggregation functions. These results permit a proof of the weak monotonicity of the class of spatial‐tonal filters that include important members such as the bilateral filter and anisotropic diffusion. Our concept of weak monotonicity provides a sound theoretical and practical basis by which (monotonic) aggregation functions and nonmonotonic averaging functions can be related within the same framework, allowing us to bridge the gap between these previously disparate areas of research.     

Comparing Apples and Oranges: The Weighted OWA Function

This paper advocates the use of weighted ordered weighted averaging (WOWA) functions in decision‐making processes, where the alternatives are not directly comparable. In particular, WOWA allows one to compare the strongest points of each alternative, also weighted by the importance of each criterion. Four different approaches to applying weights in OWA functions are reviewed. Torra's method based on interpolating regular increasing monotone quantifier and the pruned n‐ary tree are compared to the WOWA obtained from recently proposed implicit averaging. Computationally efficient algorithms are outlined. The use of WOWA is illustrated in several examples.     

Marginal contribution representation of capacity-based multicriteria decision making

Integrals defined with respect to fuzzy measures (capacities) are powerful tools in multicriteria decision making. Monotonicity is a basic property of capacity, which means that the marginal contribution of any single criterion to any subset of criteria is always nonnegative. In this paper, we present the capacity-based decision making theory in terms of marginal contributions, which provides an alternative perspective to this widely used decision making strategy. We construct the marginal contribution representations of the equivalent transformations of capacities, some particular capacities, three types of nonlinear integrals, and discuss the capacity identification methods. We also introduce some new concepts and representations, such as nonadditivity and nonmodularity indices, 0 to 1 variables-based linear constraints of k-maxitive capacity, a special representation of the Choquet integral and pan integral. We discuss constraints on marginal contributions which ensure supermodularity of capacities. Finally, an illustrative example is given to show the use of marginal contribution presentation in capacity-based decision making methods.