Defining the e-learner’s security profile: Towards awareness improvement

The paper presents an improved e-learner model that supports monitoring of user behavior related to information security. The model is built upon standardized IMS specification, according to literature research and survey conducted among e-learners. It is positioned as key part of an extended LTSA architecture in which the learner data is used to improve learner security position by continuous delivery of important information and adapting security mechanisms. The implementation is considered in Moodle LMS.     

The influence of bile salts on the distribution of simvastatin in the octanol/buffer system

Abstract

Introduction: Distribution coefficient (D) is useful parameter for evaluating drugs permeability properties across biological membranes, which are of importance for drugs bioavailability. Given that bile acids are intensively studied as drug permeation-modifying and -solubilizing agents, the aim of this study was to estimate the influence of sodium salts of cholic (CA), deoxycholic (DCA) and 12-monoketocholic acids (MKC) on distribution coefficient of simvastatin (SV) (lactone [SVL] and acid form [SVA]) which is a highly lipophilic compound with extremely low water solubility and bioavailability.

Methods: LogD values of SVA and SVL with or without bile salts were measured by liquid–liquid extraction in n-octanol/buffer systems at pH 5 and 7.4. SV concentrations in aqueous phase were determined by HPLC-DAD. Chem3D Ultra program was applied for computation of physico-chemical properties of analyzed compounds and their complexes.

Results: Statistically significant decrease in both SVA and SVL logD was observed for all three studied bile salts at both selected pH. MKC exerted the most pronounced effect in the case of SVA while there were no statistically significant differences between observed bile salts for SVL. The calculated physico-chemical properties of analyzed compounds and their complexes supported experimental results.

Conclusions: Our data indicate that the addition of bile salts into the n-octanol/buffer system decreases the values of SV distribution coefficient at both studied pH values. This may be the result of the formation of hydrophilic complexes increasing the solubility of SV that could consequently impact the pharmacokinetic parameters of SV and the final drug response in patients.     

A subexponential bound for linear programming

We present a simple randomized algorithm which solves linear programs withn constraints andd variables in expected $$\min \{ O(d^2 2^d n),e^{2\sqrt {dIn({n \mathord{\left/ {\vphantom {n {\sqrt d }}} \right. \kern-\nulldelimiterspace} {\sqrt d }})} + O(\sqrt d + Inn)} \}$$ time in the unit cost model (where we count the number of arithmetic operations on the numbers in the input); to be precise, the algorithm computes the lexicographically smallest nonnegative point satisfyingn given linear inequalities ind variables. The expectation is over the internal randomizations performed by the algorithm, and holds for any input. In conjunction with Clarkson's linear programming algorithm, this gives an expected bound of $$O(d^2 n + e^{O(\sqrt {dInd} )} ).$$ The algorithm is presented in an abstract framework, which facilitates its application to several other related problems like computing the smallest enclosing ball (smallest volume enclosing ellipsoid) ofn points ind-space, computing the distance of twon-vertex (orn-facet) polytopes ind-space, and others. The subexponential running time can also be established for some of these problems (this relies on some recent results due to Gärtner).     

Long memory time series forecasting by using genetic programming

Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterized by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods’ advantages in long memory time series forecasting.     

Multi-scale computational model for failure analysis of metal frames that includes softening and local buckling

In this work we present a new modelling paradigm for computing the complete failure of metal frames by combining the stress-resultant beam model and the shell model. The shell model is used to compute the material parameters that are needed by an inelastic stress-resultant beam model; therefore, we consider the shell model as the meso-scale model and the beam model as the macro-scale model. The shell model takes into account elastoplasticity with strain-hardening and strain-softening, as well as geometrical nonlinearity (including local buckling of a part of a beam). By using results of the shell model, the stress-resultant inelastic beam model is derived that takes into account elastoplasticity with hardening, as well as softening effects (of material and geometric type). The beam softening effects are numerically modelled in a localized failure point by using beam finite element with embedded discontinuity. The original feature of the proposed multi-scale (i.e. shell-beam) computational model is its ability to incorporate both material and geometrical instability contributions into the stress-resultant beam model softening response. Several representative numerical simulations are presented to illustrate a very satisfying performance of the proposed approach.     

Left-forbidding cooperating distributed grammar systems

A left-forbidding grammar, introduced in this paper, is a context-free grammar, where a set of nonterminal symbols is attached to each context-free production. Such a production can rewrite a nonterminal provided that no symbol from the attached set occurs to the left of the rewritten nonterminal in the current sentential form. The present paper discusses cooperating distributed grammar systems with left-forbidding grammars as components and gives some new characterizations of language families of the Chomsky hierarchy. In addition, it also proves that twelve nonterminals are enough for cooperating distributed grammar systems working in the terminal derivation mode with two left-forbidding components (including erasing productions) to characterize the family of recursively enumerable languages.     

Multi-view Superpixel Stereo in Urban Environments

Urban environments possess many regularities which can be efficiently exploited for 3D dense reconstruction from multiple widely separated views. We present an approach utilizing properties of piecewise planarity and restricted number of plane orientations to suppress reconstruction and matching ambiguities causing failures of standard dense stereo methods. We formulate the problem of the 3D reconstruction in MRF framework built on an image pre-segmented into superpixels. Using this representation, we propose novel photometric and superpixel boundary consistency terms explicitly derived from superpixels and show that they overcome many difficulties of standard pixel-based formulations and handle favorably problematic scenarios containing many repetitive structures and no or low textured regions. We demonstrate our approach on several wide-baseline scenes demonstrating superior performance compared to previously proposed methods.     

The <Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript>-Optimality of the IIPG Method for Odd Degrees of Polynomial Approximation in 1D

The paper deals with a numerical analysis of the incomplete interior penalty Galerkin (IIPG) method applied to one dimensional Poisson problem. Based on a particular choice of the interior penalty parameter σ (order of O(h ⁻¹)), we derive the optimal error estimate in the L ²-norm for odd degrees of polynomial approximation for locally quasi-uniform meshes. Moreover, we show that only the mentioned choice of the penalty parameter leads to optimal orders of convergence. Finally, presented numerical experiments verify the theoretical results.