CeO<Subscript>2</Subscript> and Co<Subscript>3</Subscript>O<Subscript>4</Subscript>–CeO<Subscript>2</Subscript> nanoparticles: effect of the synthesis method on the structure and catalytic properties in COPrOx and methanation reactions

CeO₂ and Co₃O₄–CeO₂ nanoparticles were synthesized, thoroughly characterized, and evaluated in the COPrOx reaction. The CeO₂ nanoparticles were synthesized by the diffusion-controlled precipitation method with ethylene glycol. A notably higher yield was obtained when H₂O₂ was used in the synthesis procedure. For comparison, two commercial samples of CeO₂ nanoparticles (Nyacol^®)—one calcined and the other sintered—were also studied. Catalytic results of bare CeO₂ calcined at 500 °C showed a strong influence of the method of synthesis. Despite having similar BET area values, the CeO₂ synthesized without H₂O₂ was the most active sample. Co₃O₄–CeO₂ catalysts with three different Co/(Co + Ce) atomic ratios, 0.1, 0.3, and 0.5, were prepared by the wet impregnation of the CeO₂ nanoparticles. TEM and STEM observations showed that impregnation produced mixed oxides composed of small CeO₂ nanoparticles located both over the surface and inside the Co₃O₄ crystals. The mixed oxide catalysts prepared with a cobalt atomic ratio of 0.5 showed methane formation, which started at 200 °C due to the reaction between CO₂ and H₂. However, above 250 °C, the reaction between CO and H₂ became important, thus contributing to CO elimination with a small H₂ loss. As a result, CO could be totally eliminated in a wide temperature range, from 200 to 400 °C. The methanation reaction was favored by the reduction of the cobalt oxide, as suggested by the TPR experiments. This result is probably originated in Ce–Co interactions, related to the method of synthesis and the surface area of the mixed oxides obtained.     

Cohesive surface model for fracture based on a two-scale formulation: computational implementation aspects

The paper describes the computational aspects and numerical implementation of a two-scale cohesive surface methodology developed for analyzing fracture in heterogeneous materials with complex micro-structures. This approach can be categorized as a semi-concurrent model using the representative volume element concept. A variational multi-scale formulation of the methodology has been previously presented by the authors. Subsequently, the formulation has been generalized and improved in two aspects: (i) cohesive surfaces have been introduced at both scales of analysis, they are modeled with a strong discontinuity kinematics (new equations describing the insertion of the macro-scale strains, into the micro-scale and the posterior homogenization procedure have been considered); (ii) the computational procedure and numerical implementation have been adapted for this formulation. The first point has been presented elsewhere, and it is summarized here. Instead, the main objective of this paper is to address a rather detailed presentation of the second point. Finite element techniques for modeling cohesive surfaces at both scales of analysis (FE\(^2\) approach) are described: (i) finite elements with embedded strong discontinuities are used for the macro-scale simulation, and (ii) continuum-type finite elements with high aspect ratios, mimicking cohesive surfaces, are adopted for simulating the failure mechanisms at the micro-scale. The methodology is validated through numerical simulation of a quasi-brittle concrete fracture problem. The proposed multi-scale model is capable of unveiling the mechanisms that lead from the material degradation phenomenon at the meso-structural level to the activation and propagation of cohesive surfaces at the structural scale.     

Determination of the Optimal Fourier Number on the Dynamic Thermal Transmission

This article represents the result of experimental research on transient heat transfer in a multilayered (heterogeneous) wall. Our non-steady thermal transmission simulation is based on a finite-difference calculation method. The value of a Fourier number shows the similarity of thermal variation in conditional layers of an enclosure. Most scientists recommend using no more than a value of 0.5 for the Fourier number when performing calculations on dynamic (transient) heat transfer. The value of the Fourier number is determined in order to acquire reliable calculation results with optimal accuracy. To compare the results of simulation with experimental research, a transient heat transfer calculation spreadsheet was created. Our research has shown that a Fourier number of around 0.5 or even 0.32 is not sufficient (\({\approx }17\,\%\) of oscillation amplitude) for calculations of transient heat transfer in a multilayered wall. The least distorted calculation results were obtained when the multilayered enclosure was divided into conditional layers with almost equal Fourier number values and when the value of the Fourier number was around 1/6, i.e., approximately 0.17. Statistical deviation analysis using the Statistical Analysis System was applied to assess the accuracy of the spreadsheet calculation and was developed on the basis of our established methodology. The mean and median absolute error as well as their confidence intervals has been estimated by the two methods with optimal accuracy (\({F}_{\mathrm{oMDF}}= 0.177\) and \(F_{\mathrm{oEPS}}= 0.1633\) values).     

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.