Vortices in anisotropic type-II superconductors

We present a generalized variational model of an isolated vortex, valid in the anisotropic case when the external magnetic field is along one of the symmetry directions. We discuss the effects of the field anisotropy on the core and magnetic pinning of individual vortices.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Boosted Bayesian network classifiers

The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present boosted Bayesian network classifiers, a framework to combine discriminative data-weighting with generative training of intermediate models. We show that boosted Bayesian network classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal. We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers. On a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes and TAN in classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other discriminatively trained graphical models including ELR and BNC. Furthermore, boosted Bayesian networks require significantly less training time than the ELR and BNC algorithms.     

Selective Al-Ti reactivity in laser-processed Al/Ti multilayers

Multilayers consisting of five (Al/Ti) bilayers were deposited on (100) silicon wafers. On top was deposited the Ti layer, aimed at preventing Al from diffusing to the surface upon laser treatment. The total thickness of the thin-film structure was 200?nm. Laser irradiations with Nd:YAG picoseconds laser pulses in the defocused regime were performed in air. Laser beam energy was 4?mJ and laser spot diameter on the sample surface was 3?mm (fluence 0.057?J?cm^?2). The samples were treated with different numbers of laser pulses. Structural characterizations were performed by different analytical methods and nano-hardness was also measured. Laser processing induced layer intermixing, formation of titanium aluminides, oxidation of the surface titanium layer and enhanced surface roughness. Aluminum appears at the sample surface only for the highest density of laser irradiation. Laser processing induces increment of nano-hardness by approximately 20% and decrease of residual Young’s modulus for a few percentages from the starting value of the untreated samples. These results can be interesting toward achieving structures with a selective extent of Al-Ti reactivity in this multilayered system, within the development of biocompatible materials.     

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.     

DNA-directed immobilization of horseradish peroxidase onto porous SiO2optical transducers

ABSTRACT: Multifunctional porous Si nanostructure is designed to optically monitor enzymatic activity of Horseradish Peroxidase. First, an oxidized PSi optical nanostructure, a Fabry-Perot thin film, is synthesized and is used as the optical transducer element. Immobilization of the enzyme onto the nanostructure is performed through DNA-Directed Immobilization. Preliminary studies demonstrate high enzymatic activity levels of the immobilized Horseradish Peroxidase, while maintaining its specificity. The catalytic activity of the enzymes immobilized within the porous nanostructure is monitored in real time by reflective interferometric Fourier transform spectroscopy. We show that we can easily regenerate the surface for consecutive biosensing analysis by mild de-hybridization conditions.     

Intelligent forest fire monitoring system

This paper presents iForestFire, an Environmental Monitoring Information System for forest fire protection. The system is composed of several components, each having a particular function. Automatic fire detection is a crucial component of the system. It is based on various complex image processing algorithms. Complexity of the system also emerges from integration, based on multi agent technology, of different environment information. The presented system contributes to the environment protection and is in use in Croatia for several years.