Steady-state O<Subscript>2</Subscript> and CO<Subscript>2</Subscript> diffusion in carbonated mortars produced with blended cements

The diffusion coefficient \(D_{{{\text{O}}_{2} }}\), the porosity and the pore structure of mortars produced with a Portland cement and a range of blended cements containing limestone powder, microsilica, portlandite or slag were measured in the non-carbonated and the carbonated state. Additionally, the setup for measuring O₂ diffusion was adapted to measure also the CO₂ diffusion of the carbonated mortars. The diffusion coefficient \(D_{{{\text{O}}_{2} }}\) and the total porosity were increased in the mortars containing microsilica and slag, while they were decreased in the other mortars due to carbonation. Invariably, the pore structure became coarser in all samples. The relationship between diffusion coefficients \(D_{{{\text{O}}_{2} }}\) and \(D_{{{\text{CO}}_{2} }}\) in the carbonated mortars was always linear, with \(D_{{{\text{O}}_{2} }}\) systematically higher by factor of 1.37. As this factor broadly agrees with what was found in the scant literature about CO₂ diffusion, it could be used for estimating \(D_{{{\text{CO}}_{2} }}\) of carbonated mortar and concrete based on measurements of O₂ diffusion.     

Healing Effect of Controlled Anti‐Electromigration on Conventional and High‐Tc Superconducting Nanowires

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti‐electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La_2?x Ce_x CuO₄), the electromigration process acts selectively on the oxygen atoms with no apparent modification of the structure. This allows to adjust the doping of this compound and switch from a superconducting to an insulating state in a nearly reversible fashion. In addition, the conditions needed to replace feedback controlled electromigration by a simpler technique of electropulsing are discussed. These findings have a direct practical application as a method to explore the dependence of the characteristic parameters on the exact oxygen content and pave the way for a reversible control of local properties of nanowires.     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part II: process and product characterization

The aqueous-core enclosed in lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water mimic, at least in theory, the environment within viable cells, thus being suitable for housing hydrophilic protein entities such as bioactive proteins, peptides and bacteriophage particles. This study reports a complete physicochemical characterization of optimized biomimetic aqueous-core lipid nanoballoons housing hydrophilic (BSA) protein entities, evolved from a statistical 2³×3¹ factorial design study (three variables at two levels and one variable at three levels) that was the subject of the first paper of a series of three, aiming at complete stabilization of the three-dimensional structure of protein entities attempted via housing the said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion. The statistical factorial design followed led to the production of an optimum W/O/W multiple emulsion possessing quite homogeneous particles with an average hydrodynamic size of (186.2?±?2.6) nm and average Zeta potential of (?36.5?±?0.9) mV, and exhibiting a polydispersity index of 0.206?±?0.014. Additionally, the results obtained for the diffusion coefficient of the lipid nanoballoons integrating the optimized W/O/W multiple emulsion were comparable and of the same order of magnitude (10^?12 m² s^?1) as those published by other authors since, typically, diffusion coefficients for molecules range from 10^?10 to 10^?7 m² s^?1, but diffusion coefficients for nanoparticles are typically of the order of magnitude of 10^?12 m² s^?1.     

Prediction of indications for valve replacement among asymptomatic or minimally symptomatic patients with chronic aortic regurgitation and normal left ventricular performance

BACKGROUND: Optimal criteria for valve replacement are unclear in asymptomatic/minimally symptomatic patients with aortic regurgitation (AR) and normal left ventricular (LV) performance at rest. Moreover, previous studies have not assessed the prognostic capacity of load-adjusted LV performance ("contractility") variables, which may be fundamentally related to clinical state. Therefore, 18 years ago, we set out to test prospectively the hypothesis that objective noninvasive measures of LV size and performance and, specifically, of load-adjusted variables, assessed at rest and during exercise (ex), could predict the development of currently accepted indications for operation for AR. METHODS AND RESULTS: Clinical variables and measures of LV size, performance, and end-systolic wall stress (ESS) were assessed annually in 104 patients by radionuclide cineangiography at rest and maximal ex and by echocardiography at rest; ESS was derived during ex. During an average 7.3-year follow-up among patients who had not been operated on, 39 of 104 patients either died suddenly (n = 4) or developed operable symptoms only (n = 22) or subnormal LV performance with or without symptoms (n = 13) (progression rate=6.2%/y). By multivariate Cox model analysis, change (delta) in LV ejection fraction (EF) from rest to ex, normalized for deltaESS from rest to ex (deltaLVEF-deltaESS index), was the strongest predictor of progression to any end point or to sudden cardiac death alone. Unadjusted deltaLVEF was almost as efficient. Symptom status modified prediction on the basis of the deltaLVEF-deltaESS index. The population tercile at highest risk by deltaLVEF-deltaESS progressed to end points at a rate of 13.3%/y, and the lowest-risk tercile progressed at 1.8%/y. CONCLUSIONS: Currently accepted symptom and LV performance indications for valve replacement, as well as sudden cardiac death, can be predicted in asymptomatic/minimally symptomatic patients with AR by load-adjusted deltaLVEF-deltaESS index, which includes data obtained during exercise.     

In situ ray tracing and computational steering for interactive blood flow simulation

Recent algorithm and hardware developments have significantly improved our capability to interactively visualise time-varying flow fields. However, when visualising very large dynamically varying datasets interactively there are still limitations in the scalability and efficiency of these methods. Here we present a rendering pipeline which employs an efficient in situ ray tracing technique to visualise flow fields as they are simulated. The ray casting approach is particularly well suited for the visualisation of large and sparse time-varying datasets, where it is capable of rendering fluid flow fields at high image resolutions and at interactive frame rates on a single multi-core processor using OpenMP. The parallel implementation of our in situ visualisation method relies on MPI, requires no specialised hardware support, and employs the same underlying spatial decomposition as the fluid simulator. The visualisation pipeline allows the user to operate on a commodity computer and explore the simulation output interactively. Our simulation environment incorporates numerous features that can be utilised in a wide variety of research contexts.     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.