A Dual Physical Cross-Linking Strategy to Construct Tough Hydrogels with High Strength,Excellent Fatigue Resistance,and Stretching-Induced Strengthening Effect

Hydrogels with excellent stiffness, toughness, anti-fatigue, and self-recovery properties are regarded as promising water-containing materials. In this work, a dual physically cross-linked (DPC) sodium alginate (SA)/poly[acrylamide (AAm)-acrylic acid (AAc)-octadecyl methacrylate (OMA)]-Fe³⁺ hydrogel is reported, which is constructed by hydrophobic association (HA) and ionic coordination (IC). The optimal DPC hydrogel demonstrates excellent mechanical performance: tensile modulus of 0.65 MPa, tensile strength of 3.31 MPa, elongation at break of 1547%, and toughness of 27.8 MJ m^–3. SA/P(AAm-AAc-OMA)-Fe³⁺ DPC hydrogels also exhibit prominent anti-fatigue and self-recovery performance (99.1–109.7% modulus recovery and 90.4–108.9% dissipated energy recovery after resting for 5 min without additional stimuli at ambient temperature) through the reconstruction of reversible physical cross-linking. Some of the SA/P(AAm-AAc-OMA)-Fe³⁺ DPC hydrogels even exhibit a stretching-induced strengthening effect, which is similar to the performance of muscle—“the more training, the more strength.” Hence, the combination of HA and IC will provide an effective approach to design DPC hydrogels with desirable mechanical performances and a longer service life for wider applications of soft materials.     

Kinetics of the Initial Stage of Sintering from Shrinkage Data: Simultaneous Determination of Activation Energy and Kinetic Model from a Single Nonisothermal Experiment

A new method for the kinetic analysis of the initial stage of sintering for constant-heating-rate data has been proposed. Unlike all of the methods previously reported in the literature, this new method proposed here allows the simultaneous determination of the activation energy and the kinetic model from a single dilatometric curve recorded under a linear-heating-rate program. The proposed method has been tested with simulated sintering curves and experimental results have been obtained for the sintering of a rutile sample.     

Surface Pretreatments of Silicon Nitride for CVD Diamond Deposition

The efficiency of different surface pretreatments (four standard chemical etchings and four diamond powder abrasive procedures) on silicon nitride (Si₃N₄) substrates for chemical vapor deposition (CVD) of diamond has been systematically investigated. Blank Si₃N₄ samples were polished with colloidal silica (∼0.25 μm). Diamond nucleation and growth runs were conducted in a microwave plasma chemical vapor deposition apparatus for 10 min and 6 h, respectively. Superior results concerning nucleation density ( N _d∼ 10¹⁰ cm⁻² after 10 min), film uniformity, and grain size (below 2 μm after 6 h) were obtained for the mechanically microflawed samples, revealing that chemical etchings (hot and cold strong acids, molten base or CF₄ plasma) are not crucial for good CVD diamond quality on Si₃N₄.     

Fast finite difference Poisson solvers on heterogeneous architectures

In this paper we propose and evaluate a set of new strategies for the solution of three dimensional separable elliptic problems on CPU–GPU platforms. The numerical solution of the system of linear equations arising when discretizing those operators often represents the most time consuming part of larger simulation codes tackling a variety of physical situations. Incompressible fluid flows, electromagnetic problems, heat transfer and solid mechanic simulations are just a few examples of application areas that require efficient solution strategies for this class of problems. GPU computing has emerged as an attractive alternative to conventional CPUs for many scientific applications. High speedups over CPU implementations have been reported and this trend is expected to continue in the future with improved programming support and tighter CPU–GPU integration. These speedups by no means imply that CPU performance is no longer critical. The conventional CPU-control–GPU-compute pattern used in many applications wastes much of CPU’s computational power. Our proposed parallel implementation of a classical cyclic reduction algorithm to tackle the large linear systems arising from the discretized form of the elliptic problem at hand, schedules computing on both the GPU and the CPUs in a cooperative way. The experimental result demonstrates the effectiveness of this approach.     

Effects of physical refining on contents of waxes and fatty alcohols of refined olive oil

Changes in the contents of waxes and fatty alcohols during deodorization/physical refining of bleached olive oil were studied. Experiments were carried out with 1.85% acidity oil, which was physically refined in a discontinuous deodorizer of 250-kg maximum capacity using nitrogen as stripping gas instead of steam. The variables studied were load and temperature of oil in the deodorizer as well as N₂ flow. Analyses of waxes and alcohols were carried out at different operation times. The maximum content of wax was always observed when the oil reached the deodorization temperature. The variation in the wax content depended on temperature and N₂ flow. Wax decomposition started and continued during the operating time, and a progressive decrease, which was pronounced between 3 and 4 h, was observed. Small changes in waxes were observed between 4 and 5 h. Total content of fatty alcohols diminished throughout the operating time, and changes did not depend on the variables studied.     

Single frame correction of motion artifacts in PMD-based time of flight cameras

One of the leading time of flight imaging technologies for depth sensing is based on Photonic Mixer Devices (PMD). In PMD sensors each pixel samples the correlation between emitted and received light signals. Current PMD cameras compute eight correlation samples per pixel in four sequential stages to obtain depth with invariance to signal amplitude and offset variations. With motion, PMD pixels capture different depths at each stage. As a result, correlation samples are not coherent with a single depth, producing artifacts. We propose to detect and remove motion artifacts from a single frame taken by a PMD camera. The algorithm we propose is very fast, simple and can be easily included in camera hardware. We recover depth of each pixel by exploiting consistency of the correlation samples and local neighbors of the pixel. In addition, our method obtains the motion flow of occluding contours in the image from a single frame. The system has been validated in real scenes using a commercial low-cost PMD camera and high speed dynamics. In all cases our method produces accurate results and it highly reduces motion artifacts.     

A Class of Incomplete Riemann Solvers Based on Uniform Rational Approximations to the Absolute Value Function

In this paper we propose a new class of incomplete Riemann solvers, based on approximations in the \(L^\infty \) -norm to the absolute value function in \([-1,1]\) by means of rational functions, for the numerical approximation of the solution of hyperbolic systems of conservation laws. The main idea relies on the construction of a numerical approximation to the viscosity matrix by using an appropriate rational real function \(R(x)\) , that approximates the function \(|x|\) uniformly in \([-1,1]\) , evaluated at the Jacobian of the fluxes of the hyperbolic system computed at some average value (for example, Roe averages). In addition to the Jacobians of the fluxes we shall use either the maximum in absolute value of the characteristic speeds in each cell or an upper bound of them. Thus, the resulting approximate Riemann solver is incomplete in the sense that we do not use the complete spectral decomposition of the Jacobian. Moreover, the new class of Riemann solvers consists of a hierarchy of schemes running from the more dissipative to the less dissipative ones, and having as limiting case a Roe-like scheme. According to the order of the approximation of the generating rational function used, the degree of dissipation can be dosed for particular applications. We study different rational approximations: Newman-type functions, iterative generated Halley functions, and also Chebyshev polynomial approximants. We test our basic algorithms for different initial value Riemann problems for ideal gas dynamics (HD) and magnetohydrodynamics (MHD) to observe their behavior with respect to challenging scenarios in numerical simulations, including some standard numerical pathologies (e. g., heat conduction, postshock oscillations and overheating) and the formation of compound waves in ideal MHD. We also examine our proposed schemes, by computing the numerical approximation of different initial value problems for nonconservative multilayer shallow water equations, where it has been observed that intermediate waves can be properly captured for an appropriate degree of approximation of the generating rational function used. Our numerical tests indicate that the proposed schemes are robust, running stable and accurate with a satisfactory time step restriction (CFL constant), and the computational cost is more advantageous with respect to schemes that use a complete spectral decomposition of the Jacobians.     

High-Strength and High-Fracture-Toughness Ceramics in the Al2O3/LaAl11O18 Systems

Control of microstructure in the Al₂O₃/LaAl₁₁O₁₈ system was performed. Elongated alumina grains were formed by doping with small addition of silica, and 20 vol% lanthana- luminate was formed in situ by the reaction of LaAlO₃- A1₂O₃ in an alumina matrix. Strengths of over 600 MPa and a high fracture toughness (6 MPa.m^1/2) were achieved in the material with both elongated A1₂O₃ grains and LaAl₁₁O₁₈ platelets. Generally antagonistic properties such as strength and fracture toughness have been made compatible in the same ceramic system.     

Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.     

Abnormal behavior detection using dominant sets

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\) -Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.