Probabilistic evaluation of conformity criteria for concrete families

When only a limited number of test results are available, compressive strength results of concrete family members can be combined in order to enable conformity control on a sufficient number of test results. The principles behind the conformity control of concrete families are explained and an original probabilistic evaluation of conformity criteria for concrete families is introduced. For the calculation of the corresponding operating characteristics, some approximate formulae are presented. Additionally, Monte Carlo simulation techniques are used for more exact calculations. In this way, also autocorrelation between consecutive test results can be taken into account, using an autoregressive process. The current guidelines and conformity criteria for concrete families in EN 206-1 are evaluated and discussed, based on the AOQL concept. Different conformity criteria and transformation methods are described, evaluated and discussed.     

Failure mechanisms in granular media: a discrete element analysis

This paper attempts to numerically validate the concept of diffuse failure using a discrete element method. First, the theoretical background is reviewed, and it is shown how the kinetic energy of a system, initially at rest after a loading history, is likely to abruptly increase under the effect of disturbances. The vanishing of the second-order work thus constitutes a basic ingredient, related to both the pioneering work of Hill (J Mech Phys Solids (6):236–249, 1958) and the notion of bifurcation applied to geomechanics (Vardoulakis and Sulem in Bifurcation analysis in geomechanics, Chapman & Hall Publisher, London, 1995). Discrete numerical simulations were performed on homogeneous three-dimensional specimens, and the three basic conditions that must be satisfied in order to observe a failure mechanism are numerically checked. Finally, this work illustrates the phenomena that are likely to affect in situ slopes, for instance, when the loading (due to weather conditions or human activities) meets the three basic conditions for a failure mechanism to develop.     

Native and Biotechnologically Engineered Plant Proteases with Industrial Applications

Proteases occupy the most relevant position among industrial enzymes. Plant proteases have been used in medicine, detergent manufacturing, and food science for many years, but their production is diminishing in favor of those of microbial origin because lower production costs. Papain, bromelain, and ficin are the most frequently employed plant proteases, although new proteases with new and more appealing physicochemical properties for industry are still emerging. DNA technology and genetic engineering shall play, without a doubt, an important role for the production of these proteases at the industrial level. The present review focuses on the applications of traditional plant proteases as well as new proteases discovered during the last 20 years, some of which have already been genetically engineered either to increase production or to strengthen some of their physicochemical properties. The review also refers to the protease classification, action pattern, and main characteristics.     

Performance characterization of a small-capacity thermoacoustic cooler for air-conditioning applications

The performance of a working prototype, operated with and without water flow through the heat exchangers, was measured and found to be in reasonably close agreement to predictions from a simulation code, DELTAE, based on linear thermoacoustic theory. Further analysis and DELTAE simulations showed that the coefficient of performance may be significantly reduced when the stack temperature profile becomes non-linear, i.e. when the system is operated for a temperature span smaller than the optimal value for a given stack length. Guidelines to avoid this condition are provided.     

Effect of Thermomechanical Fatigue on Precipitation Microstructure in Two Precipitation-Hardened Cast Aluminum Alloys

Thermal loading induces modifications of the precipitation microstructure of Al–Si–Cu–Mg alloys. This study focuses on the effect of deformation on precipitation microstructure during thermomechanical loadings. Several specimens were thermomechanically cycled while others were exposed to the same thermal cycles without any mechanical loading. The nature and morphological characteristics of the precipitation microstructure of the thermomechanically cycled specimens are compared to those of the thermally aged ones, using transmission electron microscopy (TEM), in order to assess the effect of deformation on the precipitation microstructure and especially on the kinetics of precipitate growth. The absence of any significant effect of superimposed straining during thermal cycling is discussed. Implications for the prevision of yield strength degradation during service operation are briefly presented.

     

Introduction to the special issue on IEEE NEWCAS 2017

A contactless detector is presented for evaluating hand tremors caused by exercise-induced fatigue and early Parkinson’s disease. The device consists of a spiral coil, a microcontroller, and an inductive sensor circuitry. Theory shows that the resonant frequency of the circuitry increases when the distance between the hand and the spiral coil decreases, thus small variations of distance from tremor can be detected from the changes of resonant frequencies. A mechanical hand was built for experiments to simulate human hand tremors with repeatability at a fixed frequency. The magnitudes and frequencies of the tremors in the mechanical hand were quantitatively identified using the inductive sensor. Hence, feasibility and accuracy of the contactless hand tremor detector were determined. A triaxial accelerometer was used for comparison. By comparing spectral distributions and magnitudes of the tremors, the inductive sensor performed better than the accelerometer. The detector was applied to evaluate actual hand tremors of three subjects who had undergone exercise to induce tremors. The tremor waveform amplitudes of the subjects were quantitatively analyzed by the standard deviations method. The increased signal energies of exercise-induced tremor within 8–12 Hz were confirmed. Then, a subject with early Parkinson’s disease was evaluated by the proposed hand tremor detector. The tremor magnitudes and frequencies of the patient hand were quantitatively identified within in 4–7 Hz. Therefore, the new contactless hand tremor detector can be developed as a clinical instrument for monitoring the fatigue symptoms of post-exercise and diagnosing the early Parkinson’s disease.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.