Thiophene‐ and silarylene‐containing polyesters. Resonance effect on conductivity after polarization in an external electric field

Polyesters were synthesized by direct polycondensation of thiophene‐2,5‐dicarboxylic acid and five different silarylene‐containing diphenols using a tosyl chloride/pyridine/N,N‐dimethylformamide system as a condensing agent. Polymers were obtained in good yields and were characterized using Fourier transform infrared and NMR (¹H, ¹³C, 135‐DEPT and ²⁹Si) spectroscopy and elemental analysis. All polymers were completely soluble in aprotic organic polar solvents such as dimethylformamide, dimethylsulfoxide and N‐methyl‐2‐pyrrolidone. The range of effective mass of the polymers (m/z) was 1 × 10⁵–2 × 10⁵, determined using electrospray ionization mass spectrometry. Asymmetry and steric hindrance prevented dense packing of the polymeric chains, showing glass transition temperatures between ? 78 and ? 51 °C and loss of thermal stability at 177–199 °C (10% weight loss). Additionally, the melting points of the polyesters were found to be in the range 62–67 °C. Because of this, the samples were semi‐solid at room temperature. The optical band gaps of the polymers were observed between 4.54 and 4.48 eV, corresponding in all cases to insulator behavior. The molecular structure of the samples was studied using X‐ray diffraction, showing a degree of order that was associated with two monoclinic lattices. Additionally, the conductivity was studied using a two‐point method with contacts on top of polymer films. Prior to the electrical measurement, the samples were polarized in an external electric field of 0.8 to 6.4 V cm^?1, and the alignment of the dipoles increased the electrical conductivity. Copyright © 2012 Society of Chemical Industry     

Reducing the power of wireless terminals by adaptive baseband processing

In this paper, we illustrate specific power savings obtained from exploiting a reconfigurable mobile terminal under the 3GPP LTE standard. Building on traditional link adaptation towards maximum throughput and extended towards minimal power consumption, we add two flexible baseband components: the turbo decoder and the multiple input multiple output (MIMO) detector. Optimizing their configuration leads to larger power savings when compared to non-flexible systems only performing link adaptation. The gain observed strongly depends on the scenario. For low-activity set-ups with a few minutes of voice per day, the idle power dominates and the active data rate is relatively low. This makes analog front-end and time-domain processing dominant given their constant power consumption while MIMO detection and turbo decoding that scale with data rate play a smaller role. Still, because of its ability to improve the system spectral efficiency and hence reduce its duty cycle, an advanced MIMO detector can save 10% in power consumption, on the condition that the network requires to use MIMO. Otherwise single input single output is more power-efficient in downlink. In high-throughput scenarios, larger gains are obtained. The flexible MIMO detector can save up to 35% of average power consumption. The turbo decoding also brings some gain, saving up to 12% of power when the full bandwidth is allocated to a single user.     

Hierarchical feature selection based on relative dependency for gear fault diagnosis

Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the entire set. Consequently, algorithms to identify these features are valuable contributions. This work deals with the feature selection problem through attribute clustering. The proposed algorithm is inspired by existing approaches, where the relative dependency between attributes is used to calculate dissimilarity values. The centroids of the created clusters are selected as representative attributes. The selection algorithm uses a random process for proposing centroid candidates, in this way, the inherent exploration in random search is included. A hierarchical procedure is proposed for implementing this algorithm. In each level of the hierarchy, the entire set of available attributes is split in disjoint sets and the selection process is applied on each subset. Once the significant attributes are proposed for each subset, a new set of available attributes is created and the selection process runs again in the next level. The hierarchical implementation aims to refine the search space in each level on a reduced set of selected attributes, while the computational time-consumption is improved also. The approach is tested with real data collected from a test bed, results show that the diagnosis precision by using a Random Forest based classifier is over 98 % with only 12 % of the attributes from the available set.     

Utility of Multivariate Analysis in Modeling the Effects of Raw Material Properties and Operating Parameters on Granule and Ribbon Properties Prepared in Roller Compaction

This article aimed to model the effects of raw material properties and roller compactor operating parameters (OPs) on the properties of roller compacted ribbons and granules with the aid of principal component analysis (PCA) and partial least squares (PLS) projection. A database of raw material properties was established through extensive physical and mechanical characterization of several microcrystalline cellulose (MCC) and lactose grades and their blends. A design of experiment (DoE) was used for ribbon production. PLS models constructed with only OP-modeled roller compaction (RC) responded poorly. Inclusion of raw material properties markedly improved the goodness of fit (R²?=?.897) and model predictability (Q²?=?0.72).     

An overview of the nutritional value of beef and lamb meat from South America

The southern region of South America, a subtropical and temperate zone, is an important region for the production of beef and lamb meat, which is mainly produced in extensive pasture-based systems. Because of its content in highly valuable nutrients such as iron, zinc, selenium, fatty acids, and vitamins, meat is a unique and necessary food for the human diet in order to secure a long and healthy life, without nutritional deficiencies. Beef and lamb production systems based on temperate or tropical grasslands show interesting and, in some cases, a differential content in minerals, fatty acids and vitamins. This review deals with the distinctive aspects of the nutritional quality of beef and lamb meat produced in this region in terms of nutritional composition and the bioavailability of key nutrients related to its contribution for a healthy diet for all ages.     

Modeling energy consumption for master–slave applications

With energy costs now accounting for nearly 30 % of a datacenter’s operating expenses, energy consumption has become an important issue when designing and executing a parallel algorithm. This paper analyzes the energy consumption of MPI applications following the master–slave paradigm. The analytical model is derived for this paradigm and is validated over a master–slave matrix-multiplication. This analytical model is parameterized through architectural and algorithmic parameters, and it is capable of predicting the energy consumption for a given instance of the problem over a given architecture. We use an external, metered, power distribution unit that allows to easily measure the power consumption of computing nodes without the needing of dedicated hardware.     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017