Microwave hybrid fast sintering of red clay ceramics

This work aimed to examine the performance of the hybrid sintering of clay ceramic in a microwave furnace, compared to the sintering process in a conventional furnace. The raw materials were subjected to X-ray fluorescence, loss on ignition (LOI), X-ray diffraction, particle size distribution, real specific mass, and thermogravimetric analyses. The red clay ceramic mass was prepared, extruded, pre-sintered in a conventional furnace at 600°C/60 min, and sintered at temperatures between 700 °C and 1100 °C. The sintering conventional (resistive oven) was carried out for 60 min with a heating rate of 10°C/min. In the microwave furnace, the sintering times were 5, 10, and 15 min, with a heating rate of 50°C/min, with a sintering chamber coated with silicon carbide (susceptor). The sintered specimens were characterized according to linear shrinkage, water absorption, apparent porosity, apparent specific mass, X-ray diffraction, Raman spectroscopy analysis, spectroscopy analysis in the ultraviolet and visible regions, microhardness, and scanning electron microscopy. The results showed that microwave sintering promoted an increase in the microhardness and apparent specific mass, and reduction in water absorption and apparent porosity values, due to greater densification in the microstructure. The best results occurred for specimens sintered at 1100°C.     

Experimental and numerical assessment of gate-lag phenomena in AlGaAs-GaAs heterostructure field-effect transistors (FETs)

Gate-lag effects are characterized in AlGaAs-GaAs heterostructure field-effect transistors (HFETs) by means of measurements and numerical device simulations. Gate lag increasingly affects device switching at increasing ungated recess extension, suggesting that responsible deep levels be located at the ungated, recess surface of the HFET. Gate lag diminishes by making the off-state gate-source voltage less negative and by increasing the drain bias. Increasing the temperature makes the turn-on transient faster at low drain bias, while slightly delaying it at high drain bias. Numerical device simulations accounting for acceptor-like traps at the ungated surface predict gate-lag phenomena in good agreement with experiments, reproducing correctly the observed bias and temperature dependences. Simulations show that surface states behave, during the turn-on transient, as hole traps capturing holes attracted at the ungated surface by the negative trapped charge.     

A digital architecture for support vector machines: theory, algorithm, and FPGA implementation

In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantization effects on the performance of the SVM in classification problems to show its robustness, in the feedforward phase, respect to fixed-point math implementations; then, we address the problem of SVM learning. The architecture described here makes use of a new algorithm for SVM learning which is less sensitive to quantization errors respect to the solution appeared so far in the literature. The algorithm is composed of two parts: the first one exploits a recurrent network for finding the parameters of the SVM; the second one uses a bisection process for computing the threshold. The architecture implementing the algorithm is described in detail and mapped on a real current-generation FPGA (Xilinx Virtex II). Its effectiveness is then tested on a channel equalization problem, where real-time performances are of paramount importance.     

Constructive model-based analysis for safety assessment

The aerospace industry still uses fault trees to perform reliability analysis. This is because fault-tree modeling and analysis (FTA) seems easier to practical engineers when compared with Markov models, even though FTA provides a weaker form of analysis. In this paper, we propose an automatic strategy for generating Markov-based models and corresponding analysis formulations, according to ARP 4761, directly from Simulink diagrams annotated with failure information. The generated Markov-based models are expressed in the formal language PRISM, and the analysis is carried out by the PRISM model checker. The strategy is compositional and based on a comprehensive set of translation rules from Simulink to PRISM. We briefly address soundness and completeness of the rules and, to illustrate the application of the strategy, we apply it to a classical avionics case study: an actuator control system.     

Tau Exon 10 Inclusion by PrPC through Downregulating GSK3β Activity

Tau protein is largely responsible for tauopathies, including Alzheimer’s disease (AD), where it accumulates in the brain as insoluble aggregates. Tau mRNA is regulated by alternative splicing, and inclusion or exclusion of exon 10 gives rise to the 3R and 4R isoforms respectively, whose balance is physiologically regulated. In this sense, one of the several factors that regulate alternative splicing of tau is GSK3β, whose activity is inhibited by the cellular prion protein (PrP^C), which has different physiological functions in neuroprotection and neuronal differentiation. Moreover, a relationship between PrP^C and tau expression levels has been reported during AD evolution. For this reason, in this study we aimed to analyze the role of PrP^C and the implication of GSK3β in the regulation of tau exon 10 alternative splicing. We used AD human samples and mouse models of PrP^C ablation and tau overexpression. In addition, we used primary neuronal cultures to develop functional studies. Our results revealed a paralleled association between PrP^C expression and tau 4R isoforms in all models analyzed. In this sense, reduction or ablation of PrP^C levels induces an increase in tau 3R/4R balance. More relevantly, our data points to GSK3β activity downstream from PrP^C in this phenomenon. Our results indicate that PrP^C plays a role in tau exon 10 inclusion through the inhibitory capacity of GSK3β.     

Superabsorbent hydrogel based on modified polysaccharide for removal of Pb2+ and Cu2+ from water with excellent performance

This contribution describes the absorption percentage of Pb²⁺ and Cu²⁺ from water by a superabsorbent hydrogel matrix (SH) made from an anionic polysaccharide copolymerized with acrylic acid (AAc) and acrylamide (AAm). Metal‐absorption tests, upon sequential pH variation, indicated that the SH has pH‐sensitivity for the absorption of both metals from solution, attributed to the functional ionic groups (? COOH) present in the AAc and arabic gum (AG) segments. At the pH 5.0, the SH exhibited good absorption capacity: 73.10% for Pb²⁺, 81.99% for Cu²⁺ in water and 63.64% for Pb²⁺, and 76.67% for Cu²⁺ in saline water with 0.1 mol kg^?1 ionic strength. A replicated 2² full factorial design with a central point was built to evaluate the maximum absorption capacity of the metals into the SH. It was found that both the interaction and main effects of the pH and the initial concentration of metal solution on absorption percentage of the metals were statistically significant. Surface response plots indicated that the absorption capacity of both metals into the SH may be appreciably improved by using the solutions with lower initial concentration of metal and with higher pH values. Metal‐absorption results demonstrated that the SH is a convenient material for absorption of Pb²⁺ and Cu²⁺ from pure aqueous and saline aqueous environments. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Well defined CuI(NO), CuI(NO)2 and CuII(NO)X (X = O− and/or NO 2 − ) complexes in CuI-ZSMS prepared by interaction of H-ZSM5 with gaseous CuCl

In this note an exchange procedure of the acidic protons of H-ZSM5 by Cu^I ions through reaction with CuCl in the gas phase is described. In the so obtained Cu^I-ZSM5 exchanged zeolite the Cu^I ions are in well defined configuration and form with NO mono and di-nitrosyl complexes of high structural and spectroscopic quality. The Cu^I(NO)₂ species are transformed at RT into Cu^II(NO)X (X=O^– and/or NO ₂ ^– ) species which could represent an intermediate in NO decomposition.     

LRCN-RetailNet: A recurrent neural network architecture for accurate people counting

Multimedia Tools and Applications - Measuring and analyzing the flow of customers in retail stores is essential for a retailer to better comprehend customers’ behavior and support...     

胎儿心电提取方法研究综述

胎儿心电信号提取是一个复杂的信号处理问题。本文首先对自适应滤波技术、独立分量分析方法和盲源提取技术等三种典型的胎儿心电提取方法的基本原理进行了阐述,然后对各方法的改进研究也进行了综述,并对比分析了三种方法的优缺点,指出基于二阶统计量盲提取方法,根据胎儿心电周期与母亲心电周期的差异,只提取胎儿心电,计算量小,便于在线实时处理,在今后的应用中将会有广阔前景。     

Pointwise and pairwise clothing annotation: combining features from social media

In this paper, we present effective algorithms to automatically annotate clothes from social media data, such as Facebook and Instagram. Clothing annotation can be informally stated as recognizing, as accurately as possible, the garment items appearing in the query photo. This task brings huge opportunities for recommender and e-commerce systems, such as capturing new fashion trends based on which clothes have been used more recently. It also poses interesting challenges for existing vision and recognition algorithms, such as distinguishing between similar but different types of clothes or identifying a pattern of a cloth even if it has different colors and shapes. We formulate the annotation task as a multi-label and multi-modal classification problem: (i) both image and textual content (i.e., tags about the image) are available for learning classifiers, (ii) the classifiers must recognize a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to assign to the query photo comes from a set of instances that is used to build a function, which separates labels that should be assigned to the query photo, from those that should not be assigned. Using this configuration, we propose two approaches: (i) the pointwise one, called MMCA, which receives a single image as input, and (ii) a multi-instance classification, called M3CA, also known as pairwise approach, which uses pair of images to create the classifiers. We conducted a systematic evaluation of the proposed algorithms using everyday photos collected from two major fashion-related social media, namely pose.com and chictopia.com. Our results show that the proposed approaches provide improvements when compared to popular first choice multi-label, multi-modal, multi-instance algorithms that range from 20 % to 30 % in terms of accuracy.