Related physicochemical,rheological, and dielectric properties of nanocomposites of superparamagnetic iron oxide nanoparticles with polyethyleneglycol

In this study, we present the physicochemical, rheological, and dielectric properties of the superparamagnetic iron oxide nanoparticles (SPIONs) coated with polyethyleneglycol (PEG). It was observed that the increase of the concentration of PEG prohibited the sedimentation of nanoparticles, which increased the colloidal stability of nanocomposites. The surface study of the polymer and the nanocomposites using TOF-SIMS showed that the samples did not have the same surface properties. The analysis of the intensity ratios of the FeO⁺/Fe⁺ and OH⁻/O⁻ peaks of the nanocomposites of bare, positively, and negatively charged SPIONs with PEG revealed that no reduction occurred for these nanoparticles when coated with PEG, whereas the iron atoms on the surface of PEG negatively charged SPIONs were less oxidized than those on the surface of PEG-bare SPIONs, and the iron atoms on the surface of PEG positively charged SPIONs were more oxidized than the second ones. The rheological study of the polymer and the nanocomposites revealed that the presence of SPIONs did not change the rheological behavior of PEG. The analysis of the dielectric properties of PEG and its composites with SPIONs showed that when SPIONs particles (bare and positively charged) were included in PEG, low frequency dispersion (LFD) was strongly enhanced, that the iron oxide nanoparticles reduced the impedance of PEG in the nanocomposites and that the difference in their surface charge affected their impedance. Taken together, these results suggest that the nanocomposites of PEG with SPIONs have appropriate properties for fluid modification and applications in the nanotechnology and photovoltaic devices. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48280.     

Enhancement in electrical and thermal performance of high-temperature vulcanized silicone rubber composites for outdoor insulating applications

High-temperature vulcanized silicone rubber composites are highly desirable as outdoor insulating materials due to their immense thermal and electrical performance. The aim of this work is to study the role of co-combined fillers (modified fumed silica [MFS], titanium dioxide [TiO₂], with graphene [G]) on electrical and thermal properties of silicone rubber (S) composites. The dielectric response of S/MFS_10 phr and S/TiO₂_20 composites tailored with 2 phr G was characterized by broadband dielectric spectroscopy. The hybrid filler/composites were found to show higher thermal stability when 2 phr G was added. In addition, a low quantity of G filler was found to slightly increase the AC dielectric breakdown strength of the S/MFS_10 and S/TiO₂_20, where an improvement of 3 and 5% was found, respectively. Several steps were observed in the thermal decomposition of the S rubber composites by thermogravimetric analysis-Fourier-transform infrared spectroscopy. Our findings revealed great potentials for fabricating hybrid-filler/silicone rubber composites with enhanced electrical and thermal properties for outdoor insulating applications.     

Base station selection and resource allocation in macro–femtocell networks under noisy scenario

In this paper, we address the problem of optimizing resources for downlink transmission in a macro–femtocell network under non-dense femtocell deployment. In the literature, some approaches perform bandwidth or power optimization depending on the air interface technology and others optimize both types of resources, but only in femtocell network. However, the following limitations can be noticed: (1) Equal distribution of transmitted power among all subcarriers, even if they are not used, leads to resource underutilization, (2) femtocell data rates are reduced in order to minimize the interference from femto base stations to macro users, and (3) the impact of noise has not been evaluated. Moreover, there is lack of optimal selection of users that can be served by femtocells. To overcome these limitations, we propose a model that finds a tradeoff between bandwidth and power to reduce the bandwidth usage per user and to minimize the impact of noise. By means of Linear Programming, our solution maximizes user satisfaction and provides optimal: serving base station, power and bandwidth for each mobile user taking into account its location and demand. Furthermore, we present a performance analysis under changes of signal to noise ratio. Simulations were conducted and a comparison with a modified version of Weighted Water Filling algorithm is presented.     

Hydraulic Performance Index of a Sewer Network

An objective methodology is proposed for evaluating the hydraulic performance for possible rehabilitation of sewer systems. It involves assigning a hydraulic performance index to each pipe section. This hydraulic index reflects both the local surcharge in a pipe and the surcharge induced at upstream sections of the same branch in a sewer network. The hydraulic index also takes into account the vulnerability and the retention capacity of each pipe section. This index may be used directly to establish the rehabilitation priority of different sections to maximize hydraulic performance for the entire network. This methodology was successfully applied to the sewer system of the city of Laval in Canada. The results show how pipe dimensions and locations have the effect of surcharging or relieving a pipe network and how the hydraulic performance index adequately rates the contributions of sewer network components.     

AC actuation contribution to the induced electromotive force in the miniaturized inductive planar microphone

In this paper, we aim to analyze the gain ratio in the output induced voltage of a CMOS-compatible miniaturized electrodynamic microphone which is based on the use of two coplanar concentric inductors. These latter are comparable to a concentric transformer with a secondary situated on the microphone diaphragm top. The study has been applied when the outer inductor is actuated using an AC bias instead of DC leading to a time-variable B-field. The induced output voltage expression has been evaluated using two distinct analytic methods: the magnetic field derivation and the electric field approach. When an AC current with frequency above that of the acoustic band is used to bias the primary, results show that the induced voltage increases and becomes independent from the incident pressure wave frequency. The paper demonstrates also that with an AC bias, the microphone induced voltage values can reach the order of hundreds of µVs, as opposed to only few µVs when applying a DC bias. The gain in the induced voltage has been investigated and it is shown that it can be correlated to the AC current frequency, ω_c, and the inner inductor fluctuation frequency, ω_p. Moreover, this has been quantified analytically and it is shown that it equals ω_c/8ω_p. This result will not only enhance the overall performance of the microphone, but will also make the preamplification design less complicated by increasing the SNR ratio.     

Joint production and supply control in three levels flexible manufacturing systems

This paper considers a stochastic optimal control problem of a three stages Flexible Manufacturing System. The supplier (i.e., upstream FMS) and the transformation stage (i.e., FMS) are both subject to random events. Our objective is to find a feedback control policy for the supply and production activities that minimizes the incurred cost. It is shown that the considered joint production and supply control problem is difficult to tackle using a dissociated analytical approach. A simulation based approach is thus proposed to achieve a close approximation of the optimal policy. The advantages of the approach include possible extensions after numerical characterization of the optimal control policy.     

Shear Strengthening of RC Beams with EB FRP: Influencing Factors and Conceptual Debonding Model

This paper deals with the shear strengthening of RC beams using externally bonded (EB) fiber-reinforced polymers (FRP). Current code provisions and design guidelines related to shear strengthening of RC beams with FRP are discussed in this paper. The findings of research studies, including recent work, have been collected and analyzed. The parameters that have the greatest influence on the shear behavior of RC members strengthened with EB FRP and the role of these parameters in current design codes are reviewed. This study reveals that the effect of transverse steel on the shear contribution of FRP is important and yet is not considered by any existing codes or guidelines. Therefore, a new design method is proposed to consider the effect of transverse steel in addition to other influencing factors on the shear contribution of FRP (Vf). Separate design equations are proposed for U-wrap and side-bonded FRP configurations. The accuracy of the proposed equations has been verified by predicting the shear strength of experimentally tested RC beams using data collected from the literature. Finally, comparison with current design guidelines has shown that the proposed model achieves a better correlation with experimental results than current design guidelines.     

Dynamic selection approaches for multiple classifier systems

In this paper we propose a new approach for dynamic selection of ensembles of classifiers. Based on the concept named multistage organizations, the main objective of which is to define a multi-layer fusion function adapted to each recognition problem, we propose dynamic multistage organization (DMO), which defines the best multistage structure for each test sample. By extending Dos Santos et al.’s approach, we propose two implementations for DMO, namely DSA ^m and DSA ^c . While the former considers a set of dynamic selection functions to generalize a DMO structure, the latter considers contextual information, represented by the output profiles computed from the validation dataset, to conduct this task. The experimental evaluation, considering both small and large datasets, demonstrated that DSA ^c dominated DSA ^m on most problems, showing that the use of contextual information can reach better performance than other existing methods. In addition, the performance of DSA ^c can also be enhanced in incremental learning. However, the most important observation, supported by additional experiments, is that dynamic selection is generally preferred over static approaches when the recognition problem presents a high level of uncertainty.     

A Novel Active Learning Method Using SVM for Text Classification

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.