Carbon nanotubes‐filled thermoplastic polyurethane–urea and carboxylated acrylonitrile butadiene rubber blend nanocomposites

This article reports the preparation and characterization of multiwalled carbon nanotubes (MWCNTs)‐filled thermoplastic polyurethane–urea (TPUU) and carboxylated acrylonitrile butadiene rubber (XNBR) blend nanocomposites. The dispersion of the MWCNTs was carried out using a laboratory two roll mill. Three different loadings, that is, 1, 3, and 5 wt % of the MWCNTs were used. The electron microscopy image analysis proves that the MWCNTs are evenly dispersed along the shear flow direction. Through incorporation of the nanotubes in the blend, the tensile modulus was increased from 9.90 ± 0.5 to 45.30 ± 0.3 MPa, and the tensile strength at break was increased from 25.4 ± 2.5 to 33.0 ± 1.5 MPa. The wide angle X‐ray scattering result showed that the TPUU:XNBR blends were arranged in layered structures. These structures are formed through chemical reactions of ? NH group from urethane and urea with the carboxylic group on XNBR. Furthermore, even at a very low loading, the high degree of nanotubes dispersion results in a significant increase in the electrical percolation threshold. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40341.     

Synergistic fire-retardancy properties of melamine coated ammonium poly(phosphate) in combination with rod-like mineral filler attapulgite for polymer-modified bitumen roofing membranes

A novel intumescent (carbonization, acid donor and foaming) fire retardant that mimics carbon nanotubes was introduced into bitumen roofing and characterized using cone calorimetry as the main analytical tool. The experimental results indicate that 18% (by mass) attapulgite mineral (ATTP) mixed with base bitumen decreased the peak heat release rate per unit area (pHRRPUA) by 10%. Further, incorporation of melamine coated ammonium polyphosphate (MAPP) decreased the pHRRPUA by 52% and a mixture of these (3:1, ATTP:MAPP) decreased the pHRRPUA by 25% as compared to adding CaCO₃ as a filler. The residual mass loss after the cone test was also improved with up to 3%. The indication of a positive synergistic flame retardant effect of the ATTP-MAPP mixture is supported by thermogravimetric analysis. The addition of this rod-like mineral improved the general fire retardant properties of the base bitumen and increased the viscosity. Therefore, the polymer-modified bitumen with both fire retardant and rheological properties (providing mechanical strength) is a promising novel approach in the design of bitumen roofing membranes.     

Effect of BiAlO3 concentration on the dielectric and piezoelectric properties of lead-free (Bi0.5Na0.5)0.94Ba0.06TiO3 piezoelectric ceramics

In this paper, lead-free (1-x)(Bi_0.5Na_0.5)_0.94Ba_0.06TiO₃-xBiAlO₃ (BNBT-BA, x?=?0, 0.010, 0.015, 0.020, 0.025, and 0.030) piezoelectric ceramics were synthesized using a conventional solid-state reaction method. The effect of BiAlO₃ concentration on dielectric, ferroelectric and piezoelectric properties were investigated. The ferroelectric and piezoelectric properties of BNBT ceramics are significantly influenced by the presence of BA. In the composition range studied, X-ray diffraction revealed a perovskite phase with the coexistence of rhombohedral and tetragonal phases. The temperature dependence of dielectric properties showed that the depolarization temperature (T _d) shifted towards lower temperatures and that the degree of diffuseness of the phase transition around T _d and T _m became more obvious with increasing BiAlO₃ content. The remanent polarization increased with increasing BA, and reached a maximum value of 30 μC/cm² at x?=?0.020. As a result, at x?=?0.020, the piezoelectric constant (d ₃₃) and the electromechanical coupling factor (k _p) of the ceramics attained maximum values of 188 pC/N and 34.4 %, respectively. These results indicate that BNBT-BA ceramics is a promising candidate for lead-free piezoelectric materials.     

Synthesis,Characterization and Surface Properties of Amidosulfobetaine Surfactants Bearing Odd-Number Hydrophobic Tail

Three amidosulfobetaine surfactants were synthesized namely: 3-(N-pentadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2a); 3-(N-heptadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2b), and 3-(N-nonadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2c). These surfactants were prepared by direct amidation of commercially available fatty acids with 3-(dimethylamino)-1-propylamine and subsequent reaction with 1,3-propanesultone to obtain quaternary ammonium salts. The synthesized surfactants were characterized by IR, NMR and mass spectrometry. Thermogravimetric analysis (TGA) results showed that the synthesized surfactants have excellent thermal stability with no major thermal degradation below 300 °C. The critical micelle concentration (CMC) values of the surfactants 2a and 2b were found to be 2.2 × 10^?4 and 1.04 × 10^?4 mol/L, and the corresponding surface tension (γ_CMC) values were 33.14 and 34.89 mN m^?1, respectively. The surfactants exhibit excellent surface properties, which are comparable with conventional surfactants. The intrinsic viscosity of surfactant (2b) was studied at various temperatures and concentrations of multi-component brine solution. The plot of natural logarithm of relative viscosity versus surfactant concentration obtained from Higiro et al. model best fit the surfactant behavior. Due to good salt resistance, excellent surface properties and thermal stability, the synthesized surfactant has potential to be used in various oil field applications such as enhanced oil recovery, fracturing, acid diversion, and well stimulation.     

Robust forecasting of mortality and fertility rates: A functional data approach

A new method is proposed for forecasting age-specific mortality and fertility rates observed over time. This approach allows for smooth functions of age, is robust for outlying years due to wars and epidemics, and provides a modelling framework that is easily adapted to allow for constraints and other information. Ideas from functional data analysis, nonparametric smoothing and robust statistics are combined to form a methodology that is widely applicable to any functional time series data observed discretely and possibly with error. The model is a generalization of the Lee-Carter (LC) model commonly used in mortality and fertility forecasting. The methodology is applied to French mortality data and Australian fertility data, and the forecasts obtained are shown to be superior to those from the LC method and several of its variants.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

A free space frequency‐time‐domain technique for electromagnetic characterization of materials using reflection‐based measurement

In this report, a free space frequency‐time‐domain technique is presented for characterizing the electrical properties and thickness of the sample using multiple reflections and fabry‐perot resonance phenomenon. The retrieval of constitutive electromagnetic parameters of the sample has been carried out by comparing the measured reflection coefficient data from the sample at two different incident angles. The relative permittivity as well as relative permeability along with the thickness of different samples viz., beryllia, silicon, and plexiglass have been evaluated with high accuracy in the frequency range 1 to 15 GHz. The method is also experimentally validated by successfully reconstructing the unknown material properties of two different samples. The unique advantage of this method lies in non‐requirement of any prior knowledge of the sample's thickness for measuring the complex relative dielectric constant as well as relative permeability of the sample. To determine the electromagnetic properties of the sample, the sole knowledge of reflection coefficient data are needed. Moreover, the method does not involve any additional measurement for the reference calibration. The simple, cost‐effective proposed scheme is quite useful in many applications like accurate determination of signal strength in indoor wireless communication, through wall imaging, food industry, and so on.     

A comparative study of rate monotonic schedulability tests

With the increased penetration of real-time systems into our surroundings, the selection of an efficient schedulability test under fixed priority system from a plethora of existing results, has become a matter of primary interest to real-time system designers. The need for a faster schedulability tests becomes more prominent when it applies to online systems, where processor time is a sacred resource and it is of central importance to assign processor to execute tasks instead of determining system schedulability. Under fixed priority nonpreemptive real-time systems, current schedulability tests (in exact form) can be divided into: response time based tests, and scheduling points tests. To the best of our knowledge, no comparative study of these test to date has ever been presented. The aim of this work is to assist the system designers in the process of selecting a suitable technique from the existing literature after knowing the pros and cons associated with these tests. We highlight the mechanism behind the feasibility tests, theoretically and experimentally. Our experimental results show that response time based tests are faster than scheduling points tests, which make the response time based tests an excellent choice for online systems.     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.