The advantages of using impact resonance test in dynamic modulus master curve construction through the abbreviated test protocol

Asphalt concrete behavior is heavily dependent on temperature and loading rate. Hence, the material is typically tested at a range of temperatures and loading frequencies to capture its properties. Results are then used to develop a master curve exhibiting material behavior at the full spectrum of loading frequencies. An abbreviated testing protocol, under AASHTO PP 61-13, proposes a practical approach for development of this master curve. In this practice, the low temperature asymptote of the master curve is dominated by the limiting maximum modulus estimated through the Hirsch model. In this study, the dynamic modulus (DM) testing coupled with impact resonance (IR) test was used to evaluate the effect of this limiting maximum modulus on construction of the asphalt concrete master curve. Three different asphalt mixtures prepared with the same gradation and binder content, but different grades of stiffness were tested. The DM testing was performed at multiple temperatures and loading frequencies. The IR tests were conducted on the same specimens at the same temperatures. Two sigmoid functions (MEPDG and Richards models), and three shift factors (Arrhenius, Williams–Landel–Ferry, and polynomial) were utilized in the analysis. Richards sigmoid function coupled with polynomial shift factor provided the best fitting accuracy to the measured data. It was observed that the limiting maximum modulus obtained from experimental data was underpredicted by that obtained from the Hirsch model. The results indicated potential benefits of the IR test as a complementary testing tool to the abbreviated DM testing protocol to reliably characterize asphalt concrete.     

Prediction of deflection of reinforced concrete shear walls

Reinforced concrete shear walls are used in tall buildings for efficiently resisting lateral loads. Due to the low tensile strength of concrete, reinforced concrete shear walls tend to behave in a nonlinear manner with a significant reduction in stiffness, even under service loads. To accurately assess the lateral deflection of shear walls, the prediction of flexural and shear stiffness of these members after cracking becomes important. In the present study, an iterative analytical procedure which considers the cracking in the reinforced concrete shear walls has been presented. The effect of concrete cracking on the stiffness and deflection of shear walls have also been investigated by the developed computer program based on the iterative procedure. In the program, the variation of the flexural stiffness of a cracked member has been evaluated by ACI and probability-based effective stiffness model. In the analysis, shear deformation which can be large and significant after development of cracks is also taken into account and the variation of shear stiffness in the cracked regions of members has been considered by using effective shear stiffness model available in the literature. Verification of the proposed procedure has been confirmed from series of reinforced concrete shear wall tests available in the literature. Comparison between the analytical and experimental results shows that the proposed analytical procedure can provide an accurate and efficient prediction of both the deflection and flexural stiffness reduction of shear walls with different height to width ratio and vertical load. The results of the analytical procedure also indicate that the percentage of shear deflection in the total deflection increases with decreasing height to width ratio of the shear wall.     

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-against-all support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three state-of-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified. We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.     

Capacitor Applications of Nanocomposite Films for Poly(3,4-ethylenedioxythiophene-co-pyrrole)/Multi-Walled Carbon Nanotubes and Poly(3,4-ethylenedioxythiophene-co-pyrrole)/Copper Oxide

Polypyrrole/multi-walled carbon nanotube, poly(3,4-ethylenedioxythiophene)/multi-walled carbon nanotube and their nanocomposites P(EDOT-co-Py)/multi-walled carbon nanotube and P(EDOT-co-Py)/copper (II) oxide, (CuO) in the initial feed ratio of [EDOT]₀/[Py]₀ = 1/5 were electrosynthesized on glassy carbon electrode by cyclic voltammetric method. Their characterizations were performed by cyclic voltammetric, Fourier transform infrared-attenuated total reflectance, scanning electron microscopy, energy dispersion X-ray analysis, and electrochemical impedance spectroscopy. To the best of authors’ knowledge, the first report on polypyrrole/multi-walled carbon nanotube, PEDOT/multi-walled carbon nanotube, P(EDOT-co-Py)/multi-walled carbon nanotube and P(EDOT-co-Py)/CuO nanocomposite films were comparatively examined in 0.1 M NaClO₄/CH₃CN and in 0.1 M sodium dodecyl sulfate solutions. The highest specific capacitance for PEDOT/multi-walled carbon nanotube and polypyrrole/multi-walled carbon nanotube composite films were obtained as C_sp = 306 mF × cm^?2 for 3% multi-walled carbon nanotube and C_sp = 804 mF × cm^?2 for 1% multi-walled carbon nanotube, respectively. The highest specific capacitances were obtained as C_sp = 27.40 mF × cm^?2 and C_sp = 26.90 mF × cm^?2 for P(EDOT-co-Py)/multi-walled carbon nanotube includes the wt percent of 1% multi-walled carbon nanotube and P(EDOT-co-Py)/CuO includes the wt percent of 3% CuO, respectively. The C_sp of P(EDOT-co-Py)/CNT composite films were calculated as 9.43 and 11.49 mF × cm^?2 for 3 and 5% multi-walled carbon nanotube, respectively. In addition, The EIS results were simulated with the equivalent circuit model of R_s(C_dl1(R₁(QR₂)))(C_dl2R₃).     

An integrated decision aid extension to material selection problem

Choice of an adequate material in design process is one of the critical tasks for the relevant decision-makers. At this insight, the consistency of the decisions is extremely depending on the relevance of adapted techniques to the nature of different problem cases. This paper proposed an integrated decision aid (IDEA) to match the suitable techniques with different problem cases based on the following six dimensions: (i) the type of the decision problem, (ii) the size of the problem, (iii) selection of the preference techniques by decision-makers, (iv) decision-makers’ preference structure, (v) the necessity for the use of relative importance, (vi) the nature of performance values. Furthermore, the implementation procedure of the proposed IDEA for a material selection problem is demonstrated with the previously cited applications from material science literature. Hereafter, it is expected that the IDEA provides great advantages and encouragements to researchers/practitioners in order to prevent excessive time consuming, probable misapplications, and the other challenging issues in multiple criteria analysis of material selection problems.     

Organic solvent absorbents based on linear diol

Cross‐linked poly(orthocarbonate)s were prepared by condensation of the tetraethyl orthocarbonate with different length of aliphatic diols. The synthesized polymers have been characterized by Fourier‐transform infrared spectroscopy, solid‐state ¹³C cross‐polarization magic angle spinning (CPMAS) nuclear magnetic resonance, thermal gravimetric analysis, and elemental analysis. The cross‐linked polymers were evaluated for organic solvent absorbency application. The effect of diol type on swelling properties of cross‐linked polymers was studied through the solvent absorption tests. The swelling parameters such as maximum solvent absorbency, saturation time, and retention of the solvent were evaluated for the synthesized sorbents. All of the cross‐linked polymers had moderate thermal stability and good regenerable solvent uptake abilities. POLYM. ENG. SCI., 53:2102–2108, 2013. © 2013 Society of Plastics Engineers     

The impact of a realistic packet traffic model on the performance of surveillance wireless sensor networks

It is quite common to see that classical periodic or Poisson packet traffic models are used for evaluating the performance of wireless sensor networks (WSNs). However, these models may not be appropriate for modeling the data traffic resulting from a particular application. Furthermore, they may be overestimating the performance of a WSN. In this paper, we show the significance of using a realistic and application-specific packet traffic model by comparing the performance of a well-known WSN protocol under the Surveillance WSN packet traffic model (SPTM), as well as under periodic and binomial traffic models. A packet traffic framework specific to surveillance applications is proposed which is then used for deriving SPTM analytically. In order to be adaptable and flexible, SPTM incorporates a probabilistic and parametric sensor detection model. Simulation results show that to employ an application-specific packet traffic model has significant impact on the performance evaluation of the WSN and ordinary traffic models may overestimate the capacity of the WSN.     

Digital image analysis of gunshot residue dimensional dispersion by computer vision method

Detection and identification of gunshot residues (GSR) have been used as base evidence in elucidating forensic cases. GSR particles consist of burnt and partially unburned material and contaminate the hands, face, hair, and clothes of the shooter when coming out of the gun. Nowadays, GSR samples are collected from the hands of the suspect and are analyzed routinely in forensic laboratories by the scanning electron microscope/energy dispersive spectroscopy (SEM/EDS) method. GSR particles are comprised of a morphological and specific structure (generally spherical and have a diameter between 0 and 100 μm [occasionally even larger]). In addition, the present studies in the field have claimed that GSR particles during formation are formed under equilibrium surface distribution and are unrelated to morphological dimensional classification. Our contribution to this study is two-folded. First, this study offers a new approach to identify images of GSR particles by computer vision gathered by SEM/EDS method from the hand of the shooter. Second, it presents open access to the SEM/EDS image data set of the analyzed GSR. During the study, a new data set consisting of 22,408 samples from three different types of MKEK (Mechanical and Chemical Industries Corporation) brand ammunition has been used. It is seen in the results that the computer vision method has been successful in the dimensional classification of GSR.     

A polyurethane‐based nanocomposite biocompatible bone adhesive

A novel polyurethane‐based foam‐like adhesive reinforced with nanosized hydroxyapatite (HA) particles was developed and investigated for bone‐to‐bone bonding applications in terms of mechanical adhesion and biocompatibility. The adhesive has a hierarchical structure with HA particles at the nanoscale level and pores at the micro‐scale level. This adhesive was tested mechanically in the three principal loading modes anticipated: shear, tension, and compression. Standard testing procedures were used when available. Tensile strength of primed adhesive showed a four‐fold increase in adhesion on unmodified bone and a nearly two‐fold increase in adhesion to primed bone as compared with the conventional bone cement. Biocompatibility was initially assessed in vitro using cell culture tests, which showed positive interaction with the adhesive. Then, a second biocompatibility test was performed using Xenopus laevis limbs to assess an in vivo response. The results indicated that the adhesive material produces a normal response consistent with control specimens. However, long‐term observations and tests with additional species are needed to demonstrate full biocompatibility. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2013     

Meta-Analysis of Methamphetamine Modulation on Amyloid Precursor Protein through HMGB1 in Alzheimer’s Disease

The deposition of amyloid-beta (Aβ) through the cleavage of amyloid-beta precursor protein (APP) is a biomarker of Alzheimer’s disease (AD). This study used QIAGEN Ingenuity Pathway Analysis (IPA) to conduct meta-analysis on the molecular mechanisms by which methamphetamine (METH) impacts AD through modulating the expression of APP. All the molecules affected by METH and APP were collected from the QIAGEN Knowledge Base (QKB); 78 overlapping molecules were identified. Upon simulation of METH exposure using the “Molecule Activity Predictor” feature, eight molecules were found to be affected by METH and exhibited activation relationships on APP expression at a confidence of p = 0.000453 (Z-score = 3.51, two-tailed). Core Analysis of these eight molecules identified High Mobility Group Box protein 1 (HMGB1) signaling pathway among the top 5 canonical pathways with most overlap with the 8-molecule dataset. Simulated METH exposure increased APP expression through HMGB1 at a confidence of p < 0.00001 (Z-score = 7.64, two-tailed). HMGB1 is a pathogenic hallmark in AD progression. It not only increases the production of inflammatory mediators, but also mediates the disruption of the blood-brain barrier. Our analyses suggest the involvement of HMGB1 signaling pathway in METH-induced modulation of APP as a potential casual factor of AD.