Consideration of uncertainties in seismic analysis of coupled building piping systems

In this paper, we present an analytical study for incorporating the effect of uncertainties in modal properties of uncoupled primary and secondary systems in the seismic analysis of non-classically damped coupled systems such as building piping by response spectrum method. Monte Carlo simulation is used to illustrate that the secondary system design response when defined at a non-exceedence probability of 0.84 over the individual responses obtained from multiple response spectrum analyses by considering uncertainties in modal parameters is excessively higher than the design response specified at the same non-exceedence probability over the responses obtained from multiple time history analyses. This is so because the earthquake input in a response spectrum method is characterized by a design spectrum which by itself is specified at a non-exceedence probability of 0.84 over the multiple time histories with normalized peak ground acceleration. Accurate evaluation of design response at a non-exceedence probability of 0.84 in the response spectrum method requires that the individual modal responses be defined at appropriate probability levels that may be different than the conventionally used non-exceedence probability value of 0.84. The required probability values are evaluated by using first order reliability method. It is shown that the modal responses, when defined at a non-exceedence probability of 0.84, would give relatively accurate values of design response only if the individual modes are perfectly correlated or a single mode contributes to the particular response quantity of interest. For all other cases, the design response would be excessively high. The accurate probability values needed to specify each modal response evaluated using the first order reliability method cannot be incorporated directly in a response spectrum analysis due to computational inefficiency. Two simplified methods, based on total probability theorem, are developed in this paper to overcome this limitation. It is shown that these methods give design response values that are very close to the true values obtained from multiple time history analyses.     

Endogenous Levels of Five Fatty Acid Metabolites in Exhaled Breath Condensate to Monitor Asthma by High-Performance Liquid Chromatography: Electrospray Tandem Mass Spectrometry

Airway inflammation characterizing asthma and other airway diseases may be monitored through biomarker analysis of exhaled breath condensate (EBC). In an attempt to discover novel EBC biomarkers, a high performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) method was used to analyze EBC from ten control non-asthmatics and one asthmatic individual for five fatty acid metabolites: 9,12,13-trihydroxyoctadecenoic acid (9,12,13-TriHOME), 9,10,13-TriHOME, 12,13-dihydroxyoctadecenoic acid (12,13-DiHOME), 12-hydroxyeicosatetraenoic acid (12-HETE), and 12(13)-epoxyoctadecenoic acid (12(13)-EpOME). The method was shown to be sensitive, with an on-column limit of quatitation (LOQ) in the pg range (corresponding to pM concentrations in EBC), and linear over several orders of magnitude for each analyte in the calibrated range. Analysis of EBC spiked with the five fatty acid metabolites was within 81%-119% with only a few exceptions. Endogenous levels in EBC exhibited intra- and inter-assay precision of 10%-22%, and 12%-36%, respectively. EBC from the healthy subjects contained average analyte levels between 15 and 180 pM with 12-HETE present above the LOQ in only one of the subjects at a concentration of 240 pM. Exposure of the asthmatic subject to allergen led to increased EBC concentrations of 9,12,13-TriHOME, 9,10,13-TriHOME, 12,13-DiHOME, and 12(13)-EpOME when compared to levels in EBC collected prior to allergen exposure (range =40-510 pM). 12,13-DiHOME was significantly increased (Student's t-test, p < 0.05). In conclusion, we have developed a new HPLC-ESI-MS/MS method for the analysis of five fatty acid metabolites in EBC, which are potential biomarkers for asthma monitoring and diagnosis.     

Micellization and gelation of PEO-PPO-PEO binary mixture with non-identical PPO block lengths in aqueous solution

Sol/Gel behavior of a mixture of two Pluronic species with non-identical polypropylene oxide (PPO) and polyethylene oxide (PEO) block lengths in aqueous medium is explored. Emphasis is placed on the nature of micelles (either mixed or separate micelles), nature of gelation, gel strength and microstructure. Two mixtures are considered: F127 + L64 and F127 + P105. Cooperative aggregation leading to mixed micelles is found for both cases via dynamic light scattering, differential scanning calorimetry and cloud point temperature experiments. From rheometry, F127 + L64 is found to exhibit two hard gel states separated by a soft gel as the temperature increases, although each species alone does not show such behavior. The first hard gel formed at lower temperature appears clear and has a cubic packing structure, while the second hard gel at higher temperature is hexagonally structured and appears hazy. Interestingly, the second hard gel is observed only during the temperature ramp up process, whereas the first gel is seen both in the ramp up and down processes. The aforementioned behavior does not occur for F127 + P105.     

Mechanical-Thermal-Electrical and Morphological Properties of Graphene Reinforced Polymer Composites: A Review

Grephene is a substance composed of pure carbon with atoms arranged in a regular hexagonal pattern similar to graphite, but in a one-atom thick sheet. It is very light, with a 1-square-meter sheet weighing only 0.77 mg. It is the basic structural element of some carbon allotropes including graphite, charcoal, carbon nanotubes and fullerenes. It has considerable interest over recent years due to its intrinsic mechanical, thermal and electrical properties. Incorporation of small quantity of graphene fillers into polymer can create novel nano-composites with improved structural and functional properties. Recent investigation clearly confirmed that graphene-polymer nano-composites are promising materials with applications ranging from transportation, biomedical systems, sensors, electrodes for solar cells and electromagnetic interference. This review discusses the different methods of manufacturing graphene based composites and also compiles their electrical, mechanical and thermal properties. Many references to the latest work on properties and processing have been cited in this review.     

Study of dielectric relaxation behavior of electron beam-cured conductive carbon black-filled ethylene acrylic elastomer

Composites based on ethylene acrylic elastomer (AEM) filled with a special type of conductive carbon black (CCB) have been prepared by two-roll mixing mill. The compression-molded sheet of the prepared composites have been subjected to electron beam (EB) radiation dose up to 400 kGy to induce radiation crosslinked composites. The crosslinked density has been calculated according to Flory–Rehner equation and is found to increase with increasing EB dose and CCB loading. Chain scission-to-crosslink density has been calculated by Charlesby–Pinner equation, which shows decreasing trend with increasing radiation dose. The dielectric relaxation behaviors of different doses of EB-treated AEM/CCB composites have been extensively studied as a function of frequency of applied electric field (10¹–10⁶ Hz), CCB loading [0–30 phr (parts per hundred)], temperature (25–120 °C), and EB dose (50–400 kGy). It is observed that the dielectric permittivity (ε′) increases with CCB loading and temperature, but decreases with increasing EB dose. This can be explained on the basis of interfacial polarization. Based on dielectric loss tangent (tan δ) values, it is observed that the dielectric relaxation time decreases with increases in the filler loading and temperature. However, it increases with increase in the radiation doses. Both the real and imaginary parts of the impedance (Z′ and Z″) have been found to decrease with increase in conductive filler loading. The AC conductivity (σ_ac) increases with increase in the CCB concentration, test temperature, and radiation doses, which is attributed to the more pronounced hopping and tunneling mechanism. The percolation threshold (φ_crit) occurred in the range of 16 phr CCB loading. The dispersions of CCB phase in AEM matrix below and above percolation have been captured by the transmission electron microscope photomicrographs.     

Temporal clustering for order reduction of nonlinear parabolic PDE systems with time‐dependent spatial domains: Application to a hydraulic fracturing process

A temporally‐local model order‐reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time‐dependent spatial domains is presented. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, low‐dimensional models are derived by constructing appropriate temporally‐local eigenfunctions. Within this context, first of all, the time domain is partitioned into multiple clusters (i.e., subdomains) by using the framework known as global optimum search. This approach, a variant of Generalized Benders Decomposition, formulates clustering as a Mixed‐Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed‐Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low‐dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order‐reduction technique is applied to a hydraulic fracturing process described by a nonlinear parabolic PDE system with the time‐dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order‐reduction technique with temporally‐global eigenfunctions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3818–3831, 2017     

Modelling of navigation based LNA parameters using neural network technique

An approach to model the parameters of LNA which is ideal for GLONASS navigation system. To design LNA, multilayer perceptron architecture is used. The parameters of LNA are calculated using Levenberg Marquardt Backpropagation Algorithm for the frequency range 300 MHz to 18 GHz. ANN model is trained using Agilent MGA 71543 Low Noise Amplifier datasheet and this model shows high regression. The smith and polar charts are plotted for frequency range 300 MHz to 18 GHz and parameters are calculated for center frequency of L1 band of GLONASS, which is 1.602 GHz.     

Constraint integration for efficient multiview pose estimation with self-occlusions

Automatic initialization and tracking of human pose is an important task in visual surveillance. We present a part-based approach that incorporates a variety of constraints in a unified framework. These constraints include the kinematic constraints between parts that are physically connected to each other, the occlusion of one part by another and the high correlation between the appearance of certain parts, such as the arms. The location probability distribution of each part is determined by evaluating appropriate likelihood measures. The graphical (non-tree) structure representing the interdependencies between parts is utilized to "connect" such part distributions via nonparametric belief propagation. Methods are also developed to perform this optimization efficiently in the large space of pose configurations.     

Classification and weakly supervised pain localization using multiple segment representation

Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through ‘concept frames’ to ‘concept segments’ and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation.