An experimental study on loading rate effect on acoustic emission based b-values related to reinforced concrete fracture

This article reports on analysis of fracture processes in reinforced concrete (RC) beams with acoustic emission (AE) technique. An emphasis was given to study the effect of loading rate on variation in AE based b-values with the development of cracks in RC structures. RC beams of length 3.2 m were tested under load control at a rate of 4 kN/s, 5 kN/s and 6 kN/s and the b-value analysis available in seismology was used to study the fracture process in RC structures. Moreover, the b-value is related to the strain in steel to assess the damage state. It is observed that when the loading rate is higher, quick cracking development lead to rapid fluctuations and drops in the b-values. Also it is observed that concrete behaves relatively more brittle at higher loading rates (or at higher strain rates). The average b-values are lower as a few but larger amplitudes of AE events occur in contrast to more number of low amplitude AE events occur at low loading rates (or at low strain rates). (C) 2014 Elsevier Ltd. All rights reserved.

Porous lithium rich Li1.2Mn0.54Ni0.22Fe0.04O2 prepared by microemulsion route as a high capacity and high rate capability positive electrode material

A porous layered composite of Li2MnO3 and LiMn0.35Ni0.55Fe0.1O2 (composition:Li1.2Mn0.54Ni0.22Fe0.04O2) is prepared by inverse microemulsion method and studied as a positive electrode material. The precursor is heated at several temperatures between 500 and 900 degrees C. The X-ray diffraction, scanning electron microscopy, and transmission electron microscopy studies suggested that well crystalline submicronsized particles are obtained. The product samples possess mesoporosity with broadly distributed pores around 10 similar to 50 nm diameter. Pore volume and surface area decrease by increasing the temperature of preparation. However, the electrochemical activity of the composite samples increases with an increase in temperature. The discharge capacity values of the samples prepared at 900 degrees C are about 186 mAh g(-1) at a specific current of 25 mA g(-1) with an excellent cycling stability. The composite sample also possesses high rate capability. The high rate capability is attributed to the porous nature of the material. (C) 2014 Elsevier Ltd. All rights reserved.

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

ELLIPSE FITTING USING FINITE RATE OF INNOVATION PRINCIPLES

We address the problem of parameter estimation of an ellipse from a limited number of samples. We develop a new approach for solving the ellipse fitting problem by showing that the x and y coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals. Uniform samples of x and y coordinate functions of the ellipse are modeled as a sum of weighted complex exponentials, for which we propose an efficient annihilating filter technique to estimate the ellipse parameters from the samples. The FRI framework allows for estimating the ellipse parameters reliably from partial or incomplete measurements even in the presence of noise. The efficiency and robustness of the proposed method is compared with state-of-art direct method. The experimental results show that the estimated parameters have lesser bias compared with the direct method and the estimation error is reduced by 5-10 dB relative to the direct method.

Appraisal of observational method for consolidation analysis

Two of the aims of laboratory one-dimensional consolidation tests are prediction of the end of primary settlement, and determination of the coefficient of consolidation of soils required for the time rate of consolidation analysis from time-compression data. Of the many methods documented in the literature to achieve these aims, Asaoka's method is a simple and useful tool, and yet the most neglected one since its inception in the geotechnical engineering literature more than three decades ago. This paper appraises Asaoka's method, originally proposed for the field prediction of ultimate settlement, from the perspective of laboratory consolidation analysis along with recent developments. It is shown through experimental illustrations that Asaoka's method is simpler than the conventional and popular methods, and makes a satisfactory prediction of both the end of primary compression and the coefficient of consolidation from laboratory one-dimensional consolidation test data.

Wind speed prediction using spatio-temporal covariance

High wind poses a number of hazards in different areas such as structural safety, aviation, and wind energy-where low wind speed is also a concern, pollutant transport, to name a few. Therefore, usage of a good prediction tool for wind speed is necessary in these areas. Like many other natural processes, behavior of wind is also associated with considerable uncertainties stemming from different sources. Therefore, to develop a reliable prediction tool for wind speed, these uncertainties should be taken into account. In this work, we propose a probabilistic framework for prediction of wind speed from measured spatio-temporal data. The framework is based on decompositions of spatio-temporal covariance and simulation using these decompositions. A novel simulation method based on a tensor decomposition is used here in this context. The proposed framework is composed of a set of four modules, and the modules have flexibility to accommodate further modifications. This framework is applied on measured data on wind speed in Ireland. Both short-and long-term predictions are addressed.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on ``abstract metrics'' that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging ``big data'' domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

On the strain rate sensitivity of plastic flow in metallic glasses

The nanoindentation technique was employed to examine the strain rate sensitivity, m, and its dependence on the structural state of a Zr-based bulk metallic glass (BMG). The free volume content in the BMG was varied by examining samples in the as-cast (AC), shot-peened (SP), and structurally relaxed (SR) states. Hardness values measured at different loading rates and over a temperature range of 300-423 K as well as the strain-rate jump tests conducted in the quasi-static regime at room temperature, show that m is always negative. All the load-displacement (P-h) curves in this temperature regime exhibit serrated load-displacement responses, indicating that the shear band mediated inhomogeneous plastic flow governs deformation. Such localization of flow and associated softening is the raison d'etre for the negative m. Significant levels of pile-up around the indents were also noted. The order in the average values of hardness, pile-up heights, and the displacement bursts on the P-h curves was always such that SR > AC > SP, which is also the order of increasing free volume content. These observations were utilized to discuss the reasons for the negative strain rate sensitivity, and its dependence on the structural state of metallic glasses. It is suggested that the positive values of m reported in the literature for them are possibly experimental artefacts that arise due to large pile ups around the indents which lead to erroneous estimation in hardness values. (C) 2014 Elsevier B.V. All rights reserved.

Characterizing the Impact of Feedback Delays on Wideband Rate Adaptation

In contemporary orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE), LTE-Advanced, and WiMAX, a codeword is transmitted over a group of subcarriers. Since different subcarriers see different channel gains in frequency-selective channels, the modulation and coding scheme (MCS) of the codeword must be selected based on the vector of signal-to-noise-ratios (SNRs) of these subcarriers. Exponential effective SNR mapping (EESM) maps the vector of SNRs into an equivalent flat-fading SNR, and is widely used to simplify this problem. We develop a new analytical framework to characterize the throughput of EESM-based rate adaptation in such wideband channels in the presence of feedback delays. We derive a novel accurate approximation for the throughput as a function of feedback delay. We also propose a novel bivariate gamma distribution to model the time evolution of EESM between the times of estimation and data transmission, which facilitates the analysis. These are then generalized to a multi-cell, multi-user scenario with various frequency-domain schedulers. Unlike prior work, most of which is simulation-based, our framework encompasses both correlated and independent subcarriers and various multiple antenna diversity modes; it is accurate over a wide range of delays.

Discrete-Rate Adaptation and Selection in Energy Harvesting Wireless Systems

In a system with energy harvesting (EH) nodes, the design focus shifts from minimizing energy consumption by infrequently transmitting less information to making the best use of available energy to efficiently deliver data while adhering to the fundamental energy neutrality constraint. We address the problem of maximizing the throughput of a system consisting of rate-adaptive EH nodes that transmit to a destination. Unlike related literature, we focus on the practically important discrete-rate adaptation model. First, for a single EH node, we propose a discrete-rate adaptation rule and prove its optimality for a general class of stationary and ergodic EH and fading processes. We then study a general system with multiple EH nodes in which one is opportunistically selected to transmit. We first derive a novel and throughput-optimal joint selection and rate adaptation rule (TOJSRA) when the nodes are subject to a weaker average power constraint. We then propose a novel rule for a multi-EH node system that is based on TOJSRA, and we prove its optimality for stationary and ergodic EH and fading processes. We also model the various energy overheads of the EH nodes and characterize their effect on the adaptation policy and the system throughput.

Influence of plate length and plate cooling rate on solidification and microstructure of A356 alloy produced by oblique plate

A356 alloy melt solidifies partially when it flows down on an oblique plate cooled from bottom by counter flowing water. Columnar dendrites are continuously formed on the plate wall. Because of the forced convection, these dendrites are sheared off into equiaxed/fragmented grains and then washed away continuously by producing semisolid slurry at plate exit. Plate cooling rate provides required extent/amount of solidification whereas plate length enables necessary shear for producing semisolid slurry of desired quality. Slurry obtained is solidified in metal mould to produce semisolid-cast billets of desired microstructure. Furthermore, semisolid-cast billets are also heat-treated to improve surface quality. Microstructures of both semisolid-cast and heat-treated billets are compared. The effects of plate length and plate cooling rate on solidification and microstructure of billets produced by using oblique plate are illustrated. Three different plate lengths (200 mm, 250 mm, 300 mm) associated with three different heat transfer coefficients (1000, 2000 and 2500 W/(m(2).K)) are involved. Plate length of 250 mm with heat transfer coefficient of 2000 W/(m(2).K) gives fine and globular microstructures and is the optimum as there is absolutely no possibility of sticking of slurry to plate wall.

Electrospun SnSb Crystalline Nanoparticles inside Porous Carbon Fibers as a High Stability and Rate Capability Anode for Rechargeable Batteries

In an electrochemical alloying reaction, the electroactive particles become mechanically unstable owing to large volume changes occurring as a result of high amounts of lithium intake. This is detrimental for long-term battery performance. Herein, a novel synthesis approach to minimize such mechanical instabilities in tin particles is presented. An optimal one-dimensional assembly of crystalline single-phase tin-antimony (SnSb) alloy nanoparticles inside porous carbon fibers (abbreviated SnSb-C) is synthesized for the first time by using the electrospinning technique (employing non-oxide precursors) in combination with a sintering protocol. The ability of antimony to alloy independently with lithium is beneficial as it buffers the unfavorable volume changes occurring during successive alloying/dealloying cycles in Sn. The SnSb-C assembly provides nontortuous (tortuosity coefficient, =1) fast conducting pathways for both electrons and ions. The presence of carbon in SnSb-C completely nullifies the conventional requirement of other carbon forms during cell electrode assembly. The SnSb-C exhibited remarkably high electrochemical lithium stability and high specific capacities over a wide range of currents (0.2-5Ag(-1)). In addition to lithium-ion batteries, it is envisaged that SnSb-C also has potential as a noncarbonaceous anode for other battery chemistries, such as sodium-ion batteries.

Crystal Structure and Prediction

The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape.

Associated jet and subjet rates in light-quark and gluon jet discrimination

We show that in studies of light quark- and gluon-initiated jet discrimination, it is important to include the information on softer reconstructed jets (associated jets) around a primary hard jet. This is particularly relevant while adopting a small radius parameter for reconstructing hadronic jets. The probability of having an associated jet as a function of the primary jet transverse momentum (PT) and radius, the minimum associated jet pi, and the association radius is computed up to next-to-double logarithmic accuracy (NDLA), and the predictions are compared with results from Herwig++, Pythia6 and Pythia8 Monte Carlos (MC). We demonstrate the improvement in quark-gluon discrimination on using the associated jet rate variable with the help of a multivariate analysis. The associated jet rates are found to be only mildly sensitive to the choice of parton shower and hadronization algorithms, as well as to the effects of initial state radiation and underlying event. In addition, the number of k(t) subjets of an anti-k(t) jet is found to be an observable that leads to a rather uniform prediction across different MC's, broadly being in agreement with predictions in NDLA, as compared to the often used number of charged tracks observable.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.