Rapid determination of main constituents of packed juices by reverse phase-high performance liquid chromatography: an insight in to commercial fruit drinks

The present work reports the compositional analysis of thirteen different packed fruit juices using high performance liquid chromatography (HPLC). Vitamin C, organic acids (citric and malic) and sugars (fructose, glucose and sucrose) were separated, analyzed and quantified using different reverse phase methods. A new rapid reverse phase HPLC method was developed for routine analysis of vitamin C in fruit juices. The precision results of the methods showed that the relative standard deviations of the repeatability and reproducibility were < 0.05 and < 0.1 respectively. Correlation coefficient of the calibration models developed was found to be higher than 0.99 in each case. It has been found that the content of Vitamin C was less variable amongst different varieties involved in the study. It is also observed that in comparison to fresh juices, the packed juices contain lesser amounts of vitamin C. Citric acid was found as the major organic acids present in packed juices while maximum portion of sugars was of sucrose. Comparison of the amount of vitamin C, organic acids and sugars in same fruit juice of different commercial brands is also reported.

Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography

The sparse estimation methods that utilize the l(p)-norm, with p being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These l(p)-norm-based regularizations make the optimization function nonconvex, and algorithms that implement l(p)-norm minimization utilize approximations to the original l(p)-norm function. In this work, three such typical methods for implementing the l(p)-norm were considered, namely, iteratively reweighted l(1)-minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of l(p)-minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. (C) 2014 Optical Society of America

A performance-based framework for assessing liquefaction potential based on CPT data

This article describes a new performance-based approach for evaluating the return period of seismic soil liquefaction based on standard penetration test (SPT) and cone penetration test (CPT) data. The conventional liquefaction evaluation methods consider a single acceleration level and magnitude and these approaches fail to take into account the uncertainty in earthquake loading. The seismic hazard analysis based on the probabilistic method clearly shows that a particular acceleration value is being contributed by different magnitudes with varying probability. In the new method presented in this article, the entire range of ground shaking and the entire range of earthquake magnitude are considered and the liquefaction return period is evaluated based on the SPT and CPT data. This article explains the performance-based methodology for the liquefaction analysis – starting from probabilistic seismic hazard analysis (PSHA) for the evaluation of seismic hazard and the performance-based method to evaluate the liquefaction return period. A case study has been done for Bangalore, India, based on SPT data and converted CPT values. The comparison of results obtained from both the methods have been presented. In an area of 220 km2 in Bangalore city, the site class was assessed based on large number of borehole data and 58 Multi-channel analysis of surface wave survey. Using the site class and peak acceleration at rock depth from PSHA, the peak ground acceleration at the ground surface was estimated using probabilistic approach. The liquefaction analysis was done based on 450 borehole data obtained in the study area. The results of CPT match well with the results obtained from similar analysis with SPT data.

Sodium-ion battery cathodes Na2FeP2O7 and Na2MnP2O7: diffusion behaviour for high rate performance

Na-ion batteries are currently the focus of significant research activity due to the relative abundance of sodium and its consequent cost advantages. Recently, the pyrophosphate family of cathodes has attracted considerable attention, particularly Li2FeP2O7 related to its high operating voltage and enhanced safety properties; in addition the sodium-based pyrophosphates Na2FeP2O7 and Na2MnP2O7 are also generating interest. Herein, we present defect chemistry and ion migration results, determined via atomistic simulation techniques, for Na2MP2O7 (where M = Fe, Mn) as well as findings for Li2FeP2O7 for direct comparison. Within the pyrophosphate framework the most favourable intrinsic defect type is found to be the antisite defect, in which alkali-cations (Na/Li) and M ions exchange positions. Low activation energies are found for long-range diffusion in all crystallographic directions in Na2MP2O7 suggesting three-dimensional (3D) Na-ion diffusion. In contrast Li2FeP2O7 supports 2D Li-ion diffusion. The 2D or 3D nature of the alkali-ion migration pathways within these pyrophosphate materials means that antisite defects are much less likely to impede their transport properties, and hence important for high rate performance.

High specific surface area alpha-Fe2O3 nanostructures as high performance electrode material for supercapacitors

Porous alpha-Fe2O3 nanostructures have been synthesized by a simple sol-gel route. The alpha-Fe2O3 nanostructures are poorly crystalline and porous with BET surface area of 386 m(2) g(-1). The high discharge capacitance of alpha-Fe2O3 electrodes is 300 F g(-1) when the electrodes are cycled in 0.5 M Na2SO3 at a current density of 1 A g(-1). The capacitance retention after 1000 cycles is about 73% of the initial capacitance at a current density of 2 A g(-1). The high discharge capacitance of alpha-Fe2O3 in comparison with the literature reports are attributed to high surface area and porosity of the iron oxide prepared in the present study. As the iron oxides are inexpensive, the capacity of alpha-Fe2O3 is expected to be of potential use for supercapacitor application. (C) 2014 Elsevier B.V. All rights reserved.

Comparison of CO2 and steam in transcritical Rankine cycles for concentrated solar power

High temperature, high pressure transcritical condensing CO2 cycle (TC-CO2) is compared with transcritical steam (TC-steam) cycle. Performance indicators such as thermal efficiency, volumetric flow rates and entropy generation are used to analyze the power cycle wherein, irreversibilities in turbo-machinery and heat exchangers are taken into account. Although, both cycles yield comparable thermal efficiencies under identical operating conditions, TC-CO2 plant is significantly compact compared to a TC-steam plant. Large specific volume of steam is responsible for a bulky system. It is also found that the performance of a TC-CO2 cycle is less sensitive to source temperature variations, which is an important requirement of a solar thermal system. In addition, issues like wet expansion in turbine and vacuum in condenser are absent in case of a TC-CO2 cycle. External heat addition to working fluid is assumed to take place through a heat transfer fluid (HTF) which receives heat from a solar receiver. A TC-CO2 system receives heat though a single HTF loop, whereas, for TC-steam cycle two HTF loops in series are proposed to avoid high temperature differential between the steam and HTF. (C) 2013 P. Garg. Published by Elsevier Ltd.

Noteworthy performance of La1-xCaxMnO3 perovskites in generating H-2 and CO by the thermochemical splitting of H2O and CO2

Perovskite oxides of the composition La1-xCaxMnO3 ( LCM) have been investigated for the thermochemical splitting of H2O and CO2 to produce H-2 and CO, respectively. The study was carried out in comparison with La1-xSrxMnO3, CeO2 and other oxides. The LCM system exhibits superior characteristics in high-temperature evolution of oxygen, and in reducing CO2 to CO and H2O to H-2. The best results were obtained with La0.5Ca0.5MnO3 whose performance is noteworthy compared to that of other oxides including ceria. The orthorhombic structure of LCM seems to be a crucial factor.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Performance Analysis of Space-Shift Keying in Decode-and-Forward Multihop MIMO Networks

In this paper, space-shift keying (SSK) is considered for multihop multiple-input-multiple-output (MIMO) networks. In SSK, only one among n(s) = 2(m) available transmit antennas, chosen on the basis of m information bits, is activated during transmission. We consider two different systems of multihop co-operation, where each node has multiple antennas and employs SSK. In system I, a multihop diversity relaying scheme is considered. In system II, a multihop multibranch relaying scheme is considered. In both systems, we adopt decode-and-forward (DF) relaying, where each relay forwards the signal only when it correctly decodes. We analyze the end-to-end bit error rate (BER) and diversity order of both the systems with SSK. For binary SSK (n(s) = 2), our analytical BER expression is exact, and our numerical results show that the BERs evaluated through the analytical expression overlap with those obtained through Monte Carlo simulations. For nonbinary SSK (n(s) > 2), we derive an approximate BER expression, where the analytically evaluated BER results closely follow the simulated BER results. We show the comparison of the BERs of SSK and conventional phase-shift keying (PSK) and also show the instances where SSK outperforms PSK. We also present the diversity analyses for SSK in systems I and II, which predict the achievable diversity orders as a function of system parameters.

A fast and accurate performance analysis of beaconless IEEE 802.15.4 multi-hop networks

We develop an approximate analytical technique for evaluating the performance of multi-hop networks based on beaconless IEEE 802.15.4 ( the ``ZigBee'' PHY and MAC), a popular standard for wireless sensor networks. The network comprises sensor nodes, which generate measurement packets, relay nodes which only forward packets, and a data sink (base station). We consider a detailed stochastic process at each node, and analyse this process taking into account the interaction with neighbouring nodes via certain time averaged unknown variables (e.g., channel sensing rates, collision probabilities, etc.). By coupling the analyses at various nodes, we obtain fixed point equations that can be solved numerically to obtain the unknown variables, thereby yielding approximations of time average performance measures, such as packet discard probabilities and average queueing delays. The model incorporates packet generation at the sensor nodes and queues at the sensor nodes and relay nodes. We demonstrate the accuracy of our model by an extensive comparison with simulations. As an additional assessment of the accuracy of the model, we utilize it in an algorithm for sensor network design with quality-of-service (QoS) objectives, and show that designs obtained using our model actually satisfy the QoS constraints (as validated by simulating the networks), and the predictions are accurate to well within 10% as compared to the simulation results in a regime where the packet discard probability is low. (C) 2015 Elsevier B.V. All rights reserved.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.