In Vivo and In Vitro Assessment of Antinutritional Factors in Peanut and Soy

Raw and heat processed peanut flours were found to contain higher in vitro trypsin inhibitor activity and lectin content than similarly processed soy flour. When fed to weanling rats at a level of 10% protein, the response pattern in food consumption, growth, liver function, and serum chemistries was not consistent among or between the groups. It is concluded that the levels of antinutritional factors in legumes do not correlate with their overall biological impact in feeding studies with the rat.     

Input-parallel output-parallel configuration of boost half bridge DC-DC converter with control strategy

The concept of connecting two boost half bridge DC-DC converter modules in input-paral- lel output-parallel configuration is presented. The input-parallel-output-parallel （IPOP） converter consists of multiple boost half bridge （BHB） DC-DC converter modules which are connected in par- allel at the input and output side. This kind of converter is an attractive solution for high power ap- plications. The correlation between input current sharing （ICS） and output current sharing （OCS） of the IPOP converter basic modules is described. Two loop control strategies, consisting of input cur- rent loop and output voltage loop, have been developed to achieve equal ICS and OCS in this present work. The control strategy for the IPOP configuration of boost haft bridge DC-DC converter has been verified for different load conditions （half load and full load）, The IPOP system proposed here is comprising of two modules but it can be extended to three or more. The performance of the pro- posed system along with the control strategy is verified by simulation in MATLAB using Simpower tool. Finally the satisfactory simulation results are obtained.     

大规模MIMO系统下的低复杂度迫零预编码技术

针对大规模天线技术导致传统迫零(zero-forcing, ZF)预编码复杂度上升的问题,提出了一种低复杂度预编码技术。首先利用对称超松弛迭代(semi-iteration symmetric successive overrelaxation method, SSOR)技术优化信道矩阵求逆复杂度的问题;然后利用切比雪夫半迭代法(semi-iteration, SI)加速SSOR迭代(semi-iteration symmetric successive overrelaxation method, SI-SSOR)的收敛速度,使得ZF预编码可以快速的收敛。实验结果表明,SI-SSOR预编码在误码率和传输速率上都有非常好的性能。切比雪夫半迭代法加速的SSOR预编码技术通过2次迭代就可以近似达到传统ZF预编码性能的95%以上,同时利用相对于迫零预编码更少的计算资源,因此,SI-SSOR预编码技术可以作为无线通信抑制干扰的重要手段。     

Siting park-and-ride facilities using a multi-objective spatial optimization model

Park-and-ride services are an important component of many public transportation systems in the United States. Locating park-and-ride facilities is an essential step when planning for these services. In this research we focus on three major siting/modeling concerns that need to be addressed when siting park-and-ride facilities: covering as much potential demand as possible, locating park-and-ride facilities as close as possible to major roadways, and siting such facilities in the context of an existing system. Unfortunately, existing models do not enable each of these concerns to be simultaneously addressed. This paper presents a multi-objective spatial optimization model for integrating these considerations. This model is applied for siting park-and-ride facilities in Columbus, Ohio. Application results show the usefulness of the developed model in supporting transit planning in an urban region.     

Giant Anomalous Hall and Nernst Conductivities in Magnetic All-d Metal Heusler Alloys

All-d Heuslers are a category of novel compounds combining versatile functionalities such as caloric responses and spintronics with enhanced mechanical properties. Despite the promising transport properties (anomalous Hall (AHC) and anomalous Nernst (ANC) conductivities) shown in the conventional Co₂XY Heuslers with p-d hybridization, the all-d Heuslers with only d-d hybridization open a new horizon to search for new candidates with outstanding transport properties. In this work, the AHC and ANC are evaluated for thermodynamically stable ferro/ferri-magnetic all-d-metal regular Heusler compounds based on high-throughput first-principles calculations. It is observed that quite a few materials exhibit giant AHCs and ANCs, such as cubic Re₂TaMn with an AHC of 2011 S cm^-1, and tetragonal Pt₂CrRh with an AHC of 1966 S cm^-1 and an ANC of 7.50 A m^-1K^-1. Comprehensive analysis on the electronic structure reveals that the high AHC can be attributed to the occurrence of the Weyl nodes or gapped nodal lines in the neighborhood of the Fermi level. The correlations between such transport properties and the number of valence electrons are also thoroughly investigated, which provides a practical guidance to tailor AHC and ANC via chemical doping for transverse thermoelectric applications.     

Cu<Superscript>2+</Superscript> and Al<Superscript>3+</Superscript> co-substituted cobalt ferrite: structural analysis,morphology and magnetic properties

Cu–Al substituted Co ferrite nanopowders, Co_1?x Cu _x Fe_2?x Al _x O₄ (0.0 ≤ x ≤ 0.8) were synthesized by the co-precipitation method. The effect of Cu–Al substitution on the structural and magnetic properties have been investigated. X-ray diffraction (XRD) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FESEM) and vibrating sample magnetometer (VSM) are used for studying the effect of variation in the Cu–Al substitution and its impact on particle size, magnetic properties such as M _s and H _c . Cu–Al substitution occurs and produce a secondary phase, α-Fe ₂ O ₃. The crystallite size of the powder calcined at 800 ^°C was in the range of 19–26 nm. The lattice parameter decreases with increasing Cu–Al content. The nanostructural features were examined by FESEM images. Infrared absorption (IR) spectra shows two vibrational bands; at around 600 (v ₁) and 400 cm ^?1 (v ₂). They are attributed to the tetrahedral and octahedral group complexes of the spinel lattice, respectively. It was found that the physical and magnetic properties have changed with Cu–Al contents. The saturation magnetization decreases with the increase in Cu–Al substitution. The reduction of coercive force, saturation magnetization and magnetic moments are may be due to dilution of the magnetic interaction.     

Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation

Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers.     

Decentralized state estimation in large-scale systems

A novel approach to decentralized state estimation in a large-scale interconnected system is proposed. The method assumes a known model for the local subsystem only, and therefore is suitable when the other subsystem models and the interaction matrices are partially or totally unknown. An innovation representation suitable for decentralized subsystem state estimation is derived. The state estimation problem is then solved through the parametric identification of the innovation representation. The identification algorithm is based upon a pseudo-linear regression (PLR) principle that attempts minimization of the innovation variances.     

Autoregressive models in stochastic approximation algorithms for parameter estimation

This paper describes an on-line procedure for estimating the parameters of linear discrete time systems when input and output are subjected to measurement noise of unknown statistics. The algorithm is derived through stochastic approximation, To ensure unbiased parameter estimates, the correlated part of the residuals are first estimated by modelling the residuals as an autoregressive series, and then subtracted from the estimated residuals. The algorithm estimates the system parameters and noise parameters simultaneously. Three gain expressions are derived for the estimation algorithm. They are («) scalar gain, (b) diagonal matrix gain, and (c) square matrix gain.