Characterisation and functional properties of proteins of some Indian chickpea (Cicer arietinum) cultivars

BACKGROUND: Chickpea (Cicer arietinum L.) proteins have received attention during recent years owing to their higher biological values and better functional ingredients than oilseed proteins. In this study the composition, fractionation, electrophoretic behaviour and functional properties of five chickpea protein concentrates were determined. RESULTS: The chickpea proteins contained 15.9–54.8 g kg^?1 albumin, 48.9–154.1 g kg^?1 globulin, 39.2–76.5 g kg^?1 glutelin and traces of prolamin. Electrophoresis of the various fractions revealed that albumin and globulin were made up of sub‐units of different molecular weights ranging from 7 to 96 kDa. Water and oil absorption of the protein concentrates varied from 1.15 to 2.75 g g^?1 and from 2.60 to 5.65 g g^?1 respectively. Foaming capacity and foam stability of the protein concentrates were good and improved with the addition of salt (10 g L^?1 NaCl) or sugar (100 g L^?1 sucrose) at both isoelectric and neutral pH. Emulsifying capacity and emulsion stability of the protein concentrates were good and excellent respectively. CONCLUSION: Protein concentrates prepared from chickpeas have potential use in food formulations owing to their good emulsifying/foaming and water/oil‐binding capacities. Copyright © 2008 Society of Chemical Industry     

Fast and Ultraclean Approach for Measuring the Transport Properties of Carbon Nanotubes

In this work, a fast approach for the fabrication of hundreds of ultraclean field‐effect transistors (FETs) is introduced, using single‐walled carbon nanotubes (SWCNTs). The synthesis of the nanomaterial is performed by floating‐catalyst chemical vapor deposition, which is employed to fabricate high‐performance thin‐film transistors. Combined with palladium metal bottom contacts, the transport properties of individual SWCNTs are directly unveiled. The resulting SWCNT‐based FETs exhibit a mean field‐effect mobility, which is 3.3 times higher than that of high‐quality solution‐processed CNTs. This demonstrates that the hereby used SWCNTs are superior to comparable materials in terms of their transport properties. In particular, the on–off current ratios reach over 30 million. Thus, this method enables a fast, detailed, and reliable characterization of intrinsic properties of nanomaterials. The obtained ultraclean SWCNT‐based FETs shed light on further study of contamination‐free SWCNTs on various metal contacts and substrates.     

On Network Designs with Coding Error Detection and Correction Application

The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs. This method of networks representation has simplified the method of constructing the network designs. In this paper, a novel representation of networks is introduced and used as a technique of constructing the group generated network designs of the complete bipartite networks and certain circulants. A technique of constructing the group generated network designs of the circulants is given with group generated graph designs (GDs) of certain circulants. In addition, the GDs are transformed into an incidence matrices, the rows and the columns of these matrices can be both viewed as a binary nonlinear code. A novel coding error detection and correction application is proposed and examined.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Multi-objective optimization of a grid-connected PV system for a public building: Faculty of Sciences and Technology at Pau

Clean Technologies and Environmental Policy - In this paper, an optimal sizing of a grid-connected PV system to accommodate the load demands of a public building (i.e., Faculty of Sciences and...     

Hemostatic efficacy of a fibrin sealant dressing in an animal model of kidney injury

Arterial spin tagging techniques have been used to image tissue perfusion in MR without contrast injection or ionizing radiation. Currently, spin tagging studies are performed primarily using single-slice imaging sequences, which are time consuming. This note reports a multislice echo-planar arterial spin tagging technique (Simultaneous Multislice Acquisition with aRterial-flow Tagging, or "SMART"). Multiband RF encoding (Hadamard) is used to provide simultaneous multislice acquisition capability for spin tagging techniques (such as echo planar imaging signal targeting with alternating radio frequency and flow-sensitive alternative inversion recovery). The method is illustrated with a two-slice pulse sequence that was implemented using the FAIR technique to generate two perfusion weighted images simultaneously. Compared with single-slice sequences, this two-slice sequence provided similar image quality, signal-to-noise ratio, and twice the spatial coverage compared with the single-slice technique within the same scan time.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

Optimal Newton–Secant like methods without memory for solving nonlinear equations with its dynamics

We construct two optimal Newton–Secant like iterative methods for solving nonlinear equations. The proposed classes have convergence order four and eight and cost only three and four function evaluations per iteration, respectively. These methods support the Kung and Traub conjecture and possess a high computational efficiency. The new methods are illustrated by numerical experiments and a comparison with some existing optimal methods. We conclude with an investigation of the basins of attraction of the solutions in the complex plane.     

RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.

     

Synthesis of esters: Development of the rate expression for the Dowex 50 Wx8-400 catalyzed esterification of propionic acid with 1-propanol

The kinetics of the esterification reaction of propionic acid with 1-propanol over the ion-exchange resin Dowex 50Wx8-400 has been studied in this investigation. Kinetic experiments were conducted using a 1 L Lab-Max system at a stirrer speed of 900 rpm over a temperature range of 303.15 -333.15 K. The catalyst loading was varied from 10 to 60 g dry cat/L and acid to alcohol molar ratios of 1:1, 1:2, 1:4, 2:1 and 4:1 were employed. The equilibrium constants for this reaction were determined in separate experiments at 303.15, 313.15 and 323.15 K. The values were equal to 33.18, 30.62 and 28.37, respectively, with a standard enthalpy change of reaction of 6.4 kJ/mol. These values show the reaction to be mildly exothermic. It was found that both external and internal diffusion limitations did not affect the overall reaction rate. The conversion of propionic acid increased with increasing temperature and catalyst loading and decreased with increasing initial mole fraction of acid. The increase in chain length of acid or alcohol or branching had a retarding effect on the conversion. Several kinetic models were tested to correlate the kinetic data, the pseudo-homogeneous (P-H) model, the Eley-Rideal (E-R) model, the Langmuir-Hinshelwood (L-H) model, the modified Eley-Rideal (M-E-R) model and the modified Langmuir-Hinshelwood (M-L-H) model. In all models, the activity coefficients were estimated using UNIFAC to account for the non-ideal thermodynamic behavior of reactants and products. A correction factor for the resin affinity for water (α) was used in both M-E-R and M-L-H models. The above models predicted the kinetic behavior of the studied system with an overall error ranging from 1.65% to 13.32%. Water was found to be more strongly adsorbed than other species present in the system. The M-E-R model between adsorbed 1-propanol and non-adsorbed propionic acid which assumes surface reaction as the rate controlling step, with α equal to 2, was found to be the best model with the least overall error (1.65%). The activation energy for the esterification was estimated to be 67.3 kJ/mol by this model.