Physicochemical and technological properties of beef burger as influenced by the addition of pea fibre

This study aimed to evaluate the physicochemical characteristics and sensory attributes of beef burgers with the addition of pea fibre as a partial substitute of meat or fat. Three formulations were prepared: control (CON) – similar to the commercial formulation; fibre/less meat (FLM)—5% meat reduction and addition of 1% pea fibre; fibre/less fat (FLF)—7% fat reduction and addition of 1% pea fibre. Non-significant differences were obtained for pH, colour parameters (L* and b*), texture profile, cooking loss and size reduction among formulations. Moreover, sensory analysis with consumers of beef burgers did not indicate differences among the formulations for all the analysed attributes. Therefore, pea fibre is a promising partial replacer for meat and fat in beef burgers due to the preservation of technological parameters and sensory acceptance.     

Synthesis and Properties of Azo Compounds Based on Nitroaniline Derivatives of 2,4,6-Trinitrotoluene and 1,3,5-Trinitrobenzene

Theoretical Foundations of Chemical Engineering - New research results, as well as those published earlier by the authors, on the synthesis of azo compounds based on nitroanilines obtained by the...     

Hydrogen Production from Catalytic Steam Reforming of Bio-Oils: A Critical Review

In the future, hydrogen will be an important energy carrier and industrial raw material. Catalytic steam reforming of bio-oils is a promising and economically viable technology for hydrogen production. However, during the reforming process, the catalysts are rapidly deactivated due to coke formation and sintering. Thus, maintaining the activity and stability of catalysts is the key issue in this process. Optimized operation conditions could extend the catalyst lifetime by affecting the coke morphology or promoting coke gasification. This article summarizes the recent developments in the field of catalytic steam reforming of bio-oils, focusing on the operation conditions, the properties of the catalysts, and the effects of the catalyst supports. The expected insights into the catalytic steam reforming of bio-oils will provide further guidance for hydrogen production from bio-oils.     

Development of a new ion-imprinted polymer (IIP) with Cd2+ ions based on divinylbenzene copolymers containing amidoxime groups

Polymer Bulletin - In this study, we synthesized a new ion-imprinted polymer (IIP) based on introduction of amidoxime groups in acrylonitrile, complexation with Cd2+ ions and polymerization with...     

Ultrasonic Technique for Assessment of the Rim Tightness of Hydro Generator Rotor

At the hydroelectric unit No. 11 of the Nizhnekamsk hydroelectric power plant, the filling wedges of a rotor were investigated by ultrasound in the free st     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Influence of Sodium Microalloying on Rolling Contact Endurance and Mechanical Properties of Hot-Deformed Powder Steels

Russian Journal of Non-Ferrous Metals - Rolling bearing rings comprise a large reserve for expanding powder metallurgy production. This reserve is being incompletely implemented. The hot forging of...     

Related Oscillations of Plasmons in Two Similar Nanocylinders from Silver

Journal of Communications Technology and Electronics - Abstract—The two-dimensional diffraction problem of a plane polarized TM-wave on two identical solid silver nanocylinders is considered....