Effect of temperature on raw whole milk density and its potential impact on milk payment in the dairy industry

The objective of this study was to determine the effect of temperature on whole milk density measured at four different temperatures: 5, 10, 15, and 20 °C. A total of ninety-three individual milk samples were collected from morning milking of thirty-two Holstein Friesian dairy cows, of national average genetic merit, once every two weeks over a period of 4 weeks and were assessed by Fourier transform infrared spectroscopy for milk composition analysis. Density of the milk was evaluated using two different analytical methods: a portable density meter DMA35 and a standard desktop model DMA4500M (Anton Paar GmbH, UK). Milk density was analysed with a linear mixed model with the fixed effects of sampling period, temperature and analysis method; triple interaction of sampling period x analysis method x temperature; and the random effect of cow to account for repeated measures. The effect of temperature on milk density (ρ) was also evaluated including temperature (t) as covariate with linear and quadratic effects within each analytic method. The regression equation describing the curvature and density–temperature relationship for the DMA35 instrument was ρ = 1.0338−0.00017T−0.0000122T² (R² = 0.64), while it was ρ = 1.0334 + 0.000057T−0.00001T² (R² = 0.61) for DMA4500 instrument. The mean density determined with DMA4500 at 5 °C was 1.0334 g cm⁻³, with corresponding figures of 1.0330, 1.0320 and 1.0305 g cm⁻³ at 10, 15 and 20 °C, respectively. The milk density values obtained in this study at specific temperatures will help to address any bias in weight–volume calculations and thus may also improve the financial and operational control for the dairy processors in Ireland and internationally.     

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     

Advancements in the Development of non-BET Bromodomain Chemical Probes

The bromodomain and extra terminal (BET) family of bromodomain-containing proteins (BCPs) have been the subject of extensive research over the past decade, resulting in a plethora of high-quality chemical probes for their tandem bromodomains. In turn, these chemical probes have helped reveal the profound biological role of the BET bromodomains and their role in disease, ultimately leading to a number of molecules in active clinical development. However, the BET subfamily represents just 8/61 of the known human bromodomains, and attention has now expanded to the biological role of the remaining 53 non-BET bromodomains. Rapid growth of this research area has been accompanied by a greater understanding of the requirements for an effective bromodomain chemical probe and has led to a number of new non-BET bromodomain chemical probes being developed. Advances since December 2015 are discussed, highlighting the strengths/caveats of each molecule, and the value they add toward validating the non-BET bromodomains as tractable therapeutic targets.     

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....     

Chlorination of Quartz-Leucoxene Concentrate of Yarega Field

Metallurgical and Materials Transactions B - The process of obtaining titanium tetrachloride (TiCl4) from the quartz-leucoxene concentrate (QLC) from the Yaregskoe oil-titanium field was studied....