Full AC analysis of value of DER to the grid and optimal DER scheduling

This paper presents part of the work ComEd and Quanta Technology have performed to quantify the locational and temporal value of DER to avoid distribution grid upgrade investments. It focuses on the formulation of a robust and efficient algorithm for DER optimal dispatch on a distribution feeder to mitigate the violation of current and voltage limits using the allocated cost of capacity and locational marginal value of real and reactive DER injection/withdrawal.     

Amaranth functional cookies exert potential antithrombotic and antihypertensive activities

In this study, amaranth flour was used as an ingredient to prepare gluten-free cookies. The production process and attributes of amaranth cookies were characterised, and the potential use of amaranth flour as a functional ingredient was analysed. Cookies exhibited a non-uniform reddish brown colour and a cookie factor ratio of 4.5 ± 0.6. Storage studies indicated that after 3 weeks at room temperature cookies presented slight variations in the texture. Simulated gastrointestinal digestion of this product was able to release peptides capable of exerting potential antithrombotic and antihypertensive activities, IC₅₀ values of 0.22 ± 0.04 and 0.23 ± 0.03 mg mL⁻¹ protein, respectively. This work demonstrates for the first time that food made with amaranth flour exerts potential antithrombotic and antihypertensive activity. In conclusion, these amaranth cookies could be an alternative way of incorporating potentially health beneficial products for people who choose a conscious diet, including coeliac or vegan consumers.     

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...