Восходящий поток с переменным гидродинамическим режимом применительно к переработке кислых никельсодержащих растворов

По мере выработки месторождений происходит снижение среднего содержания никеля в сырье, что ведет к увеличению себестоимости товарного никеля. Особенно остро это отражается на предприятиях Урала, работающих на окисленных никелевых рудах, где среднее содержание никеля в рудах на уровне 1 %. Окисленные никелевые руды считаются необогатимыми, поэтому переработка их весьма затратная и сильно зависит от рыночной стоимости кокса и стоимости товарного никеля. Поэтому при снижении рыночной стоимости никеля предприятия Урала по переработки окисленных никелевых руд работают в убыток или вынуждены временно приостанавливать производство. Для удовлетворения спроса никеля необходим переход к более совершенному использованию вторичного сырья и техногенных образований. Более рационален способ переработки вторичного сырья с возвратом в туже отрасль где и образовался отход производства. Т.е. сплавы и стали перерабатывать пирометаллургическими способами с получением компактного никеля или ферроникеля с возвратом в производство сталей и сплавов. А техногенные образования, представленные химическими соединениями никеля, карбонаты, гидроксиды, сульфаты и др., перерабатывать гидрометаллургическими методами с получением востребованных продуктов.Программа развития УрФУ на 2013 год (п.1.2.2.3

Problem of buildup of literary scholar's scientific sense

© Medwell Journals, 2015. The study deals with different aspects of buildup of literary scholar' scientific sense and demonstrates this problem with the help of several examples. The researchers reveal connections between a scientist and a surrounding context in different form of its expression. The researchers analyze factors influencing, among other things, modern perception of scientific viewpoint of a literary scholar. At first, the authors study general regularities of scientist's world view buildup. They single out several concrete factors, among which there are historical context, literary studies school influence, national specificity, peculiarities of a scientist's personality (e.g., religiosity, education etc.). Particularly, they present how literary studies as a field of science depend on a specific era's ideology. The researchers provide examples of such interaction both in XIX-XX centuries and today. They postulate sequence and repetitiveness of general principles of development of this dependence

Machine Learning for Understanding and Predicting Injuries in Football

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data

IMPROVING THE TEACHING OF SCIENTIFIC CONCEPTS ABOUT THE LINE IN INTERDISCIPLINARY COMMUNICATION IN THE PROCESS OF PREPARING FUTURE MATHEMATICS TEACHERS

The article describes the experience of improving the teaching of linear concepts, which are important concepts of mathematics for future teachers of mathematics, based on the principles of scientificness and historiography in the teaching of interdisciplinary sciences in geometry and mathematical analysis

Female motorsport fan engagement on social media-based brand communities

This study explored the engagement of female motorsport fans within F1 team brand communities on Twitter (now ‘X’). Specifically, we sought to investigate why and how female fans were engaging with social media-based brand communities managed by F1 motorsports teams, and to gain better insights into the factors that encourage or deter female fan engagement within such communities. Our research methods combined online surveys and content analysis of Twitter posts. The investigation revealed that female fans seemed hesitant to engage actively within motorsports team brand accounts due to a fear of receiving negative reactions to their comments. The findings also identified differences between the participation activities of female and male fans, as well as interest in different content categories. This study recommends that motorsports team brand community managers or social media managers give greater consideration to the well-being of female fans interacting on their social media platforms and communities. They also need to be aware of gender-based differences in engagement as well as the specific issues faced by female publics

Data-driven Automatic Attribution of Azerbaijani Flat Woven Carpets

Carpet attribution is an important task for studying the carpets and textiles, and more generally the history and culture of the communities producing these carpets. However, this is not an easy task, often relying on experts' subjective opinion or complex chemical or radiographical analysis, often not available to many practitioners. In this work, building on the success of applying machine learning and artificial intelligence methods in different fields, we present another, data-driven approach for carpet attribution. Based on a large dataset of Azerbaijani flat woven carpets we have developed a novel machine learning based data-driven carpet attribution system, which successfully determines their types, schools and weaving century, achieving up to 98% accuracy of the attribution

A multi-season machine learning approach to examine the training load and injury relationship in professional soccer

OBJECTIVES: The purpose of this study was to use machine learning to examine the relationship between training load and soccer injury with a multi-season dataset from one English Premier League club. METHODS: Participants were 35 male professional soccer players (aged 25.79±3.75 years, range 18–37 years; height 1.80±0.07 m, range 1.63–1.95 m; weight 80.70±6.78 kg, range 66.03–93.70 kg), with data collected from the 2014–2015 season until the 2018–2019 season. A total of 106 training loads variables (40 GPS data, 6 personal information, 14 physical data, 4 psychological data and 14 ACWR, 14 MSWR and 14 EWMA data) were examined in relation to 133 non-contact injuries, with a high imbalance ratio of 0.013. RESULTS: XGBoost and Artificial Neural Network were implemented to train the machine learning models using four and a half seasons’ data, with the developed models subsequently tested on the following half season’s data. During the first four and a half seasons, there were 341 injuries; during the next half season there were 37 injuries. To interpret and visualize the output of each model and the contribution of each feature (i.e., training load) towards the model, we used the Shapley Additive Explanations (SHAP) approach. Of 37 injuries, XGBoost correctly predicted 26 injuries, with recall and precision of 73% and 10% respectively. Artificial Neural Network correctly predicted 28 injuries, with recall and precision of 77% and 13% respectively. In the model using Artificial Neural Network (the relatively more accurate model), last injury area and weight appeared to be the most important features contributing to the prediction of injury. CONCLUSIONS: This was the first study of its kind to use Artificial Neural Network and a multi-season dataset for injury prediction. Our results demonstrate the potential to predict injuries with high recall, thereby identifying most of the injury cases, albeit, due to high class imbalance, precision suffered. This approach to using machine learning provides potentially valuable insights for soccer organizations and practitioners when monitoring load injuries

Spin exchange between charged paramagnetic particles in dilute solutions

© Springer-Verlag Wien 2014. Kinetic equations for the spin density matrix which take into account binary collisions and a method of calculating the spin exchange effective radius have been generalized to the case of dilute solutions of charged paramagnetic particles. The effective radius of the spin exchange and rate constant of the bimolecular spin exchange between charged paramagnetic particles in solutions have been calculated numerically. Calculations have been performed under the assumption that the exchange interaction is isotropic and decays exponentially with the increase in the distance between radicals, and the solution has a given dielectric permittivity and Debye screening radius. Dependences of the spin exchange rate constant on the mutual diffusion coefficient, exchange and electrostatic interactions parameters have been found numerically. The theory has been applied to experimental results taken from the literature. The rate constant of the spin exchange between radicals of like charge found from the experiment and calculated within the developed theory are in good qualitative agreement

Solid-state 31P and 109Ag CP/MAS NMR as a powerful tool for studying of silver(I) complexes with N-thiophosphorylated thiourea and thioamide ligands

A family of three- and four-coordinated silver(I) complexes of formulas [Ag(PPh3)2L], [Ag(PPh3)L], and [AgL]n with N-thiophosphorylated thiourea and thioamide ligands of general formula RC(S)NHP(S)(OPri)2 [R = Ph, PhNH, iPrNH, tBuNH, NH2] have been studied by solid-state 109Ag and 31P CPMAS NMR spectroscopy. 109Ag NMR spectra have provided valuable structural information about Ag coordination, which is in good accordance with the available crystal structure data. The data presented in this work represent a significant addition to the available 109Ag chemical shifts and chemical shifts anisotropies. The silver chemical shift ranges for different P,S-environments and coordination state were discussed in detail. The 1J(31P–107/109Ag) and 2J(31P–31P) values were determined and analyzed. © 2022 John Wiley & Sons Ltd.Kazan Federal University: Priority-2030Financial support from the IR-RMN-THC Fr3050 CNRS for conducting the research is gratefully acknowledged. This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program (Priority-2030).Financial support from the IR-RMN-THC Fr3050 CNRS for conducting the research is gratefully acknowledged. This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program (Priority-2030)

Nutrient Deficiency Correction in Ovarian Cancer Patients Following Surgical Treatment: a Clinical Case

Background. According to some studies, nutrient deficiencies reach an over-70% prevalence in ovarian cancer, among other gynaecological malignancies, thus constituting an important risk factor for postoperative mortality, surgical complications and longer hospital stays. Therefore, effective nutrient deficiency correction methods are warranted to improve the ovarian cancer outcomes, especially in patients following radical surgical interventions. New systematic evidence emerges in literature on the impact of such novel methods on the critical status of variant-category patients. Meanwhile, such evidence bears a recommendatory value only, with no current standard or protocol assumed for nutrient deficiency management. This issue presently remains open and requires careful research and analysis.Materials and methods. The clinical case demonstrates the efficacy of nutrient deficiency correction in an ovarian cancer patient following an individualised radical surgery.Results and discussion. The energy supplied on day 1 was >42%, >83% on day 3, and the target values had been achieved by day 7 of intensive therapy. The nutrient deficiency marker dynamics revealed the growth of transferrin, triglycerides and peripheral blood lymphocyte counts as early as by day 3 post-surgery. Albumin was the latest to respond, increasing only on day 7.Conclusion. The introduction of novel nutrition strategies and knowledge of their impact depend on further high-quality research, especially prospective studies, incorporating a  greater homogeneity of intervention types and clinical outcomes, as well as wider sampling of female ovarian cancer