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Background: Latinx persons are overrepresented in terms of 2019 Coronavirus (COVID-19) infection rates and constitute a subpopulation at increased risk for COVID-19 related physical ailments. Fatigue and pain are among the most prevalent somatic symptoms among the Latinx population; however, there is little understanding of individual difference factors that are related to fatigue and pain during COVID-19 among this health disparities population. Experiential avoidance (EA) reflects the persistent tendency to avoid aversive internal sensations. Methods: The current study sought to extend past work by exploring EA in relation to fatigue severity, pain intensity, and pain disability among 182 Latinx adult persons during the COVID-19 pandemic. Results: Results indicated that EA accounted for a statistically significant amount of variance across the criterion variables. Conclusions: Overall, the current work provides initial empirical evidence that EA is related to greater fatigue severity and pain severity/disability among Latinx persons during COVID-19. [ABSTRACT FROM AUTHOR]

Background: The outbreak of the novel 2019 SARS2-Coronavirus disease (COVID-19) has led to substantial psychological distress across the United States and the world. However, emerging work suggests that prominent COVID-19 consequences such as fear of COVID-19 infection and emotional distress resulting from economic adversity and social distancing may afflict the Latinx population disproportionately. The current study sought to investigate the influence of experiential avoidance (EA), or an individual's tendency to rigidly avoid negative internal experiences (i.e. feelings, thoughts, etc.), on the severity of COVID-19 fear and emotional distress symptoms due to economic adversity and social distancing. Method: Participants included 188 Latinx persons recruited via an online survey panel program. Results: Results indicated that EA was a statistically significant contributing factor to fear of COVID-19 and emotional distress associated with economic adversity and social distancing. These effects were evident over and above the variance accounted for by gender, years living in the United States, education, and COVID-19 related work and home life stress. Conclusions: Overall, the present work is the first to document empirically EA as it relates to fear of COVID-19 infection and related emotional distress resulting from economic adversity and social distancing among Latinx persons. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the cardiovascular disease risk of patients with uterine leiomyoma using carotid intima-media thickness (CIMT), insulin resistance and lipid profile. Methods: A total of 84 reproductive-aged women (20-45 years) were divided into two groups as the study group ( n = 42) of women with leiomyoma and the control group ( n = 42) of healthy women without leiomyoma. The risk factors for cardiovascular disease such as age, body mass index (BMI), low-density lipoprotein (LDL), triglycerides (Tg), high-density lipoprotein (HDL), total cholesterol and lipoprotein a levels, systemic disorders, cigarette smoking and CIMT were all recorded and compared between groups. Results: The mean age, gravida, parity, BMI, total cholesterol level and CIMT were significantly different between the groups ( p < 0.05). CIMT after adjusted for the age and age, BMI was significantly lower in study groups (<0.001). Insulin resistance, serum LDL, Tg, HDL, lipoprotein a, and endometrial thickness were comparable between groups ( p > 0.05). The number of leiomyoma was significantly correlated with Tg ( r = 0. 322, p = 0.037) and LDL ( r = 0. 431, p = 0.006). The size of leiomyoma was significantly correlated with HOMA-IR values ( r = −0.370, p = 0.016). Conclusion: Uterine leiomyoma was associated with lipid profile, insulin resistance and CIMT in reproductive-aged women. [ABSTRACT FROM AUTHOR]

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In this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN + AlO + TiN and AlO), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force ( F) and the feed force ( F) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for F, the optimal results for F were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability. [ABSTRACT FROM AUTHOR]

Developing low cost and energy-saving methods for manufacturing ceramics is always desirable and is now one of the hottest and most challenging research topics in the ceramics industry. In addition to saving energy, reduction of CO2 emissions is also a priority. Addressing these needs makes development of new processing and manufacturing techniques a necessity and also stimulates a search for new raw material alternatives. Geopolymers are non-crystalline aluminosilicate polymers that are formed by mixing alkali-silicate solutions with reactive aluminosilicate materials under normal temperature (RT - 120 °C) conditions. Since geopolymers were introduced, there has been considerable research in new application areas. Traditional ceramic tiles are produced at temperatures of 1,100-1,200 °C. Applying geopolymer technology to ceramic tile manufacturing saves substantial energy cost and reduces CO2 emissions. [ABSTRACT FROM AUTHOR]

Low alloy steel specimens were subjected to erosion-oxidation in a simulated fluidized bed environment in the range 100–600° C. Transmission electron microscopy was carried out on the surfaces which experienced particle impacts, with specimens prepared by backthinning. At low temperatures (≤ 200° C) a dense, protective layer of bed particle fragments formed on the steel surface due to fragmentation of particle asperities and their subsequent comminution to a very fine size (< 10 nm). At intermediate temperatures thin oxide films developed on the wear scar surfaces; these were predominantly magnetite with a fine grain size. There was rapid material loss with particle impacts removing the oxide and some metal below, but the thin oxide rapidly regrew due to the fine grain size, absence of a haematite layer and the mechanical damage during particle impact. At high temperatures the oxide became sufficiently thick to be mechanically protective. Erosion occurred within the fine grained surface haematite layer, while there was grain growth in the lower magnetite layer. [ABSTRACT FROM AUTHOR]