Intersectionality

Women continue to be underrepresented as P-12 school administrators, and this marginalization is more conspicuous in Appalachian Kentucky public schools. This chapter presents a review of extant scholarship on the intersectionality of the focus population as women, educational leaders, and residents of Appalachia Kentucky. The critical consciousness of administrators was examined in both male and female participants. Personal and school predictor variables served as additional variables in the prediction model to better understand the context of the participants. Comparisons of means and multiple regression analysis were utilized to potentially create predictive equation of social justice leadership propensity of school administrators and to determine differences between gender and if personal and school predictor variables had any effect on the critical consciousness of the sample. This chapter reveals the importance of context, intersectionality, and need for more inclusive quantitative instruments for the study of social justice leadership.

Recycled Concrete Aggregate for Medium-Quality Structural Concrete

This paper reports an evaluation of the properties of medium-quality concrete incorporating recycled coarse aggregate (RCA). Concrete specimens were prepared with various percentages of the RCA (25%, 50%, 75%, and 100%). The workability, mechanical properties, and durability in terms of abrasion of cured concrete were examined at different ages. The results reveal insignificant differences between the recycled concrete (RC) and reference concrete in terms of the mechanical and durability-related measurements. Meanwhile, the workability of the RC reduced vastly since the replacement of the RCA reached 75% and 100%. The ultrasound pulse velocity (UPV) results greatly depend on the porosity of concrete and the RC exhibited higher porosity than that of the reference concrete, particularly at the transition zone between the RCA and the new paste. Therefore, the sound transmission in the RC required longer times than that in the reference concrete. Moreover, a predictive equation relating the compressive strength to the UPV was developed.

A Combined Flow Cytometric Semen Analysis and miRNA Profiling as a Tool to Discriminate Between High- and Low-Fertility Bulls

Predicting bull fertility is one of the main challenges for the dairy breeding industry and artificial insemination (AI) centers. Semen evaluation performed in the AI center is not fully reliable to determine the level of bull fertility. Spermatozoa are rich in active miRNA. Specific sperm-borne miRNAs can be linked to fertility. The aim of our study is to propose a combined flow cytometric analysis and miRNA profiling of semen bulls with different fertility to identify markers that can be potentially used for the prediction of field fertility. Sperm functions were analyzed in frozen-thawed semen doses (CG: control group) and high-quality sperm (HQS) fraction collected from bulls with different field fertility levels (estimated relative conception rate or ERCR) by using advanced techniques, such as the computer-assisted semen analysis system, flow cytometry, and small RNA-sequencing. Fertility groups differ for total and progressive motility and in the abnormality degree of the chromatin structure (P < 0.05). A backward, stepwise, multiple regression analysis was applied to define a model with high relation between in vivo (e.g., ERCR) and in vitro (i.e., semen quality and DE-miRNA) fertility data. The analysis produced two models that accounted for more than 78% of the variation of ERCR (CG: R2 = 0.88; HQS: R2 = 0.78), identifying a suitable combination of parameters useful to predict bull fertility. The predictive equation on CG samples included eight variables: four kinetic parameters and four DNA integrity indicators. For the HQS fraction, the predictive equation included five variables: three kinetic parameters and two DNA integrity indicators. A significant relationship was observed between real and predicted fertility in CG (R2 = 0.88) and HQS fraction (R2 = 0.82). We identified 15 differentially expressed miRNAs between high- and low-fertility bulls, nine of which are known (miR-2285n, miR-378, miR-423-3p, miR-191, miR-2904, miR-378c, miR-431, miR-486, miR-2478) while the remaining are novel. The multidimensional preference analysis model partially separates bulls according to their fertility, clustering three semen quality variable groups relative to motility, DNA integrity, and viability. A positive association between field fertility, semen quality parameters, and specific miRNAs was revealed. The integrated approach could provide a model for bull selection in AI centers, increasing the reproductive efficiency of livestock.

Semi-Empirical Prediction of Turned Surface Residual Stress for Inconel 718 Grounded in Experiments and Finite Element Simulations

The surface residual stress after machining, especially for finishing, has a vital influence on the shape stability and fatigue life of components. The current study focuses on proposing an original empirical equation to predict turned surface residual stress for Inconel 718 material, taking tool parameters into consideration. The tool cutting-edge angle, rake angle, and inclination angle are introduced for the first time in the equation based on the Inconel 718 material turning experiments and finite element simulations. In this study, the reliability of simulation parameters’ setting is firstly calibrated by comparing the residual stresses and chips of the experiments and simulations. The changing trends of turned forces, temperatures of lathe tool nose, and surface residual stress with turning parameters are analyzed. Then, the predictive equation of surface residual stress is proposed considering relationships between the back-rake angle, the side-rake angle, and the tool cutting-edge angle, rake angle, and inclination angle. Moreover, the genetic algorithm optimizes the objective function to obtain the undetermined coefficients in the prediction equation. Finally, the predicted accuracy of the surface residual stress is proven by comparing the experimental, simulation, and prediction values. The results indicate that the predictive equation of surface residual stress has a good accuracy in predicting turned surface residual stress for Inconel 718 materials. The correlation coefficient, R, and absolute average error between the predicted and the simulated value are 0.9624 and 13.40%, respectively. In the range of cutting parameters studied and experimental errors, this study provides an accurate predictive equation of turned surface residual stress for Inconel 718 materials.

Prediction of optimal continuous positive airway pressure in Thai patients with obstructive sleep apnea

Continuous positive airway pressure (CPAP) is simple and effective treatment for obstructive sleep apnea (OSA) patients. However, the CPAP prediction equation in each country is different. This study aimed to predict CPAP in Thai patients with OSA. A retrospective study was conducted in Thai patients, who OSA was confirmed by polysomnography and CPAP titration from January 2015 to December 2018. Demographics, body mass index (BMI), neck circumference (NC), Epworth sleepiness scale, apnea–hypopnea index (AHI), respiratory disturbance index (RDI), mean and lowest pulse oxygen saturation (SpO2), and optimal pressure were recorded. A total of 180 subjects were included: 72.8% men, age 48.7 ± 12.7 years, BMI 31.0 ± 6.3 kg/m2, NC 40.7 ± 4.1 cm, AHI 42.5 ± 33.0 per hour, RDI 47.1 ± 32.8 per hour, and lowest SpO2 77.1 ± 11.0%. Multiple linear regression analysis identified NC, BMI, RDI, and lowest SpO2. A final CPAP predictive equation was: optimal CPAP (cmH2O) = 4.614 + (0.173 × NC) + (0.067 × BMI) + (0.030 × RDI) − (0.076 × lowest SpO2). This model accounted for 50.0% of the variance in the optimal pressure (R2 = 0.50). In conclusion, a CPAP prediction equation can be used to explain a moderate proportion of the titrated CPAP in Thai patients with OSA. However, the CPAP predictive equation in each country may be different due to differences of ethnicity and physiology.Trial registration: TCTR20200108003.

Performance of Bioelectrical Impedance and Anthropometric Predictive Equations for Estimation of Muscle Mass in Chronic Kidney Disease Patients

Background: Patients with chronic kidney disease (CKD) are vulnerable to loss of muscle mass due to several metabolic alterations derived from the uremic syndrome. Reference methods for body composition evaluation are usually unfeasible in clinical settings.Aims: To evaluate the accuracy of predictive equations based on bioelectrical impedance analyses (BIA) and anthropometry parameters for estimating fat free mass (FFM) and appendicular FFM (AFFM), compared to dual energy X-ray absorptiometry (DXA), in CKD patients.Methods: We performed a longitudinal study with patients in non-dialysis-dependent, hemodialysis, peritoneal dialysis and kidney transplant treatment. FFM and AFFM were evaluated by DXA, BIA (Sergi, Kyle, Janssen and MacDonald equations) and anthropometry (Hume, Lee, Tian, and Noori equations). Low muscle mass was diagnosed by DXA analysis. Intra-class correlation coefficient (ICC), Bland-Altman graphic and multiple regression analysis were used to evaluate equation accuracy, linear regression analysis to evaluate bias, and ROC curve analysis and kappa for reproducibility.Results: In total sample and in each CKD group, the predictive equation with the best accuracy was AFFMSergi (men, n = 137: ICC = 0.91, 95% CI = 0.79–0.96, bias = 1.11 kg; women, n = 129: ICC = 0.94, 95% CI = 0.92–0.96, bias = −0.28 kg). AFFMSergi also presented the best performance for low muscle mass diagnosis (men, kappa = 0.68, AUC = 0.83; women, kappa = 0.65, AUC = 0.85). Bias between AFFMSergi and AFFMDXA was mainly affected by total body water and fat mass. None of the predictive equations was able to accurately predict changes in AFFM and FFM, with all ICC lower than 0.5.Conclusion: The predictive equation with the best performance to asses muscle mass in CKD patients was AFFMSergi, including evaluation of low muscle mass diagnosis. However, assessment of changes in body composition was biased, mainly due to variations in fluid status together with adiposity, limiting its applicability for longitudinal evaluations.