Best-fit Regression Model Research Articles

Uroflowmetry: nomograms in healthy young Pakistani men.

To create a nomogram based on urine volume and flow of males without lower urinary tract symptoms. The prospective, cross-sectional study was conducted at the Department of Urological Surgery and Transplantation, Jinnah Postgraduate Medical Centre, Karachi, from November 1, 2020, to October 31, 2022, and comprised healthy young males without lower urinary tract dysfunction who were recruited from the hospital as well as a large textile mill. They were asked to void on their normal desire. Uroflowmetry was done to determine maximum flow rate, average flow rate, and void volume values. A best-fit regression model was used to formulate uroflowmetry nomogram using average and maximum urine flow rate over voided volume. The sample size was calculated using PASS 2020 Power Analysis and Sample Size Software (2020). NCSS, LLC. Kaysville, Utah, USA. The database was developed on NCSS 2020 Statistical Software (2020). NCSS, LLC. Kaysville, Utah, USA for the data analysis. Of the 468 male subjects enrolled, data was analysed related to 432(92.3%). The mean age was 25.59±4.32 years. Mean maximum flow rate, average flow rate and void volume were 25.28±8.70mL/s, 14.77±4.79mL/s and 405.48±163.86mL, respectively. The association of age was noted with maximum flow rate (r=0.1435, p=0.004), average flow rate (r=0.1135, p=0.004) and void volume (r=0.0619, p=0.004). The best-fitted model for maximum and average flow rate was subsequently developed which was statistically significant (p<0.05). The nomograms developed could reliably predict the maximal flow rate in young Pakistani men.

Standardized bone marrow assessment, risk variables, and survival in dogs with myelodysplastic syndrome and acute myeloid leukemia.

Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are heterogeneous neoplasms of hematopoietic stem cells that are challenging to diagnose, differentiate, and prognosticate. Cytogenetic and mutational analyses are useful in humans but unavailable for dogs, where diagnosis and classification still rely largely on hematologic and morphologic assessment. The objectives of this study were to apply a classification scheme to myeloid neoplasms and to assess outcome in relation to predictor variables. Keyword search of a laboratory database, application of sequential exclusion criteria, and consensus from 3 reviewers yielded 70 cases of myeloid neoplasia with hematology results, and cytologic (11), histologic (14), or both (45) types of marrow specimens. Based on blast percentage and morphology, 42 cases were classified as MDS and 28 as AML. Dogs with MDS had significantly lower body weights, hemoglobin concentrations and blood blasts, and higher red blood cell size variability and platelet numbers than dogs with AML. Estimates of median survival using Kaplan-Meier curves for dogs with MDS and AML were 384 and 6 days, respectively (P < .001). The instantaneous risk of death for dogs with MDS was approximately 5× lower than that of dogs with AML. Significant predictor variables of survival were body weight, white blood cell count, platelet count, and percent blood blasts (P < .05). Hazard ratios (HRs) derived from best-fitting Cox regression models were 1.043, 0.998, and 1.061 for increased neutrophils, decreased platelets, and increased blood blasts, respectively. Findings from this study suggest that hematologic and morphologic variables are useful to predict outcomes in myeloid neoplasia.

Age-dependencies of the electroretinogram in healthy subjects.

The purpose of this study was to evaluate the age-dependency of amplitudes and implicit times in the electroretinograms (ERGs) of healthy individuals and provide clinicians and researchers with a reference for a variety of stimulus paradigms. Full-field electroretinography was conducted on 73 healthy participants aged 14-73 using an extended ISCEV standard protocol that included an additional 9Hz flicker stimulus for assessing rod function and special paradigms for isolated On-Off and S-cone responses. Correlation coefficients and best-fit regression models for each parameter's age-dependency were calculated. Dark-adapted ERGs, in particular, displayed notable age-related alterations. The attenuation and delay of the b-wave with higher age were most significant in the dark-adapted, rod-driven 0.001cds/m2 flash ERG. The age-dependent reduction of the a-wave amplitude was strongest in the standard dark-adapted 3cds/m2 flash condition. Cone-driven, light-adapted responses to either flash or flicker stimuli displayed comparatively small alterations at higher age. S-cone function tended to diminish at an early age, but the effect was not significant in the whole population. The results suggest that rod and cone function decline at different rates with age, with rods being generally more affected by aging. Nonetheless, response amplitudes displayed a wide variability across the whole sample.

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

Reference Ranges of 2-Dimensional Placental Biometry and 3-Dimensional Placental Volume between 11 and 14 Weeks of Gestation.

The purpose of this study was to provide gestational age (GA) specific reference ranges for 2-dimensional (2D) placental biometry and 3-dimensional (3D) placental volume between 11 and 14 weeks of gestation. Placental biometry including 2D and 3D variables was calculated in 1142 first-trimester singleton pregnancies with non-complicated outcome between September 2016 and February 2020. Ultrasound datasets were obtained at the time of the first-trimester ultrasound, and 2D basal plate (BP), chorionic plate (CP), placental thickness (PT), and 3D placental volume (PV) were measured following a standardized methodology. Reference ranges for each variable were calculated according to GA and crown-rump-length (CRL). A total of 1142 uncomplicated pregnancies were considered for analysis. All placental measurements increased significantly between 11 and 14 weeks, especially for PT (39.64%) and PV (64.4%). Reference ranges were constructed for each 2D and 3D first-trimester placental variable using the best-fit regression model for the predicted mean and SD as a function of GA and CRL. Reference ranges of 2D placental biometry and 3D placental volume between 11 and 14 weeks of gestation were constructed, generating reference values. Placental biometry showed a progressive increase during the first trimester. This highlights the importance of using reference range charts according to GA.

Poster 229: Intra-articular VEGF and MMP-1 Are the Primary Drivers of Worse Baseline KOOS Symptoms and Quality of Life Subscores at Time of Knee Chondroplasty

Objectives: Biomarkers are a topic of interest in orthopaedics in the setting of osteoarthritis and patients undergoing knee arthroscopy for any reason. However, in patients undergoing knee arthroscopy for chondral defects, the influence of cytokines on patient pain and function is not fully understood. The purpose of this study is to investigate the concentrations of synovial inflammatory cytokines in patients undergoing arthroscopic chondroplasty for chondral defects in the knee and correlate those cytokine levels with baseline patient reported outcome measures (PROs) and defect characteristics. We hypothesized that the synovial cytokine environment will correlate with patient symptoms more so than baseline defect characteristics. Methods: Sixty patients 18-50 years old undergoing arthroscopic chondroplasty for knee cartilage defects were enrolled. Patients were assigned preoperative Knee injury and Osteoarthritis Outcome Score (KOOS) and International Knee International Knee Documentation Committee (IKDC) Subjective Knee Forms. Preoperative magnetic resonance images were used to calculate AMADEUS (Area Measurement And Depth Underlying Structure) scores. All patients received a successful synovial fluid aspiration just prior to initiation the arthroscopic procedure. The number of defects, total defect area, and ICRS grades were recorded based on intraoperative assessment. Patients with concomitant procedures beyond partial meniscectomy were excluded. Multiplex ELISA analyzed aspirations for: PDGF-BB, CCL-5/RANTES, MMP-3, MMP-1, EGF, VEGF, IL-1a, FGF-2, CCL-2, BMP-2, and aggrecan. Univariate correlation testing was used to assess significance between cartilage defect characteristics, PROs, and cytokine concentrations (Figure 1). The Akaike Information Criterion (AIC) was utilized to select the best-fit multivariate regression model using the 4 most significant independent variables for each cytokine (Figure 2). AIC best-fit modeling was repeated for each PRO that had at least two significant cytokine associations on univariate testing (Figure 3). Significance was set at P&lt;0.05. Results: MMP-1 had a positive correlation with number of defects treated ( P=0.016) and negative correlation with KOOS quality of life (QOL) subscores ( P=0.035; R2 =0.173). VEGF was positively correlated with defects treated ( P=0.005) and negatively correlated with KOOS Symptoms scores ( P=0.035; R2 =0.225). The treatment of multiple defects was an independent predictor of elevated IL-1a ( P=0.002, R2 =0.202). CCL-2 was positively correlated with multiple defects ( P=0.012) and negatively with KOOS QOL ( P=0.016, R2 =0.173). Female sex was correlated with higher concentrations of MMP-3 ( P=0.007, R2 =0.144), FGF ( P=0.012, R2 =0.178), and BMP-2 ( P=0.008; R2 = 0.169). BMP-2 was negatively correlated with KOOS Symptom ( P=0.019). The primary driver of preoperative KOOS Symptoms scores on multivariate analysis was VEGF ( P=0.023, R2 =0.120), influencing patient symptoms beyond defect characteristics. Similarly, KOOS QOL was independently correlated with MMP-1 concentration ( P= 0.045, R2 =0.079), more so than ICRS grade (Figure 3). Conclusions: Multivariate regression analysis revealed that elevated MMP-1 was the primary driver of worse preoperative KOOS QOL scores, more so than defect characteristics such as the number of lesions treated. Similarly, worse preoperative KOOS Symptoms scores were more strongly correlated with elevated VEGF concentrations rather than defect ICRS grades. Our study demonstrated that synovial fluid cytokines appear to be the primary drivers of patients’ subjective assessments of their pain and function preoperatively.

Factors influencing the development of artificial intelligence in orthodontics.

Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.

Intra-articular VEGF and MMP-1 are the Primary Drivers of Worse Baseline KOOS Symptoms and Quality of Life Subscores at Time of Knee Chondroplasty

Objectives: Biomarkers are a topic of interest in orthopaedics in the setting of osteoarthritis and patients undergoing knee arthroscopy for any reason. However, in patients undergoing knee arthroscopy for chondral defects, the influence of cytokines on patient pain and function is not fully understood. The purpose of this study is to investigate the concentrations of synovial inflammatory cytokines in patients undergoing arthroscopic chondroplasty for chondral defects in the knee and correlate those cytokine levels with baseline patient reported outcome measures (PRO’s) and defect characteristics. We hypothesized that the synovial cytokine environment will correlate with patient symptoms more so than baseline defect characteristics. Methods: Sixty patients 18-50 years old undergoing arthroscopic chondroplasty for knee cartilage defects were enrolled. Patients were assigned preoperative Knee injury and Osteoarthritis Outcome Score (KOOS) and International Knee International Knee Documentation Committee (IKDC) Subjective Knee Forms. Preoperative magnetic resonance images were used to calculate AMADEUS (Area Measurement And Depth Underlying Structure) scores.1 All patients received a successful synovial fluid aspiration just prior to initiation the arthroscopic procedure. The number of defects, total defect area, and ICRS grades were recorded based on intraoperative assessment. Patients with concomitant procedures beyond partial meniscectomy were excluded. Multiplex ELISA analyzed aspirations for: PDGF-BB, CCL-5/RANTES, MMP-3, MMP-1, EGF, VEGF, IL-1a, FGF-2, CCL-2, BMP-2, and aggrecan. Univariate correlation testing was used to assess significance between cartilage defect characteristics, PROs, and cytokine concentrations (Figure 1). The Akaike Information Criterion (AIC) was utilized to select the best-fit multivariate regression model using the 4 most significant independent variables for each cytokine (Figure 2). AIC best-fit modeling was repeated for each PRO that had at least two significant cytokine associations on univariate testing (Figure 3). Significance was set at P&lt;0.05. Results: MMP-1 had a positive correlation with number of defects treated (P=0.016) and negative correlation with KOOS quality of life (QOL) subscores (P=0.035; R2 =0.173). VEGF was positively correlated with defects treated (P=0.005) and negatively correlated with KOOS Symptoms scores (P=0.035; R2 =0.225). The treatment of multiple defects was an independent predictor of elevated IL-1a (P=0.002, R2 =0.202). CCL-2 was positively correlated with multiple defects (P=0.012) and negatively with KOOS QOL (P=0.016, R2 =0.173). Female sex was correlated with higher concentrations of MMP-3 (P=0.007, R2 =0.144), FGF (P=0.012, R2 =0.178), and BMP-2 (P=0.008; R2 = 0.169). BMP-2 was negatively correlated with KOOS Symptom (P=0.019). The primary driver of preoperative KOOS Symptoms scores on multivariate analysis was VEGF (P=0.023, R2 =0.120), influencing patient symptoms beyond defect characteristics. Similarly, KOOS QOL was independently correlated with MMP-1 concentration (P= 0.045, R2 =0.079), more so than ICRS grade (Figure 3). Conclusions: Multivariate regression analysis revealed that elevated MMP-1 was the primary driver of worse preoperative KOOS QOL scores, more so than defect characteristics such as the number of lesions treated. Similarly, worse preoperative KOOS Symptoms scores were more strongly correlated with elevated VEGF concentrations rather than defect ICRS grades. Our study demonstrated that synovial fluid cytokines appear to be the primary drivers of patients’ subjective assessments of their pain and function preoperatively.

A Regression Model of Dry Bulk Carriers’ Loading Time

Abstract Although optimization of Vessel Turnaround Time (VTT) is a well-known research problem and its importance has long been understood, research on dry bulk carriers VTT has no priority in the available literature. It is one of the initial motives of the author to write this paper. After a general theoretical introduction, this paper presents research on dry bulk carriers’ loading time, as a component with the dominant share in the total VTT. Through the research, a mathematical model of interdependencies between dry bulk carrier‘s loading time and selected independent variables is defined using a multiple linear regression model. Results of statistical significance tests confirmed that the best-fit regression model is the one which adequately describes the correlation between the dry bulk carrier‘s loading time and the following independent variables: cargo quantity (loaded), number of used cranes per vessel and loading process interruptions. The presented results establish important bases for the author’s further research in this field as well as reliable planning bases for cargo handling management processes at dry bulk cargo terminals where cargo loading/unloading to/from vessels is realized by gantry cranes and/or mobile harbour cranes.

Phenology, phyllochron and productivity of sorghum in response to varying growing environments and nitrogen levels in the semiarid irrigated condition

Climate change is likely to affect agricultural production in many parts of the world. Sorghum is the stable food crop in semiarid areas, and climate change has significant adverse effects on sorghum yields. The optimum time of sowing is one of the important technologies that allow the crop to better utilise natural resources by the crop to maximise productivity. For optimizing the sowing time, the field investigation was carried out in Tamil Nadu Agricultural University, Coimbatore, under summer irrigated conditions in 2022. The experiment was laid out in a split-plot design with three replications. The main plot consists of five dates of sowing and the subplot includes different nitrogen levels. The results revealed that advanced sowing (first fortnight of April) reduced the yield by 13.73% and delayed sowing (second fortnight of May) recorded the maximum yield (4230.3kg ha-1) with higher phyllochron values. GDD manifested a significant negative relationship with a high correlation coefficient (r) value of -0.98** (p = 0.01) for days to anthesis, -0.98** (p = 0.01) for days to physiological maturity and -0.88* (p = 0.05) for phyllochron. Average temperature exhibited a significant negative correlation with days to anthesis (-0.75*), physiological maturity (-0.97**) and phyllochron (-0.89*). The best-fit regression model showed that a GDD-based model could better predict the phenology of sorghum compared to an average temperature-based model. The study indicated that understanding sorghum's phenological response and productivity level under varied dates of sowing and the nitrogen levels in semiarid environments can help determine the appropriate sowing time and optimum nitrogen level for achieving better yield in summer-irrigated sorghum.

Isa Brown yumurtlayan tavuklarda yumurta kalitesi özelliklerinin analizi ve yumurta ağırlığının tahmini

Objective: The objective of this study was to determine the best-fitted regression model for estimating egg weight of Isa Brown laying chickens using external and internal egg parameters. Materials and Method: Data collected include: egg weight (EWT), egg length (EL), egg diameter (ED), shell thickness (ST), shell weight (SW), albumen height (AH), albumen weight (AW), yolk height (YH), yolk weight (YW) and yolk length (YL). Data were subjected to statistical analysis procedures of SPSS (version 23.0.0). Egg weight was regressed on external and internal egg parameters using forward, backward and full model regression procedures, to determine the combinations of egg parameters that explain variation in the dependent variable. Results: Results showed that the mean (±SE) of EWT, EL, ED, ST, SW, AH, AW, YH, YW and YL were 60.49±0.47, 4.23±0.03, 2.94±0.02, 1.37±0.01, 6.04±0.05, 1.20±0.01, 36.30±0.39, 1.22±0.02, 15.29±0.15 and 2.32±0.02 respectively. The models of EWT = 30.638+0.582AW+0.571YW and EWT = -14.991+8.779EL+5.493ED+3.686SW are the best fitted regression models for predicting egg weight of Isa Brown laying chickens. Conclusion: Conclusively, egg weight of Isa Brown laying chickens can be improved through selection for EL, ED, ST and SW trait

Measurement of Thermal Conductivity and Thermal Diffusivity of Porcine and Bovine Kidney Tissues at Supraphysiological Temperatures up to 93 °C.

This experimental study aimed to characterize the thermal properties of ex vivo porcine and bovine kidney tissues in steady-state heat transfer conditions in a wider thermal interval (23.2-92.8 °C) compared to previous investigations limited to 45 °C. Thermal properties, namely thermal conductivity (k) and thermal diffusivity (α), were measured in a temperature-controlled environment using a dual-needle probe connected to a commercial thermal property analyzer, using the transient hot-wire technique. The estimation of measurement uncertainty was performed along with the assessment of regression models describing the trend of measured quantities as a function of temperature to be used in simulations involving heat transfer in kidney tissue. A direct comparison of the thermal properties of the same tissue from two different species, i.e., porcine and bovine kidney tissues, with the same experimental transient hot-wire technique, was conducted to provide indications on the possible inter-species variabilities of k and α at different selected temperatures. Exponential fitting curves were selected to interpolate the measured values for both porcine and bovine kidney tissues, for both k and α. The results show that the k and α values of the tissues remained rather constant from room temperature up to the onset of water evaporation, and a more marked increase was observed afterward. Indeed, at the highest investigated temperatures, i.e., 90.0-92.8 °C, the average k values were subject to 1.2- and 1.3-fold increases, compared to their nominal values at room temperature, in porcine and bovine kidney tissue, respectively. Moreover, at 90.0-92.8 °C, 1.4- and 1.2-fold increases in the average values of α, compared to baseline values, were observed for porcine and bovine kidney tissue, respectively. No statistically significant differences were found between the thermal properties of porcine and bovine kidney tissues at the same selected tissue temperatures despite their anatomical and structural differences. The provided quantitative values and best-fit regression models can be used to enhance the accuracy of the prediction capability of numerical models of thermal therapies. Furthermore, this study may provide insights into the refinement of protocols for the realization of tissue-mimicking phantoms and the choice of tissue models for bioheat transfer studies in experimental laboratories.

Left atrial appendage structural characteristics predict thrombus formation.

Nonvalvular atrial fibrillation (NVAF) is a highly prevalent arrhythmia where loss of synchronized atrial contraction increases the risk of intracardiac thrombus particularly within the left atrial appendage (LAA). Anticoagulation is the mainstay of stroke prevention based on the CHA2 DS2 -VASc score; however, it does not account for LAA structural characteristics. The research comprises a retrospective matched case-control study of 196 subjects with NVAF who underwent transesophageal echo (TEE). The control group, without thrombus (n = 117), was selected from two different groups, both pools had: NVAF and CHA2 DS2 -VASc score ≥ 3. One group underwent screening TEE before Watchman closure device placement from January 2015 to December 2019 (n = 74) the second underwent TEE before cardioversion from February to October 2014 (n = 43). The study group, with thrombus (n = 79), included patients with NVAF, TEE study performed between February 2014 and December 2020, and LAA thrombus. The propensity score method was utilized to determine the matched controls while accounting for confounding from prognostic variables resulting in 61 matched pairs included in the analysis data set. LAA ostial area (OA) (calculated from orthogonal measurements 0°, 90° or 45°, 135°), LAA maximal depth, and peak LAA outflow velocity were measured. Patient characteristics and TEE data were collected and compared using the t test or χ2 analysis. We observed a lower LAA peak exit velocity in the thrombus group as compared to the control group. Additionally, we found that patients in the thrombus group had smaller LAA OA at 0° and 90°, at 45° and 135°, using largest diameter, as well as using aggregate OA, and smaller maximum LAA depth compared to patients in the control group. Candidate conditional logistic regression models for the outcome of the presence of thrombus were evaluated. Statistical results from the best-fitting conditional regression model were calculatedshowing a significant association between aggregate OA and LAA exit velocity with presence of thrombus. Utilizing LAA structural characteristics to predict thrombus formation may help refine current cardioembolic stroke (CES) risk estimation.

Reference Ranges and Development Patterns of Fetal Myocardial Function Using Speckle Tracking Echocardiography in Healthy Fetuses at 17 to 24 Weeks of Gestation.

The purposes of the study were to develop reference ranges and maturation patterns of fetal cardiac function parameters measured by speckle tracking echocardiography (STE) using multiple biometric variables at 17 to 24 weeks' gestation among Thai fetuses and to compare with other previous reports. The four-chamber view of the fetal heart in 79 healthy fetuses was suitably analyzed by STE to establish the best-fit regression model. The 95% reference intervals and Z-score equations of fetal cardiac function parameters were computed. The fractional area change of both ventricles, left ventricular (LV) end-diastolic and end-systolic volumes, LV stroke volume, LV cardiac output (CO), and LV CO per kilogram were all increased according to gestational age (GA) and five fetal biometric measurements. However, the global longitudinal strain, basal-apical length fractional shortening (BAL-FS), BAL annular free wall and septal wall FS, BAL free wall and septal wall annular plane systolic excursions, 24-segment transverse width FS, as well as LV ejection fraction were all independent of GA or other somatic characteristics. There were varying development patterns between fetal right and left ventricles of these cardiac function indices across the gestation period. Our study created Z-score and corresponding centile calculators, 5th and 95th centile reference tables, and corresponding graphs and determined the normal evolution across gestation using multiple somatic growth and age variables between 17 and 24 gestational weeks. These nomograms serve as an essential prerequisite for quantitatively evaluating fetal cardiac contractility and allow for precisely detecting early changes in the fetal heart function. · Most fetal cardiac function measurements were correlated with all the independent variables.. · Fetal ventricular function parameters have their own characteristic maturation changes.. · Racial variability may not occupy an important place for fetal myocardial function during these GA..

Use of Caregiving Support Services Among Diverse Dementia Caregivers by Geographic Context.

Little is known about how race/ethnicity and geographic context relate to support service use among dementia caregivers. Our objectives were to investigate (a) whether the use of at least one formal caregiving service-support groups, respite care, and training-differed by race/ethnicity and across metro and nonmetro areas; and (b) whether predisposing, enabling, and need characteristics influenced support service use by race/ethnicity. Data were analyzed from a sample of primary caregivers of care recipients aged 65 years or older with probable dementia (n = 482) in the 2017 National Health and Aging Trends Study and National Study of Caregiving. We calculated weighted prevalence estimates and then used the Hosmer-Lemeshow goodness of fit statistic to find the best-fitting logistic regression models. Among minority dementia caregivers, support service use was higher in metro than nonmetro areas (35% and 15%); the trend was reversed for non-Hispanic White caregivers (47% nonmetro and 29% metro). The best-fitting regression models included predisposing, enabling, and need factors for both minority and non-Hispanic White caregivers. Younger age and more disagreement within the family were consistently associated with more service use in both groups. Among minority caregivers, better caregiver and care recipient health were associated with using support services. Among non-Hispanic White caregivers, nonmetro geographic context and caregiving interfering with valued activities were associated with using support services. Geographic context differently affected support service use and the influence of predisposing, enabling, and need factors varied by race/ethnicity.

On computation of some open and closed neighbourhood degree sum-based topological indices for metal-insulator transition super lattice network

The properties and activities of chemical compounds can be explored by computing topological descriptors of molecular compounds. We investigate the topological aspects of the crystal structure of metal-insulator transition superlattice (GST-SL) in this study. Metal-insulator transition superlattices (GST-SL) are useful as two-dimensional (2D) transition metal dichalcogenides (TMDs) in the form of thin films. For this Superlattice Network $$SL_{\eta }$$ , we calculate open and closed neighbourhood degree sum based topological indices. The numerical and graphical representations of computed results are presented. This helps in understanding the relationship between the topological index values and the levels of the $$GST-SL_{\eta }$$ network. We also derive the expressions for entropy of the $$GST-SL_{\eta }$$ network based on four topological indices. The best-fit regression models for entropy against the four topological indices have been derived.

Modeling and optimization of performance and emission parameters of a diesel engine: A comparative evaluation between date seed oil biodiesel produced via three different heating systems

In this study, the performance of a diesel engine fueled with date seed oil (DSO) biodiesel, produced under microwave, ultrasonic and conventional heating system, is investigated by experimental and statistical methods. The multi-linear regressions (MLR) were developed using the percentage of biodiesel in fuel and the engine speeds (as independent variables) and the power, torque, and SFC and CO, CO2, HC, O2, and NOx emissions (as dependent variables). Then, the best-fitted regression model was used for analyzing the response surface of the performance parameters, and finding the optimal values of biodiesel blending percentages using genetic algorithm (GA) approaches at different engine speeds. Finally, the engine performance parameters were assessed when the engine is fueled with these optimal blending for three types of biodiesel. The results showed that technologies used in the biodiesel production would have a noticeable effect on the response of engine performance, however, the response surface of exhaust emission follows roughly the same trend with different slope and curvature. Moreover, optimization indicated that the optimal percentage of DSO biodiesel at different speeds was between 11%-%13, %13-%19, and %17-%20 for microwave, ultrasonic, and conventional production method, respectively. Subsequently, when the engine is fueled with the optimal blending of biodiesel at every engine speed, biodiesel produced via an alternative heating system can significantly improve the engine performance and reduce exhaust emissions, especially HC, CO, and NOx by microwave-assisted produced biodiesel.

Analysing the SPAD dynamics of water-stressed vs. well-watered sesame (Sesamum indicum L.) accessions and establishing their relationship with seed yield.

The chlorophyll content is susceptible to deficit moisture stress and may affect the plant yield. Leaf chlorophyll content is directly related to tolerance and higher productivity under deficit moisture stress (WS). The SPAD meter is an excellent tool for rapid analysis of crop chlorophyll content. Therefore, establishing a relationship between leaf chlorophyll content and seed yield is crucial in sesame, particularly under deficit moisture stress. Seeds of 37 sesame genotypes with checks were used in this study. These genotypes were mostly landraces, adapted to different agro-ecological zones in India. The selected genotypes were evaluated under well water (WW) and deficit moisture stress (WS) conditions. The SPAD readings were recorded ten (10) times each at every seven days intervals from the juvenile/first bud (30-35 days after sowing) to ripening/ physiological maturity (95-100 days after sowing) stage. This study aimed to identify the association between leaf SPAD readings (recorded at 7-days interval) and seed yield of sesame genotypes. The analysis of variance revealed the presence of significant variation in SPAD readings due to treatment (WW and WS), genotypes, and their interaction effects. The SPAD readings at all stages were positively correlated with seed yield in both WW and WS. High values of correlation coefficients were observed at 52 (r: 0.672) and 59 (r: 0.655) DAS under WS; whereas at 59 (r: 0.960), 66 (r: 0.972) and 73 (r: 0.974) DAS under WW at one percent significance level (p<0.01), which coincided with the mid-bloom stage of the sesame crop. The best-fit multiple regression model revealed that the dependence of sesame seed yield is significantly influenced by SPAD reading at 52 DAS under WS and 59 to 73 DAS under WW. Both these models provide a good fit with the chi-squared test, which compares the predicted and observed yield.

Experimental investigation on the fracture surface features of heat-treated red sandstone containing fissure under constant amplitude low cycle impact using 3D digital reconstruction

Fractured rocks as typical engineering materials are commonly exposed to temperatures and dynamic perturbations. In this paper, constant amplitude low cycle (CALC) impact tests were carried out on red sandstone containing a single fissure (0°, 30°, 60° and 90°) after thermal treatment (25 °C, 200 °C, 400 °C, 600 °C and 800 °C) utilizing a modified split Hopkinson pressure bar (SHPB). Subsequently, the fracture surfaces of the failure specimens were digitally reconstructed based on point cloud data captured by a three-dimensional (3D) laser scanning. And scanning electron microscopy (SEM) was also employed as a complementary tool to study the fracture micromorphology. The fractal and rough features of fracture surfaces based on the box-counting method and image segmentation method were investigated, respectively. Meanwhile, four second-order statistics in the grayscale co-occurrence matrix (GLCM) of the digitized fracture surface after grayscale processing were calculated to describe the fracture surface texture. The best-fit multiple regression models between the fracture surface feature parameters and the interaction of temperatures and fissure angles were constructed and analyzed. Moreover, the relationships between the fracture surface features and the mechanical parameters as well as the intrinsic mechanisms were analyzed and discussed in detail. The results demonstrate that the fractal dimension (DC), joint roughness coefficient (JRC), maximum elevation difference (MD), roughness characterization factor (ω), and GLCM second-order statistics possess specific response patterns to the temperatures and fissure angles. Whether the sensitivity in portraying the fracture surface features to the interaction of temperatures and fissure angles or the reliability of the best-fit multiple regression models, the shadow method based on image segmentation behaves more promisingly. Furthermore, conspicuous correlations can be found between both the feature parameters including the GLCM second-order statistics and the failure strength, strain rate as well as life. The high temperature activates thermal activation mechanisms that alter the potential crack sources and pathways in the grain and rock skeleton, affecting the microscopic fracture face morphology. Finally, the maximum tensile stress at the edge of the prefabricated fissure might be responsible for the angle effect exhibited by the feature parameters.

Predicting soil depth in a humid tropical watershed: A comparative analysis of best-fit regression and geospatial models

As the climate changes, water scarcity is becoming a major global concern. The accurate prediction of soil depth is critical for hydrological modeling under different climate change scenarios for the effective management of humid tropical watersheds. This study determined the best-fit assumption-based (i.e., ordinary least square (OLS)) and assumption-relaxed (partial least squared, quantile, and elastic net) regression models, and compared them with geospatial models (ordinary kriging, universal kriging, regression kriging, and cokriging), for predicting soil depth in a 50 × 150 m humid tropical watershed. Soil depth, apparent electrical soil conductivity (ECa) at two depths of exploration (ECas, 0–0.5 m, and ECad, 0–1.6 m using a DUALEM-1S EC meter), slope gradient, and soil physical and chemical properties measured in-field or using soil samples (0–0.2 m), were determined at each sampling location. Multivariate regression models explained 86–89% of the variability in soil depth and had a comparative root mean square error (RMSE), Lin’s concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and prediction error rate (PER) to corresponding ECad univariate models. ECad was a significant predictor in all models except the elastic net regression model. The cokriging model was superior to all regression and geospatial models with the lowest RMSE (0.04) and PER (10 %), and highest LCCC (0.99) and RPD (6.71), and can be used to accurately predict soil depth across the watershed, thereby improving hydrological models for watershed management in this and similar vulnerable humid tropical watersheds. Most interestingly, our results suggest that OLS models are in some cases robust enough to handle violations of their assumptions, implying that data transformation may not always be required for OLS regression. Therefore, it is recommended that the traditional workflow for OLS regression include a validation check using an assumption-relaxed model, the outcome of which would determine the necessity of data transformation.