Regression Equations For Prediction Research Articles

Relevant Predictors in the Association Between Patients' Functional Status and Scar Outcomes After Total Hip Arthroplasty.

We aimed to investigate the relevant predictors in the association between the functional status and the consequences of the persistence of scars in patients with traumatic versus non-traumatic coxarthrosis after total hip arthroplasty (THA). A total of 203 patients undergoing THA after traumatic or non-traumatic coxarthrosis were asked to complete the Mekeres' Psychosocial Internalization Scale (MPIS), in which they self-evaluated on a Likert scale (between one and five) by selecting the rating that corresponded to their personal opinion and the activities of daily living (ADL) format six months postoperative. The statistical data were processed using the IBM SPSS Statistics software version 22.0 (IBM Corp., Armonk, NY). A combined assessment of the internalization of scars using MPIS and ADL forms after THA allowed for the identification of relevant predictors of the quality of life six months post-surgery in patients with traumatic or non-traumatic coxarthrosis. Depending on the coxarthrosis etiology (traumatic or non-traumatic), the results were further processed by a univariate ANOVA, considering the independent variables represented by symptoms, the number of surgical procedures, and the postoperative evolution, which are acting on the outcomes of physical functioning (the dependent variable) in the postoperative phase. In the case of the traumatic group, our results suggest that the number of surgical interventions, the ability to internalize scars, and autonomy in terms of body care are predictors of the quality of life. In patients with non-traumatic coxarthrosis, an important role in predicting quality of life is played by the administered treatment and the ability to maintain their autonomy regarding self-hygiene six months post-surgery. The predictive regression equation suggests that the quality of life in patients with traumatic coxarthrosis can be predicted by the number of surgical interventions, the administered treatment, the ability to internalize scars, and the autonomy regarding body care activities. On the other hand, for patients with non-traumatic coxarthrosis, an important role in predicting the quality of life is played by the treatment and the ability to maintain autonomy in terms of body hygiene activities.

Dual-energy X-ray absorptiometry: an effective approach for predicting broiler chicken body composition

Two trials were carried out to develop and validate linear regression equations for body composition prediction using Dual-energy X-ray absorptiometry (DEXA). In Trial 1, 300 Cobb500 male chickens raised from 1 to 42 d of age were scanned in DEXA to estimate total weight, fat mass, soft lean tissue (SLT) mass, bone mineral content (BMC), and fat percentage. DEXA estimates were compared to body ash, crude fat, SLT (sum of protein and water) and scale body weight. The dataset was split, with 70% used for prediction equations development and 30% for testing, and the 5k-fold cross-validation analysis was used to optimize the equations. The R2, mean absolute error (MAE), and root-mean-squared error (RMSE) were used as precision and accuracy indicators. A negative correlation (ρ = -0.27) was observed for ash content, while no correlation was observed for protein content (P > 0.05). Predictive linear equations were developed to assess broiler weight (R2 = 0.999, MAE = 25.12, RMSE = 38.99), fat mass (R2 = 0.981, MAE = 13.87, RMSE = 21.28), ash mass (R2 = 0.956, MAE = 3.98, RMSE = 5.61), SLT mass (R2 = 0.997, MAE = 35.73, RMSE = 52.45), water mass (R2 = 0.997, MAE = 29.56, RMSE = 43.94), protein mass (R2 = 0.989, MAE = 12.94, RMSE = 19.05), fat content (R2 = 0.855, MAE = 0.81, RMSE = 1.05), SLT content (R2 = 0.658, MAE = 1.01, RMSE = 1.28), and water content (R2 = 0.678, MAE = 0.99, RMSE = 1.27). All equations passed the test. In Trial 2, 395 Cobb500 male chickens were raised from 1 to 42 d of age and used for validation of prediction equations. The equations developed for weight, fat mass, ash mass, SLT mass, water mass, and protein mass were validated. In conclusion, DEXA was found to be an effective approach for measuring the body composition of broilers when using predictive equations validated in this study for estimate calibration.

Effect of alloying elements on interfacial properties and mechanical behavior of Mg/WC systems: A first-principles study

Alloying elements play a key role in the interfacial stability and mechanical properties of WC reinforced Mg matrix composites while their influence is complex and unclear. In this work, the segregation energy, adhesion work, interface energy and tensile strength of Mg(0001)/WC(0001) interface alloying with Al, Si, Zn, Zr, Ca, La, Mn, Sc, Y and Sn were calculated by first-principles method and the strengthening and fracture mechanism of the interface were clarified. The results show that alloying elements such as Al, Si, Zr, Ca, La, Mn, Sc and Y increase the adhesion work and reduce the interface energy, thereby improving the interface stability. Meanwhile, the addition of Sn and Zn shows the opposite effect. Multiple regression analysis indicates that the valence electrons and electronegativity of alloying elements have the most significant effect on the thermodynamic parameter and a high-accuracy prediction regression equation is put forward. The fracture of the Mg/WC interface occurs in the Mg matrix side but not on the exact interface. This may result from the strong M−C (M = Al, Si) bonds at the interface and weak Mg-Mg bond in the bulk according to COHP analysis.

Multi-objective optimization and price performance factor evaluation of polyaniline nanofibers-palm oil nanofluids for thermal energy storage application

The application of nanofluid in thermal energy storage technology has attracted interest among researchers in the development of novel nanofluids with high thermal conductivity behavior. Therefore, it is crucial to study the thermal physical behavior of formulated nanofluids prior to extend use in greener energy production. In this study, response surface methodology (RSM) is employed to optimize the density, viscosity and thermal conductivity behavior of formulated polyaniline-palm oil nanofluids. RSM based central composite design (CCD) is applied to extract the significant impact of temperature in the range of 30–60 °C and volume concentration of nanoadditives in the range of 0.01–0.5 vol% to the thermal physical properties of polyaniline-palm oil nanofluids and to generate empirical mathematical model for prediction purpose. Finally, the price performance factor of the studied polyaniline-palm oil nanofluids is evaluated for the first time in this research. Analysis of variance is employed to verify that the generated mathematical regression model is reliable. The formation of 45° angle line in the middle of the predicted vs actual data graph with acceptable R2 of 94.43% for density model, 99.43% for viscosity model, and 94.18% for the thermal conductivity model showing an excellent agreement of both predicted and actual data and verified the reliability of the generated regression equation for response prediction. Optimal density, viscosity and thermal conductivity of polyaniline-palm oil nanofluids found to be 0.8878 g/mL, 25.8251 mPa s and 0.2877 W/mK respectively with the critical parameters for temperature and volume concentration of polyaniline are 60 °C and 0.0347 vol% respectively. The PPF evaluation shows that the higher thermal conductivity of nanofluids are not economical. The formulated polyaniline-palm oil nanofluid evaluated properties expose the possibility of alternative advanced heat transfer fluid for thermal energy storage application due to their superior inherent qualities.

Global Hunger Index: A multistage coefficient estimation analysis using machine learning techniques for a hunger free society

The Global Hunger Index (GHI) is one of the most prominent indices for calculating and describing the severity of hunger level in various countries. Nutrition, mortality, and food supply are the basic parameters defining the hunger index value. Using a single method for coefficient estimation of hunger index parameters is not a viable option. It is necessary to design a new framework which can help us to explore more possibilities for coefficient estimation and for identifying the critical features as well. In this paper, we have proposed a new coefficient estimation technique called Multi-Stage Coefficient Estimation (MSCE) strategy that enhances the prediction rate. MSCE is an aggregation of multiple methods for generating the coefficient values of the associated features group. Decision Tree Regression, Random Forest regression, Logistic Regression (comprising of lbfgs, saga and sag problem solvers), Simple Regression and Multiple Regression coefficient computing methods are used in the proposed framework to estimate the coefficient values. The obtained coefficients were then used to build the max, avg and min regression equations for prediction. The introduced methodology is applied on the global hunger index data collected from the GHI repository. The results of the proposed MSCE methodology were compared with other popular approaches like Simple Linear Regression, and Multiple Linear Regression in terms of Mean Absolute Error, Mean Square Error, Root Mean Square Error, R- Squared and Adjusted R-Squared metrics. The proposed MSCE strategy achieved better results when compared with existing methods.

Study and prediction analysis on road performance of basalt fiber permeable concrete

Abstract To analyze the influence of basalt fiber on the performance of permeable concrete for road applications, this study focuses on two key performance indicators: compressive strength and permeability coefficient of basalt fiber permeable concrete. Based on orthogonal experimental data, regression prediction equations were established using SPSS software to assess the effects of different fiber parameters on the compressive strength and permeability coefficient. The predicted results were then compared with experimental data. The findings indicate that the average relative error of the predicted values for both performance indicators is within a manageable range of 5%, demonstrating a high prediction accuracy. Using these regression equations, we can examine the variations in the road performance of basalt fiber permeable concrete under different fiber parameter conditions, thereby overcoming the limitations of conducting numerous parameter analysis experiments.

Assessing total erythrocyte count as a potential attribute for estimating hemoglobin in Cholistani cattle.

The main objective of the study was to confirm whether a specific and constant interrelationship exists between total erythrocyte count (TEC) and hemoglobin (Hb) concentration in Cholistani cattle blood and to navigate the potential of TEC for estimating Hb level in Cholistani cattle (n = 264) grouped as per gender (males, n = 122; females, n = 142) and age (young, n = 140; adults, n = 124). The TEC and Hb (HbD) estimation was carried out through veterinary hematology analyzer. The Hb was also calculated as TEC × 3 and was termed as HbC. Linear regression was implied, and accordingly, scatterplots were drawn between TEC, HbD, HbC, and corrected Hb (CHB). The regression prediction equation hence attained was used to deduce corrected hemoglobin (CHb). A significant (P ≤ 0.05) difference was noticed between HbD and HbC. A non-significant (P ≥ 0.05) difference was noticed, however, between HbD and CHb. Tests of level of agreement indicated a higher Cronbach's alpha and intraclass correlation coefficient (0.682 for average measures) for HbD and CHb as compared to that for HbD and HbC (0.559 for average measures). A convention of Hb concentration as three times of TEC (× 3) is not valid for Cholistani cattle. A different pen-side hematological formula, i.e., Hb (g/dL) = 0.66(TEC) + 6.1, however, provides a better estimate of Hb from the TEC in cattle blood. Using hemocytometry for TEC in the field, all the stakeholders associated with veterinary research, academics, and practice may benefit from this formula in resource-poor countries.

METHODOLOGICAL APPROACH TO THE ASSESSMENT OF FACTORS OF CHEMICAL PRODUCTION (ON THE EXAMPLE OF THE CARBON DIOXIDE MARKET)

Since the activity of industrial enterprises is influenced by many factors, it becomes necessary to assess this influence by methods of statistical analysis and economic and mathematical modeling. To take into account the many factors that affect the activities of chemical companies, it is advisable to use the methods of multiple correlation and regression analysis, which allows us to evaluate not only internal and external causeand-effect relationships, but also to study the development features and trends in the development of chemical companies and the resulting attribute. When constructing a correlation-regression model, the following requirements were met in the work: required number of factors that have a quantitative assessment for the analyzed market has a monthly distribution for January. 2019-Oct.2020; a sufficient amount of factor signs (the number of observations is almost 3 times higher than the number of factors); the totality of the studied factor characteristics is homogeneous. The purpose of this article is to develop a methodological approach to assessing the factors of production of chemical products. The article presents a step-by-step description of the algorithm, with the help of which it is proposed to evaluate the factors influencing the performance of chemical products. The following stages of the algorithm for assessing the factors of production of chemical products are identified: the formation of an information base for the study, the construction of a correlation matrix, the compilation of a predictive regression equation, the assessment of the statistical significance of the parameters of the obtained regression equation, and the development of proposals for achieving the predictive values of the resultant attribute. The research methodology proposed in the article makes it possible to identify factors in the development of specific chemical products, as well as to predict key indicators that affect the production of products. The practical significance of the study lies in the fact that the considered approach to forecasting the development of the market for chemical products makes it possible to determine the specific values of quantitative indicators that affect the volume of production of a particular type of chemical product. Approbation of the proposed approach was carried out on the example of the carbon dioxide (carbon dioxide) market. The results obtained confirm that the constructed model can be used to analyze and predict the main performance indicators of chemical companies.

Using head, pectoral girdle bones and otoliths to estimate length and weight of Argentine anchovy (Engraulis anchoita), a key species in Patagonian marine ecosystem

The Argentine anchovy, Engraulis anchoita, plays a vital role as a key prey species for several marine predators in the north Patagonian marine ecosystem of the Atlantic Ocean. Reconstructing the length and weight of each consumed specimen is essential to provide a detailed description of the trophic ecology of top marine predators. Predictive linear regression equations were calculated for the Patagonian stock of Argentine anchovy to estimate parameters of length-weight relationships using measurements of whole individuals and diagnostic elements such as otoliths, head bones and pectoral fin bones. Among the diagnostic elements analyzed, the cleithrum and dentary exhibited the best fit. This study validates the use of head and pectoral girdle bones as reliable indicators for predicting the weight and length of Argentine anchovy across a wide size range, which corresponds to the target range of various predators. These relationships can contribute to the determination of body condition, estimation of consumed biomass, and calculation of energy density, providing valuable insights into the trophic ecology of predators in the southern Atlantic Ocean.

Can C7 Slope Accurately Substitute for an Invisible T1 Slope According to Age and Cervical Morphology in Cervical Lateral Radiographs?

A retrospective radiologic study. To identify age-associated changes in T1 slope (T1S) and C7 slope (C7S), as well as the difference between T1S and C7S (△, which was defined as T1S minus C7S) and to explore the cervical morphology that C7S can be the most accurate alternative for the invisible T1S. 625 asymptomatic Chinese volunteers received cervical lateral radiographs from August 2021 to May 2022. Occipito-C2 angle (O-C2), C2-7 angle (C2-7), cranial arch, caudal arch, C7S, and T1S were examined. Thereafter, the △ was established. The correlations among T1S, C7S, △ and other cervical sagittal parameters, and between age and other cervical sagittal parameters were evaluated with the Pearson correlation coefficient analysis. Then, analysis of variance (ANOVA) was conducted to compare variations in cervical sagittal parameters among volunteers aged 40 to 59years, 60 to 64years, 65 to 69years, 70 to 74years, and ≥75years of age, and among volunteers with 1 lordotic morphology, 2 lordotic morphology, straight morphology, kyphotic morphology, 1 sigmoid morphology, and 2 sigmoid morphology. Linear regression modeling of the correlation between C7S and T1S in various cervical alignment patterns was then established. △ had the strongest correlation with caudal arch (r = .646), and weakest correlation with cranial arch (r = -.082). Age was significantly correlated with T1S (r = .250), C7S (r = .244), and △ (r = .112). Among them, △ was stable until 74years after which it showed an elevation from 3.3° in the group 70-74years to 4.1° in the group over 75years. Moreover, there was marked variation between T1S and C7S at 1 lordotic, 2 lordotic, straight and 2 sigmoid alignment patterns, but no difference was seen between T1S and C7S at kyphotic and 1 sigmoid alignment patterns. There was a progressive increase in T1S, C7S, and △ with age. Linear regression equations for accurate prediction of T1S were developed based on the C7S in 1 lordotic, 2 lordotic, straight and 2 sigmoid alignment patterns. C7S may be a reliable proxy for T1S in kyphotic and 1 sigmoid alignment patterns.

Empirical validation of an angle-error model for computing free-field sound power using a cylindrical enveloping surface

The angle error inherently biases the calculated sound power level when determined using free-field sound pressure level measurements over an enveloping surface. A regression equation was previously created to predict the angle error associated with omnidirectional sources using just a few easily obtainable input variables related to the geometry of the source and the enveloping measurement surface. This paper assesses the accuracy of that predictive regression equation through an empirical study. A dodecahedron loudspeaker was used as a noise source over a range of positions within the reference box. The modeled angle error correction was applied to the computed sound power levels using measurement data from multiple cylindrical enveloping surfaces in accordance with ISO 3744 and ISO 7779. The corrected sound power levels were compared to the measured sound power levels of the source per ISO 3741 direct and comparison methods.

Investigating the potential of packed cell volume for deducing hemoglobin: Cholistani camels in perspective.

In human medical practice, a hematological rule of three has been validated for healthy human populations. One such formula is estimating hemoglobin (Hb) levels as 1/3rd of Packed Cell Volume (PCV). However, no such hematological formulae have been devised and validated for veterinary medical practice. The present study was devised with an aim to evaluate the relationship between hemoglobin (Hb) concentration and Packed Cell Volume (PCV) in camels (n = 215) being reared under pastoralism, and to devise a simple pen-side hematological formula for estimation of Hb from PCV. The PCV was determined through microhematocrit method whereas Hb estimation by cyanmethaemoglobin method (HbD). The Hb was also calculated as 1/3rd of PCV and was dubbed as calculated Hb (HbC). Overall HbD and HbC were significantly (P≥0.05) different. Similar results were attained for all study groups i.e. males (n = 94) and females (n = 121), and young (n = 85) and adult (n = 130) camels. The corrected Hb (CHb) was deduced through regression prediction equation attained from linear regression model. Scatterplots were drawn, linear regression was carried out, and Bland Altman chart was built for agreement of both methods of Hb estimation. A non-significant (P≥0.05) difference was noticed between HbD and CHb. Bland Altman agreement analysis revealed satisfactory agreement between HbD and CHb and the data was distributed closely around the mean difference line (Mean = 0.1436, 95% CI = 3.00, -2.72). A simplified pen-side hematological formula for deducing Hb concentration from PCV is accordingly recommended viz. Hb concentration (g/dL) = 0.18(PCV)+5.4 for all age and gender groups of camels instead of its calculation as one-third of PCV.

Optimization of laser machining parameters of SS 314 using response surface methodology

Stainless steel (SS) is an essential industrial and structural material that is challenging to be machined by conventional methods due to its very high melting temperature and the oxides that are formed. Still, as an alternate method, it can be machined by a laser. The primary objective of the current study is to optimize the CO2 laser beam cutting process parameters (viz. are (i) laser power, (ii) cutting speed, and (iii) gas pressure) on surface characteristics of machining 5 mm Stainless Steel (SS 314). Using Response Surface Methodology (RSM) optimization approach and the Box Behnken method, the process parameter of laser cutting were optimized while minimizing the surface roughness. RSM suggested a quadratic regression equation for surface roughness prediction in terms of process parameters. The optimum process parameters obtained are laser power = 1.2 kW, gas pressure = 0.579 bar, and the cutting speed = 2.4 m/min, which results in surface roughness of 2.132 µm.

Resting metabolic rate in healthy Singaporeans: Performance of the Harris-Benedict equation and a new predictive model

Background Prediction equations for resting metabolic rate (RMR) are valuable in managing patients’ weight; however, no accurate equation exists for Singaporeans. Objective To develop and cross-validate a predictive regression equation for RMR in Singaporeans, using indirect calorimetry as the reference method. Methods 104 healthy Singaporeans (34.3 ± 12.2 years) participated, comprising 34 men and 70 women. Anthropometric measurements and demographics information were obtained from participants. RMR was measured via indirect calorimetry (TrueOne 2400 system). Stepwise regression analysis was used to develop the most parsimonious predictive equation. Performance of the equation was evaluated using ordinary least products (OLP) regression and Bland–Altman analysis, whilst internal cross-validation was performed by use of the predicted residual sum of squares (PRESS) method. To compare the new equation with existing ones, the performance of the Harris-Benedict equation was also evaluated. Results The best predictive equation takes the form RMR(kcal) = 918 + 16.5(weight)-135.7(gender) - 1152(Waist-to-height-ratio) +0.014(International Physical Activity Questionnaire Score), where gender = 1 (female) or 0 (male). OLP regression revealed no systematic bias for the new equation. Bland–Altman analysis showed that its total (systematic and random) error was 212 kcal. Internal model validation using the PRESS method revealed minimal reduction in predictive accuracy. In contrast, OLP regression showed a significant pattern of over-prediction by the Harris-Benedict equation (y-intercept = −280 kcal; 95%CI, −100 to −461 kcal). Conclusions Our new equation outperformed the Harris-Benedict equation in accurately predicting RMR in Singaporeans. Comprising easily obtained anthropometric and self-reported measures, we envisage its potential relevance in clinical and epidemiological settings.

The Relationship Between Anthropometric Variables and Swimming Efficiency in Early Pubescent Female Front Crawl Swimmers

The aim of this study was twofold. The first aim was to examine the association of anthropometric measures on kinematic characteristic which represent stroke efficiency in young female front crawl swimmers. The second aim was to create a prediction model which could provide help to swimming coaches with the purpose of easier and better selection of female front crawl swimmers by measuring and following basic morphological characteristics. Eighty female competitive swimmers all members of the swimming Clubs in province of Vojvodina, Serbia (age 12.52 ± 08 years, years of training involvement 4.05 ± 1.2 and personal best times over 50 m front crawl 32.79 ± 0.86 s) performed 50 m front crawl race. The 50 m front crawl swimming efficiency expressed through stroke index significantly was related to body height (r = 0.44, p = 0.000), body mass (r = 0.402, p = 0.000), sitting height (r = 0.612, p = 0.000) arm spam (r = 0.576, p = 0.000), biacromial diameter (r = 0.470, p = 0.000), bicrestal diameter (r = 0.348, p = 0.001) and with chest circumference (r = 0.427, p = 0.000). Regression equation for stroke index prediction was defined by following variables: body mass, sitting height, arm span, chest circumference with 43.5% explained variance. Additionally by analyzing obtained model the higher the values of SH, ARSP, CHICR and the lower values of BM in a group of early pubescent female swimmers the higher 50 m front crawl efficiency values will be.

A used car repricing method based on K-Means++ clustering and multiple linear regression

A predictive pricing method based on k-means++ clustering and multiple linear regression is proposed to solve the unreasonable initial pricing of used cars and evaluate the model's low accuracy. To begin, all vehicles are divided into three categories based on the transaction period of the data sample. The K-Means++ clustering algorithm is then used to cluster the best-selling cars, and a multiple linear regression equation is fitted for each category as the regression equation for prediction and evaluation. Finally, the revaluation of unsold and unsalable vehicles is examined. In addition, two used car valuation methods, XGBoost and AdaBoost, are compared. Because the model performs well, the experiment shows that using the multiple linear regression method based on clustering to estimate car price is reasonable.

Simulation and Experimental Analysis of Tool Wear and Surface Roughness in Laser Assisted Machining of Titanium Alloy

A three-dimensional cutting simulation prediction model based on DEFORM-3D finite element software was developed and experimentally validated, with a maximum error of 21.1% between the experimental and simulation results. The effects of the difference in cutting mechanism between conventional machining (CM) and laser-assisted machining (LAM) of TC6 titanium alloy on the tool wear and the surface roughness were investigated in terms of the cutting force and the cutting temperature. The depth of the laser-heated layer was mainly responsible for the difference in the cutting mechanism between the two methods. When the depth of the heating layer was smaller than the cutting depth, the tool wear of the LAM was larger than that of the CM. When the depth of the heating layer was larger than the cut depth, the surface roughness of the LAM was higher than that of the CM. Range analysis revealed that the cutting speed had the largest effect on the maximum wear depth of the rake face. Based on linear regression analysis, the cutting depth had a larger effect on the surface roughness in LAM. The average error between the linear regression prediction equation and the experimental results for surface roughness was 4.30%.

Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments.

Human movements in complex traffic environments have been successfully simulated by various models. It is crucial to improve crowd safety and urban resilience. However, few studies focus on reproducing human behavior and predicting escape reaction time in the initial judgement stage in complex traffic environments. In this paper, a pedestrian pre-evacuation decision-making model considering pedestrian heterogeneity is proposed for complex environments. Firstly, the model takes different obvious factors into account, including cognition, information, experience, habits, stress, and decision-making ability. Then, according to the preference of the escapees, the personnel decision-making in each stage is divided into two types: stay and escape. Finally, multiple influencing factors are selected to construct the regression equation for prediction of the escape opportunity. The results show that: (1) Choices of escape opportunity are divided into several stages, which are affected by the pedestrian individual risk tolerance, risk categories strength, distance from danger, and reaction of the neighborhood crowd. (2) There are many important factors indicating the pedestrian individual risk tolerance, in which Gen, Group, Time and Mode are a positive correlation, while Age and Zone are a negative correlation. (3) The analysis of the natural response rate of different evacuation strategies shows that 19.81% of people evacuate immediately. The research in this paper can better protect public safety and promote the normal activities of the population.

Devising and validating a pen-side hematological formula for hemoglobin estimation in Cholistani cattle.

The present study has been designed to devise a pen-side hematological formula for estimation of hemoglobin (Hb) from packed cell volume (PCV) in Cholistani breed of cattle being reared under pastoralism in Cholistan desert, Pakistan. It also aims to validate the soundness of rule of calculating Hb concentration as one-third of PCV and vice versa as being used in human medicine. Cholistani cattle (n = 364) were bled for PCV determination (microhematocrit) method and Hb estimation (hematology analyzer) (HbD) as well as through calculation being one-third of PCV (HbC). The independent-sample t-test was implied for deducing difference between HbD and HbC, and between HbD and corrected Hb (CHb) for all study groups. The CHb was deduced through regression prediction equation attained from linear regression model. Scatter-plots were drawn and linear regression was carried out between various studied hematological attributes. Significantly (P ≤ 0.01) positive correlation coefficient was noticed for all study groups being highest for female adult stock (r = 0.893; adjusted R-square = 0.79) between HbD and PCV and between HbD and HbC. The regression equation for overall results attained, i.e., Hb = 0.13 (PCV) + 6.3 was used to deduce CHb. A non-significant (P ≥ 0.05) difference was noticed between HbD and CHb. In conclusion, a convention of human clinical medicine that Hb concentration is third of PCV and vice versa cannot be implied for Cholistani cattle. A different equation, i.e., Hb (g/dL) = 0.13 (PCV) + 6.3 may provide reliable results for Hb estimation from the PCV in cattle.

Development of a multiple regression equation for prediction of bearing capacity of geocell-reinforced sand beds based on experimental study

Development of a multiple regression equation for prediction of bearing capacity of geocell-reinforced sand beds based on experimental study