Standard Error Of Estimate Research Articles

EVALUATIONS OF DIFFERENT MODELS FOR PREDICTING MERCHANTABLE VOLUME OF PINUS BRUTIA TEN. IN DUHOK GOVERNORATE

This study was initiated with the main objective of evaluating the prediction power of previously 24 published models in the literature, developed for estimating the merchantable volume of natural stands of Pinus brutia Ten. The estimation was based on measuring the breast diameter (D), tree height (h), and absolute form quotient (F) of 120 pine trees (Pinus brutia Ten.) from 2 natural stands situated to the east of Duhok governorate. Six indicators of Fit test statistics, namely Adjusted coefficient of determination (R2- adj.) standard error of estimate (SEE), mean absolute error (MAE), Durbin-Watson statistic (D-W), p-value, and mean biased error (Bias) were used to test the performance of the applied models. The result from the centroid model was considered a reference method for the evaluation during this study. The results indicated that the square root –y logarithmic-x offered the highest performance followed by the double square root model. The square root –y logarithmic-x (equation 11) attributed more than 90% of the variation in merchantable volume to variations in D, h, and F. Furthermore, the mean absolute error of prediction of this model was 0.0434. According to this study, the mean stem form of Pinus brutia trees is (0.64), which signifies quadratic paraboloid.

Estimating age at death by Hausdorff distance analyses of the fourth lumbar vertebral bodies using 3D postmortem CT images.

The existing methods for determining adult age from human skeletons are mostly qualitative. However, a shift in quantifying age-related skeletal morphology on a quantitative scale is emerging. This study describes an intuitive variable extraction technique and quantifies skeletal morphology in continuous data to understand their aging pattern. A total of 200 postmortem CT images from the deceased aged 25-99years (130 males, 70 females) who underwent forensic death investigations were used in the study. The 3D volume of the fourth lumbar vertebral body was segmented, smoothed, and post-processed using the open-source software ITK-SNAP and MeshLab, respectively. To measure the extent of 3D shape deformity due to aging, the Hausdorff distance (HD) analysis was performed. In our context, the maximum Hausdorff distance (maxHD) was chosen as a metric, which was subsequently studied for its correlation with age at death. A strong statistically significant positive correlation (P < 0.001) between maxHD and age at death was observed in both sexes (Spearman's rho = 0.742, male; Spearman's rho = 0.729, female). In simple linear regression analyses, the regression equations obtained yielded the standard error of estimates of 12.5years and 13.1years for males and females, respectively. Our study demonstrated that age-related vertebral morphology could be described using the HD method. Moreover, it encourages further studies with larger sample sizes and on other population backgrounds to validate the methodology.

Age estimation using extracted teeth in coastal Karnataka population.

Age estimation using regressive alterations such as root dentin translucency, PDL attachment, and attrition are easy and reliable way of predicting the age. However, extensive and population-specific formula has not been generated. This attempt was to assess the correlation of these alterations with age and to generate a Linear regressive formula, specific to this population. Three alterations were assessed such as dental attrition, root dentin translucency, and periodontal attachment level from the extracted teeth. Dental attrition was measured using Johanson's and Li and Ji criteria. PDL attachment level and root dentin translucency was measured using the Johanson method and the Lamendin method. SPSS software (Version 27), Pearson correlation test, and Linear regressive analysis were used. Our results showed all three factors/parameters such as attrition, periodontal ligament, and translucency having a very good correlation with age and correlation coefficient r value ranging from 0.6 to 0.8. All the parameters were having statistically significant correlation with P value <0.005. Among them, root dentin translucency with Johanson G method showed excellent correlation with r = 0.83 followed by PDL attachment by Johanson G method with r = 0.702. Regressive changes such as Dentin translucency, PDL attachment and attrition on Coastal Karnataka showed a very good correlation with age. Among them, Dentin translucency by Johanson G method had the best correlation with the a standard error of estimate (SEE). Results of our study indicates that all these parameters [Translucency, PDL attachment, and attrition] can be utilized in age estimation.

Mathematical Models of Moisture Sorption Isotherms of Grains Sorghum (Sorghum bicolor (L.) Moench)

1U. of K. J. Agric. Sci. 22(1), 1-12, 2014Mathematical Models of Moisture Sorption Isotherms of GrainsSorghum (Sorghum bicolor (L.) Moench)Eman Abdu Abdalla1 , Mohamed Ayoub Ismail and AbdelmoniemElamin Mohamed2Department of Agricultural Engineering, Faculty of Agriculture,University of Khartoum, SudanAbstract: The objective of the study was to fit or test the experimentaldata of adsorption and desorption isotherms of grains sorghum The studywas carried out at the Department of Agricultural Engineering, Faculty ofAgriculture, University of Khartoum and the Food Processing ResearchCenter, Shambat, Sudan, during January 2007 - March 2008. Fourmathematical models namely modified Henderson, modified Chung-Pfost, modified Oswin and modified Halsey were used to predict sorptionisotherms. The adsorption and desorption isotherms were determinedusing standard static gravimetric method, six levels of water activitiesrang 0.112- 0.865 were maintained by using saturated salt solutions insideair-tight glass desiccators, while incubators were used to maintain three aconstant temperature namely 25oC,35 oC and 45oC. A non-linearregression analysis was used to evaluate the parameters of the fourselected models. The models were compared by using statisticalparameters: mean relative percentage deviation (P%), standard error ofestimates (Es), coefficient of determination (R2 ) and randomness ofresidual plots of difference between measured and predicted values. Forthe mathematical models, the results indicated that the modified Chung-Pfost model satisfactorily predicts the adsorption and desorptionisotherms of grains sorghum

Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation

Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 × AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications.

Modeling of Valsalva sinuses and coronary artery ostia height parameters, depending on age-anthropometric indicators in healthy men based on computed tomography

Computed tomography is the “gold standard” for performing aortic morphometry during preoperative planning in invasive cardiology and cardiac surgery. Predictive modeling of indicators can significantly save resources. The purpose of the study: to make modelling of Valsalva sinuses and coronary artery ostia height parameters depending on age-anthropometric indicators in healthy men based on computed tomography. The material is represented by contrast-enhanced computed tomography images of the aorta and coronary arteries of forty-three men under normal conditions. Methods: morphometric and statistical analyses. A multifactorial correlation-regression analysis was conducted to establish the complex influence of age-anthropometric parameters on sinuses of Valsalva and coronary artery ostia height. The reliability of the obtained indicators was confirmed by Fisher’s test (F). Using the Durbin-Watson autocorrelation criterion, the correctness of the built model was proved. In healthy men, weight (direct effect) and body mass index (inverse effect) significantly influenced the height of the lower edge of the right coronary artery ostia. The regression coefficient is R = +0.632, with p&lt;0.001, the standard error of estimation (SEE) is 2.951. The obtained linear equation of the prognostic model: the level of the height of the departure of the lower edge of the right coronary artery ostia = 0.359×А1 – 1.099×А2 + 16.53. The correctness of the built model was checked using the Durbin–Watson autocorrelation test (2.181). The prognostic model for calculating the height of the left aortic sinus was formed by height and weight indicators (strong direct influence): R = +0.759, p&lt;0.001, SEE = 2.208. The adjusted coefficient of multiple determination was R2adj = +0.562. The Durbin-Watson autocorrelation criterion was within the normal range (2.241). The linear equation of the prognostic model with the obtained β-coefficients: the level of the height of the left sinus of the aorta = 35.83 × А1 + 0.033 × А2 – 42.22. The work of prognostic models for individuals with different anthropometric and age parameters was verified. Thus, a model of the dependence of the indicator of the height of the left sinus of the aorta on height and weight was created; of the height of the deviation of the lower edge of the right coronary artery from weight and body mass index in healthy men based on computed tomography.

Non-linear Regression Models for Predicting Biogas Yields from Selected Bio-wastes

The benefits of biogas as alternative energy to other fossil fuel sources, due to its renewability, environmentally friendly nature, health benefits, etc., cannot be overemphasized. There are numerous models for predicting biogas production rate from bio-materials, including the modified Gompertz equation. These models are primarily dependent on specific biomass parameters. When any of these parameters, like the slurry volume, changes, another round of experiments must be conducted and curve fitted before biogas yield predictions can be made. This could be time-consuming and costly. Using experimentally published data, simple empirical models can be developed for predicting biogas yields over a range of input parameters. This will eliminate the need for always performing experiments before biogas yield predictions can be made. In light of this, scarce literature provides explicit models for predicting biogas yield over a range of parameters based on published data. This study developed non-linear regression models using published data on parameters that affect biogas yields, like the slurry volume, carbon-to-nitrogen ratio, temperature, total solids, volatile solids, hydraulic retention time, and pH. The data covered seven readily available bio-wastes, including cow dung, cow dung with plant waste, cow dung with poultry dung, poultry dung with grass, pig dung, and plant wastes. On validation of the models, the results showed that the models had a relatively low standard error of estimates, Akaike information criterion, Schwarz criterion, and Hannan-Quinn information criterion. Furthermore, the coefficients of determination, R2, were between 94.62 and 98.93%. The percentage average absolute deviation (% AAD) for each model was less than 7 %. The non-linear models were found to adequately predict the biogas yields within the limits of the available data set.

Psychological Security during Coronavirus Pandemic for a sample of University of Jeddah Students

Objectives: The study aimed to reveal the level of psychological security during Corona pandemic period for a sample of the University of Jeddah students.&#x0D; Methods: To achieve the goal of the study, a scale consisting of (33) items was constructed according to item response theory, specifically according to the rating scale model. The researcher used the following programs: (SPSS), (BIGSTEPS), and (BIOLOG-MG3) in analyzing the data of this scale, which was applied to a sample of (612) students from the University of Jeddah. During the first semester of the academic year (1441-1442 AH), corresponding to (2020-2021 AD), each of the following were estimated: psychological security scale parameters, the standard error of estimate, and items fitting to the rating scale model&#x0D; Results: In light of the results of model fitting, the psychological security scale consisted in its final form of (25) items. Also the results showed that the scale has good psychometric properties.&#x0D; Conclusion: Depending on the results, it becomes clear that the level of psychological security during the Corona pandemic was high among the students of the University of Jeddah.

Stature and its Estimation Utilizing Hand and Foot Measurements in North Indian Population

Background: Stature estimation from body measurements is an important part of forensic and medico-legal cases especially in victim identifcation, where mutilated and amputated body parts are encountered at the crime scene. Hand and foot dimensions are the important features in context of stature estimation. The present study was conducted to find out the correlation of length and breadth of hand and foot in the stature of the North Indians. Methods: Anthropometric data from 413 males and 413 females were collected. Simple and multiple linear regression methods were used for estimation of stature from hand length, hand breadth, foot length and foot breadth measurements. Results: Statistically signifcant results (p &lt;0.001) are obtained with each measurement when the derived model is tested for stature estimation. Hand length in both sexes showed highest correlation with the stature whereas foot breadth showed the lowest. Multiple regression models showed low standard error of estimate (SEE). Conclusion: The results of this study showed that, from a forensic stand point, measurements of the hands and feet may be employed to estimate the stature of North Indian populations.

Appendicular Skeletal Muscle Mass Prediction in People Living With HIV: A Cross-sectional Study.

People living with HIV (PWH) experience an accelerated aging process. There is no anthropometric predictive model for appendicular skeletal muscle mass (ASM) in PWH. This study develops anthropometric models to predict and validate ASM measured by dual energy x-ray absorptiometry (DXA) in PWH; DXA scans were obtained for 125 PWH (male = 74; age >18 years) on antiretroviral therapy. Fat mass ratio was used for lipodystrophy diagnosis. A multiple stepwise linear regression considered ASM DXA as the dependent variable and validated by PRESS method. A high power of determination and low standard estimate error were found for ASM DXA -predicted (adjusted r2 = 0.84 to 0.87, standard estimate error = 1.7-1.6 kg) and high PRESS validation coefficients (Q 2PRESS = 0.84-0.86, S PRESS = 1.7-1.6 kg). The variables included were lipodystrophy diagnosis, medial calf circumference, sex, and total body weight. We present novel, reliable, and validated anthropometric models to predict ASM DXA in PWH.

An insight into the sorption kinetics of boscalid onto soils: Effect of general soil properties

Boscalid is a carboxamide fungicide widely used for crop protection, however owing to its high persistence, it is detected in high concentrations in various environments. Since the fate of such xenobiotics is strongly influenced by its interaction with soil components a better understanding of its adsorption onto soils of varying properties could allow the adjustment of its application in a given agro-ecological region to limit the consequent environmental burden. The present investigation was carried out to examine the kinetics of boscalid adsorption onto ten Indian soils of varying physico-chemical properties. Kinetic data of boscalid for all soils under investigation fitted well to both pseudo-first-order and pseudo-second-order kinetic models. However, based on the standard error of estimate (S.E.est.) values pseudo-first-order model was better for all soil samples, except one soil which had the lowest readily oxidizable organic carbon. Adsorption of boscalid by soils appeared to be controlled by the diffusion-chemisorption process while for soils especially rich in readily oxidizable organic carbon or clay + silt content the intra-particle diffusion process seemed to be more important. Stepwise regression of kinetic parameters on soil properties revealed that the inclusion of a set of some soil properties could help better prediction of adsorbed amounts of boscalid and kinetic constants. These findings may help assess the fate and possible transport of boscalid fungicide in different soils.

Magnetic Resonance Imaging Of Calvarial Diploe Thickness for Sex And Age Estimation of A Sample Of Egyptian Population

Background: Magnetic resonance imaging (MRI) is a mainstay radiological technique that is becoming more desirable in forensic identification for its non-invasive, non-ionizing and highly precise nature. Objectives: This study aimed to investigate whether sex and age could be estimated based on Calvarial Diploe Thickness (CDT) using MRI in a sample of the Egyptian population. Methods: The study was conducted on cranial MRI images of 306 adult Egyptians. For sex and age estimation, seven CDT measurements were assessed on 256 cranial MRI images of known sex and age. A binary logistic regression equation was developed for sex prediction. Multivariate linear regression analyses were performed to generate age prediction equations; equation (1), gender was included, while equation (2) was irrespective of gender. All regression equations were tested on 50 cranial MRI images. Results: The results revealed a significant binary logistical regression model for sex prediction of r2 0.442 and classification accuracy 71.6%. The Receiver Operating Characteristic (ROC) curve showed an area under the curve (AUC) 0.816, sensitivity 76.6 %, and specificity of 70.5%. Testing the equation showed accuracy for identifying males (78.2 %), accuracy for identifying females (70.4 %), and overall accuracy (74 %). Regarding age estimation, Pearson correlation showed no statistically significant correlation between age and all CDT measurements. Age prediction multivariate linear regression models (sex-stratified/sex-pooled) were statistically insignificant, with very low adjusted r2 and high standard error of estimate. Testing the multivariate linear regression equations showed Mean Absolute deviation (MAD) between the chronological age and predicted age of 9.09254 years and Mean Absolute Percentage Error (MAPE) of 28.458% for the multivariate linear regression equation (1) and MAD of 8.94469 years; and MAPE of 27.9829% for the multivariate linear regression equation (2). Conclusion: We concluded that the studied CDT measurements could be included as a new candidate in MRI-based sex estimation for Egyptians while are unfavorable for estimating age. Our results provide preliminary results for assessing a new approach for estimating sex and age for Egyptians. Applying such parameters on a greater sector of Egyptians for both sex and age estimation is highly recommended.

Developing a two-parameter Liu estimator for the COM–Poisson regression model: Application and simulation

The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression model used for over- and under-dispersion cases. In regression analysis, when the explanatory variables are highly correlated, this means that there is a multicollinearity problem in the model. This problem increases the standard error of maximum likelihood estimates. To manage the multicollinearity effects in the COMP model, we proposed a new modified Liu estimator based on two shrinkage parameters (k, d). To assess the performance of the proposed estimator, the mean squared error (MSE) criterion is used. The theoretical comparison of the proposed estimator with the ridge, Liu, and modified one-parameter Liu estimators is made. The Monte Carlo simulation and real data application are employed to examine the efficiency of the proposed estimator and to compare it with the ridge, Liu, and modified one-parameter Liu estimators. The results showed the superiority of the proposed estimator as it has the smallest MSE value.

Channel Intensity and Edge-Based Estimation of Heart Rate via Smartphone Recordings

Smartphones, today, come equipped with a wide variety of sensors and high-speed processors that can capture, process, store, and communicate different types of data. Coupled with their ubiquity in recent years, these devices show potential as practical and portable healthcare monitors that are both cost-effective and accessible. To this end, this study focuses on examining the feasibility of smartphones in estimating the heart rate (HR), using video recordings of the users’ fingerprints. The proposed methodology involves two-stage processing that combines channel-intensity-based approaches (Channel-Intensity mode/Counter method) and a novel technique that relies on the spatial and temporal position of the recorded fingerprint edges (Edge-Detection mode). The dataset used here included 32 fingerprint video recordings taken from 6 subjects, using the rear camera of 2 smartphone models. Each video clip was first validated to determine whether it was suitable for Channel-Intensity mode or Edge-Detection mode, followed by further processing and heart rate estimation in the selected mode. The relative accuracy for recordings via the Edge-Detection mode was 93.04%, with a standard error of estimates (SEE) of 6.55 and Pearson’s correlation r &gt; 0.91, while the Channel-Intensity mode showed a relative accuracy of 92.75%, with an SEE of 5.95 and a Pearson’s correlation r &gt; 0.95. Further statistical analysis was also carried out using Pearson’s correlation test and the Bland–Altman method to verify the statistical significance of the results. The results thus show that the proposed methodology, through smartphones, is a potential alternative to existing technologies for monitoring a person’s heart rate.

Rapid MRI Assessment of Long-Axis Strain to Indicate Systolic Dysfunction in Patients With Sickle Cell Disease.

Patients with sickle cell disease (SCD) have a unique form of cardiomyopathy. However, left ventricular ejection fraction (LVEF) is often preserved. Monoplanar long-axis strain (LAS) can be assessed from MRI four-chamber views and may be better at detecting mild systolic dysfunction in these patients. To compare LAS (monoplanar and biplanar) with LVEF as a marker of systolic dysfunction in SCD patients. Retrospective. A total of 20 patients with genetically proven SCD (35 MRI examinations), 39 healthy controls, and 124 patients with systemic iron overload (for validation purposes). 1.5 T/3T. Cine balanced steady-state free-precession. Rapidly assessed biplanar LAS from four- and two-chamber views was correlated with age and compared to LVEF by two operators. For validation, biplanar LAS was compared to global longitudinal strain (GLS) using MRI feature-tracking in 124 patients with systemic iron overload. Bland-Altman analysis. Wilcoxon-Mann-Whitney test and Spearman-rank correlation (correlation coefficient, rS ). Receiver-operating-characteristic (ROC) curve analysis (area under the curve, AUC). Bivariate discriminant analysis. Significance level: P < 0.01. There was strong correlation between biplanar LAS and GLS using feature tracking (rS =0.73). Interoperator agreement showed nonsignificant bias for biplanar LAS (-0.02%; ±95%-agreement interval -2.2%/2.2%, P= 0.9). Biplanar LAS increased significantly with age in controls (rS =0.70). In SCD patients, biplanar LAS was better correlated with age than monoplanar LAS (r2 =0.53, standard error of estimate, SEE=1.4% vs. r2 =0.37;SEE=2.0%). ROC analysis of LVEF, biplanar LAS, and age-adjusted Z-scores Z (LAS(age)) showed AUCs of 0.69, 0.75, and 0.86 for differentiation between SCD patients and controls. Bivariate discriminant analysis of biplanar Z (LAS(age)) and LVEF revealed a sensitivity of 63% and a specificity of 95%. Rapidly assessed biplanar LAS demonstrated high diagnostic accuracy and was an indicator of mild systolic dysfunction in patients with SCD. Biplanar LAS provided more precise measurements than monoplanar, and normalization to age increased diagnostic accuracy. 3. Stage 2.

Age estimation of archeological populations using secondary dentin analysis

The innermost dental tissue, termed secondary dentin (SD), has been established as a valuable parameter for aging, as its thickness is highly correlated with age and can be viewed in a non-invasive manner using micro-computerized tomography (μCT). Dental remnants show a high preservation rate in an archeological context and may be used as age indicators, thus possibly supplementing traditional skeletal-based demographics. As the SD age assessment method was based on unworn teeth, in the current study, we adjusted the approach to allow age estimation of highly worn teeth and later to explore prehistoric samples. In order to do so, 90 lower premolars from known age and gender individuals with various degrees of occlusal attrition were included. The teeth originated from the Hamann-Todd Osteologic Collection (Cleveland, OH, USA) and from the Tel Aviv University collection (Israel). Combining SD measurements and occlusal Attrition slightly improved the standard error of age estimate from ±7.7 years based on SD and ±9.5 years based on Attrition to a range of ±6.5 years.The SD-age method was used to evaluate the age of individuals from three Natufian and Neolithic sites using dental remains (Hayonim Cave, Ein Mallaha 14,900–11,750 cal BP, and Kfar Hahoresh ∼12,175–8250 cal BP). Population coverage increased substantially with the addition of forty-eight premolars to previous skeletal age assessments to create new life tables. The addition of dental age assessments, representing unaccounted individuals, shifted the age distributions and increased the expected lifespan between 4.7 and 1.8 years. Using the SD-age analysis, isolated, fragmented teeth can be included in the paleodemographic study, thus broadening the coverage of samples and providing better insight into archaic populations' structure.

Development of MLR and variedly optimized ANN models for forecasting the detachability and liquefaction potential index of erodible soils

• DLPI which quantifies soil erodibility potentials, was computed for southern Anambra. • MLR and four ANNs successfully predicted the DLPI of the study area. • Low modeling errors were obtained in the developed MLR and ANN models. • Soft computing algorithms can enhance soil erosion risk assessment and management. Artificial intelligence modeling techniques, such as artificial neural network (ANN) and multiple linear regression (MLR), have been found useful in the prediction of environmental hazard indicators, even in areas with data scarcity. The detachability and liquefaction potential index ( DLPI ) is a numerical model that quantifies soil erodibility potential. This paper aims to design ANN and MLR models that can predict the DLPI of erodible soils in southeastern Nigeria. Feedforward back-propagated ANN models (ANN1, ANN2, ANN3 and ANN4) optimized using different algorithms and output layer activation functions were proposed. To implement the four models, the geotechnical data of the eroding soils, which were used for the DLPI calculation, were utilized. Sixteen geotechnical variables analyzed on the soils were used as input variables. While ANN1 and ANN3 were optimized using scaled conjugate gradient algorithm, ANN2 and ANN4 were optimized using gradient descent algorithm. However, the output layers of the ANN1 and ANN2 were activated using identity function while those for the ANN3 and ANN4 were activated using hyperbolic tangent function. The results of the four models showed high performance with determination coefficient (R 2 ) values of 0.971 (ANN1), 0.998 (ANN2), 0.987 (ANN3), and 0.992 (ANN4). Further, low modeling errors were detected from the residual plots, sum of square errors (0.000-0.024), and relative errors (0.003-0.080); thus, confirming the reliability of the models. The MLR gave a perfect model for the DLPI prediction, with its multiple correlation coefficient = 1.000, R 2 = 1.000, R 2 adjusted = 1.000, and standard error of estimate = 0.000. Therefore, it suffices to say that the ANN and MLR algorithms are suitable for DLPI prediction.

Stature estimation using the sacrum in a Thai population.

Stature is an essential component of biological profile analysis since it determines an individual's physical identity. Long bone dimensions are generally used to estimate the stature of skeletal remains; however, non-long bones such as the sternum, cranium, and sacrum may be necessary for some forensic situations. This study aimed to generate a regression equation for stature estimation of the skeletal remains in the Thai population. Ten measurements of the sacrum were measured from 200 dry sacra. The results revealed that the maximum anterior breadth (MAB) provided the most accurate stature prediction model among males (correlation coefficient [r]=0.53), standard error of estimation (SEE=5.94 cm), and females (r=0.48, SEE=6.34 cm). For the multiple regression model, the best multiple regression models were stature equals 41.2+0.374 (right auricular surface height [RASH])+1.072 (anterior-posterior outer diameter of S1 vertebra corpus [APOD])+0.256 (dorsal height [DH])+0.417 (transverse inner diameter of S1 vertebra corpus [TranID])+0.2 (MAB) with a SEE of 6.42 cm for combined sex. For males, stature equals 63.639+0.478 (MAB)+0.299 (DH)+0.508 (APOD) with a SEE of 5.35, and stature equals 75.181+0.362 (MAB)+0.441 (RASH)+0.132 (maximum anterior height [MAH]) with a SEE of 5.88 cm for females. This study suggests that regression equations derived from the sacrum can be used to estimate the stature of the Thai population, especially when a long bone is unavailable.

Longitudinal concordance of body composition and anthropometric assessment by a novel smartphone application across a 12-week self-managed weight loss intervention.

Smartphone applications (SPA) now offer the ability to provide accessible in-home monitoring of relevant individual health biomarkers. Previous cross-sectional validations of similar technologies have reported acceptable accuracy with high-grade body composition assessments; this research assessed longitudinal agreement of a novel SPA across a self-managed weight loss intervention of thirty-eight participants (twenty-one males, seventeen females). Estimations of body mass (BM), body fat percentage (BF%), fat-free mass (FFM) and waist circumference (WC) from the SPA were compared with ground truth (GT) measures from a dual-energy X-ray absorptiometry scanner and expert technician measurement. Small mean differences (MD) and standard error of estimate (SEE) were observed between method deltas (ΔBM: MD = 0·12 kg, SEE = 2·82 kg; ΔBF%: MD = 0·06 %, SEE = 1·65 %; ΔFFM: MD = 0·17 kg, SEE = 1·65 kg; ΔWC: MD = 1·16 cm, SEE = 2·52 cm). Concordance correlation coefficient (CCC) assessed longitudinal agreement between the SPA and GT methods, with moderate concordance (CCC: 0·55-0·73) observed for all measures. The novel SPA may not be interchangeable with high-accuracy medical scanning methods yet offers significant benefits in cost, accessibility and user comfort, in conjunction with the ability to monitor body shape and composition estimates over time.

Kibria–Lukman estimator for the Conway–Maxwell Poisson regression model: Simulation and applications

The Conway–Maxwell Poisson (COMP) regression model is one of the count data models to account for over– and under–dispersion. In regression analysis, when the explanatory variables are correlated, when there is multicollinearity problem, this inflates the standard error of the maximum likelihood estimates. The Kibria–Lukman estimator was provided to handle the effect of multicollinearity in the linear regression model. Therefore, we proposed to extend this estimator to the COMP model to overcome this problem in the COMP model. The proposed estimator mitigates the adverse effect of multicollinearity on the standard error of the estimates. We used the mean squared error (MSE) as the performance assessment criterion to assess the performance of the proposed estimator and others. Also, we compared the proposed estimator theoretically with other estimators (the ridge and Liu estimators). We employed a simulation study and two life applications to study the performance of the proposed estimator. The simulation study and applications results showed the superiority of the proposed estimator because the MSE of the proposed estimator is smaller than the other estimators.