Morphological investigation of low back erector spinae muscle: Historical data populations

Accurate and reliable low back morphological data such as the cross-sectional area (CSA) of the erector spinae muscle (ESM) is vital for biomechanical modeling of the lumbar spine to estimate spinal loading and enhance the understanding of injury mechanisms. The objective of the present study is to enhance the current database regarding ESM sizes by studying with larger sample sizes, collecting data from live subjects, using high resolution MRI scans, using computerized, reliable, and repeatable measurement techniques, and analyzing data from three inter-vertebral disc (IVD) levels for both genders. A total of 163 subjects (82 males and 81 females) were included in the study. CSAs of both right and left ESMs were measured from axial-oblique MRI scans using architectural design software. The average CSA of the ESM was 23.50, 24.22, and 24.33 cm² for females and 30.00, 28.28, and 24.60 cm² for males at the L3/L4, L4/L5, and L5/S1 levels, respectively. Results agree with some studies, but generally larger than most previous studies, possibly due to differences in sampling (sample size, subject characteristics: age, anthropometrics, cadavers, etc.), measurement techniques (scanning technology, scanning plane, scanning posture, different IVD levels), or muscle definitions.Relevance to industryLifting tasks are very common in occupational settings and associated with low back pain. Accurate and reliable low back muscle size data is of importance to produce more efficient low back biomechanical models to better understand the loading mechanism in lifting tasks and to minimize low back pain risk regarding the lifting task. However, available low back muscle size data are quite limited. This study fills part of this gap by providing data from a large sample population of live subjects, multiple levels, both genders, high resolution MRI scans, reliable and repeatable measurement technique. The updated low back muscle size data presented in this paper can be used by biomechanical modelers to improve current low back biomechanical models.     

Development of factor-score-based models to explain and predict maximal box-lifting performance

Abstract

The objectives of the study were threefold: (1) to develop factor-score-based models to predict maximum mass on a box-lifting task using multiple regressions; (2) to compare predictive and explanatory powers of factor-score-based models to models derived from data-level variables; and (3) to apply these findings to ergonomic research and practical problem-solving situations. Forty-eight volunteers (25 women and 23 men) completed a maximal box-lifting task and a maximal isoinertial lifting test on an Incremental Lifting Machine (ILM). Dynamic data collected during isoinertial testing were summarized into 32 lift parameters, and then subjected to principal components analyses using the ‘FACTOR PROCEDURE’ from the Statistical Analysis System (SAS). Factor scores were calculated for each participant on each of the four factors comprising the final solution, and multiple regression equations for men, women and combined data were generated using the ‘GENERAL LINEAR MODELS’ procedure from SAS. Results revealed that prediction of box-lifting performance was optimized when regression equations were developed using numerous data-level variables as predictors, i.e., all 32 lift parameters and ILM mass. In comparison, explanation was enhanced but predictive capabilities were reduced when linear models were formed using ILM mass and the factor scores derived from analyses of isoinertial lifting. The use of variables loading on the factors gave slightly increased predictive power than did the factor-score-based models. Similar trends in predictive and explanatory powers appeared when the data were analysed according to gender. Ergonomic applications of factor-score-based models were discussed with regard to ongoing research as well as to practical problem-solving situations. It was concluded that the advantages and usefulness of factor-score-based models warranted their inclusion in future investigations of lifting performance.     

Comparison of IRS-IB LISS-IIA pixel array sizes for estimating suspended solids concentration in Hussain Sagar lake,Hyderabad, India—a statistical approach

The objective of the study is to select the best possible array size of Indian Remote Sensing Satellite (IRS-IB) linear imaging self scanning (LISS-IIA) digital data for the estimation of the suspended solids concentration on a surface water body. For this purpose a lake namely Hussain Sagar in Hyderabad (India) has been considered. The lake water samples were collected on 21 February 1992 in concurrence with the date of IRS-IB overpass. These water samples have been analysed to determine the suspended solids concentration at predetermined sample locations. Different pixel array sizes of IRS-IB LISS-IIA digital data has been analysed for the selection of the size of the pixel array for the estimation of water quality variables. This selection has been conducted by using various statistical methods such as analysis of variance, paired t-test and linear regression techniques. Analysis of variance and paired t-test are basically used for the selection of minimum pixel array size and linear regression techniques have been used for the selection of the best favourable band and pixel array for the estimation of suspended solids concentration. The relations between digital data and measured values of suspended solids concentrations have been quantified using simple linear and multiple regression. The possible combinations of bands, i.e., model 1, model 2 are developed. From possible combinations model 1 has been chosen for the estimation of suspended solids concentration based on the highest coefficient of determination (R ²) lowest standard error of estimate and F-ratio (four times greater than critical F-ratio (F_cr). Based on the results of this study it is observed that the statistical approach has a strong potential for the application of remote sensing data for quantification of suspended solids concentration.     

Calculating adjusted R(2) measures for Poisson regression models

In regression models not only the parameter estimates and significances of explanatory variables are of interest, but also the degree to which variation in the dependent variable can be explained by covariates. In recent publications, an R(2) measure based on deviance was recommended for Poisson regression models, one of the most frequently used modelling tools in epidemiological studies. However, when sample size is small relative to the number of covariates in the model, simple R(2) measures may be seriously inflated and may need to be adjusted according to the number of covariates in the model. We present a SAS-macro that calculates adjustments for the R(2) measures in Poisson regression models based on log-likelihood and on sums of squares. The proposed measures are applied to real data sets and their performance is discussed.     

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard’s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian Journal of Statistics 28, 3–32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian Journal of Probability and Statistics 19, 13–31]. We show that, for our model, Skovgaard’s adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed.     

Anthropometric variability, equipment usability and musculoskeletal pain in a group of nurses in the Western Cape

This study examined the anthropometry and anthropometric fit of a group of nurses in Western Cape private hospitals. Anthropometric variables were measured using a sample of nurses and a correlation matrix generated. All nurses were given a questionnaire concerned with operational problems in the work environment and musculoskeletal pain. The nurses reported numerous problems in the working environment, including lumbar backache, inadequate space and equipment that caused bodily discomfort. There were consistent, statistically significant associations between the frequency of occurrence of these problems and the anthropometric data indicating that the problems were caused or amplified by body size variability and were not simply general usability problems which would affect all nurses irrespective of their body dimensions.     

Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

This article explores a non-linear partial least square (NLPLS) regression method for bamboo forest carbon stock estimation based on Landsat Thematic Mapper (TM) data. Two schemes, leave-one-out (LOO) cross validation (scheme 1) and split sample validation (scheme 2), are used to build models. For each scheme, the NLPLS model is compared to a linear partial least square (LPLS) regression model and multivariant linear model based on ordinary least square (LOLS). This research indicates that an optimized NLPLS regression mode can substantially improve the estimation accuracy of Moso bamboo (Phyllostachys heterocycla var. pubescens) carbon stock, and it provides a new method for estimating biophysical variables by using remotely sensed data.     

Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network

When constructing classification and prediction models, most researchers used genetic algorithm, particle swarm optimization algorithm, or ant colony optimization algorithm to optimize parameters of artificial neural network models in their previous studies. In this paper, a brand new approach using Fruit fly optimization algorithm (FOA) is adopted to optimize artificial neural network model. First, we carried out principal component regression on the results data of a questionnaire survey on logistics quality and service satisfaction of online auction sellers to construct our logistics quality and service satisfaction detection model. Relevant principal components in the principal component regression analysis results were selected for independent variables, and overall satisfaction level toward auction sellers’ logistics service as indicated in the questionnaire survey was selected as a dependent variable for sample data of this study. In the end, FOA-optimized general regression neural network (FOAGRNN), PSO-optimized general regression neural network (PSOGRNN), and other data mining techniques for ordinary general regression neural network were used to construct a logistics quality and service satisfaction detection model. In the study, 4–6 principal components in principal component regression analysis were selected as independent variables of the model. Analysis results of the study show that of the four data mining techniques, FOA-optimized GRNN model has the best detection capacity.     

Enhancing resolution along multiple imaging dimensions using assorted pixels

Multisampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green, and blue spectral filters found in most solid-state color cameras is one example of multisampled imaging. We briefly describe how multisampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and, hence, image quality). One can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. Hence, multisampled images can be better interpolated using local structural models that are learned offline from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image intensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are 1) traditional color imaging with a mosaic of color filters, 2) high dynamic range monochrome imaging using a mosaic of exposure filters, and 3) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.     

Seeing Human Weight from a Single RGB-D Image

Human weight estimation is useful in a variety of potential applications, e.g., targeted advertisement, entertainment scenarios and forensic science. However, estimating weight only from color cues is particularly challenging since these cues are quite sensitive to lighting and imaging conditions. In this article, we propose a novel weight estimator based on a single RGB-D image, which utilizes the visual color cues and depth information. Our main contributions are three-fold.First, we construct the W8-RGBD dataset including RGB-D images of different people with ground truth weight. Second,the novel sideview shape feature and the feature fusion model are proposed to facilitate weight estimation. Additionally, we consider gender as another important factor for human weight estimation. Third, we conduct comprehensive experiments using various regression models and feature fusion models on the new weight dataset, and encouraging results are obtained based on the proposed features and models.     

水稻叶面积指数的多光谱遥感估算模型研究

LAI是生态系统研究中最重要的结构参数之一,它是估计多种植冠功能过程的重要参数。通过两年的水稻田间试验,使用美国ASD背挂式野外光谱辐射仪(ASDFieldSpec),获取1999～2000年两年晚稻整个生育期的光谱数据,采用计算机测算图斑面积法测定LAI;根据已有的卫星传感器通道波段(MSS、RBV、SPOT、TM、CH)和它们的组合(比值植被指数、归一化差植被指数),以及具有物理意义的光谱区域(蓝区、绿区、黄边、红光吸收谷、红边、紫区、可见光区、近红外区、全部波段)等共有27个变量构建多光谱变量组,采用5个单变量线性与非线性拟合模型,用1999年试验数据为训练样本,建立水稻LAI的多光谱遥感估算模型。结果表明:适用于水稻LAI估算的多光谱变量是植被指数变量好于波段变量;RVI与NDVI比较,RVI好于NDVI。用2000年试验数据作为测试样本数据,对其精度进行评价和验证,非线性模型的精度高于线性模型的精度,其中以SPOT3/SPOT2为变量的对数模型,拟合R2与预测R2达到了最大,其RMSE和相对误差(%)为最低,因此,认为它是估算LAI的最佳模型。
     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data

Mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 233 field sample plots were estimated from various canopy height and canopy density metrics— derived by means of a small-footprint laser scanner over young and mature forest stands— using ordinary least-squares (OLS) regression analysis, seemingly unrelated regression (SUR), and partial least-squares (PLS) regression. The sample plots were distributed systematically throughout two separate inventory areas with size 1000 and 6500 ha, respectively. The plots were divided into three predefined strata. Separate regression models were estimated for each inventory as well as common models utilizing the plots of both inventories simultaneously. In the models estimated by combining data from the two areas, the statistical effect of inventory was found to be significant (p<0.05) in the mean height models only. A total of 115 test stands and plots with size 0.3-11.7 ha were used to validate the estimated regression models. The bias and standard deviations (parenthesized) of the differences between predicted and ground reference values of mean height, dominant height, mean diameter, stem number, basal area, and volume were −5.5% to 4.7% (3.1-7.3%), −6.0% to 0.4% (2.9-8.2%), −0.2% to 7.9% (5.5-15.8%), −21.3% to 12.5% (13.4-29.3%), −7.3% to 8.4% (7.1-13.6%), and −3.9% to 10.1% (8.3-14.9%), respectively. It was revealed that only minor discrepancies occurred between the three investigated estimation techniques. None of the techniques provided predicted values that were superior to the other techniques over all combinations of strata and variables.     

Age,gender and side-stratified grip strength norms and related socio-demographic factors for 20–80 years Iranian healthy population: Comparison with consolidated and international norms

As an index for many adverse health outcomes, normative values on handgrip strength are established for many populations. The aim of this study was to establish handgrip strength (HGS) norms for the Iranian population and to compare them with other existing norms. Related variables affecting HGS were also determined in order to provide appropriate prediction models. The sample consisted of 4282 Iranian 20–80 years adults; divided into 5-year intervals, male/female and dominant/non-dominant hand. Results were compared to consolidated data and those of some other countries. To ensure a valid and comparable dataset, HGS was measured using the JAMAR® hydraulic dynamometer following the standardized procedure. Hand length, palm length, palm width, forearm length, wrist circumference, forearm circumference, height and weight were measured, and BMI was calculated. HGS norms for Iranian adults were established. Inverted U-shaped lifespan profiles were found with mean maximum values of about 53 kg for males (35–39 years) and 31 kg for females (40–44 years). Two regression models (by hand dominance) were developed. The mean values of HGS in Iran were weaker than consolidated norms but greater than in some Asian countries. Applying normative data specific to each population is more accurate than international or multinational norms. It is recommended to investigate the causes of accelerated age-related decline in HGS of Iranian elders in future studies.     

Website design attributes for retrieving health information by older adults: an application of architectural criteria

The objectives of this research were to identify design attributes to develop easy-to-use websites for older adults. Forty-one males and 58 females (age range 58–90) were asked to retrieve information on a health-related topic from the NHS Direct and Medicdirect websites, and were asked to fill in a website evaluation questionnaire. An exploratory factor analysis of data identified navigation/search usability, link usability, usefulness and colour as important dimensions of a senior-friendly website. A two-stage, three-component regression model with these dimensions as predictor variables and the satisfaction level in using a website as the dependent variable has been proposed.     

A neural network-based scheme coupled with the RPV model inversion package

A hybrid scheme for the inversion of the Rahman-Pinty-Verstraete (RPV) model is presented. It combines the inversion technique described by Lavergne et al. (Lavergne, T., Kaminski, T., Pinty, B., Taberner, M., Gobron, N., Verstraete, M.M., Vossbeck, M., Widlowski, J.L., Giering, R. (2007). Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sensing of Environment, 107, 362-375.) and a multilayer backpropagation feedforward neural network. The RPV inversion package is applied to a sample set of pixels within the satellite scene. Subsequently the pairs of bidirectional reflectance factors (BRF) and model parameters estimated from the sample set of pixels are used to train the neural network. Since the mathematical formulation of the RPV model is embedded in these training data variables, the neural network can efficiently retrieve the model parameters for the whole satellite scene. This scheme has been tested for a MISR L2 BRF scene, MISR L1B2-derived BRF data corresponding to two different dates and a mosaic of MISR L2 BRF scenes acquired over Southern Africa covering a large extent of Miombo woodland. The results show this strategy retrieves the RPV model parameters and uncertainties with high accuracy and considerable speed over large areas.     

Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design

Logistic regression models are frequently used in epidemiological studies for estimating associations that demographic, behavioral, and risk factor variables have on a dichotomous outcome, such as disease being present versus absent. After the coefficients in a logistic regression model have been estimated, goodness-of-fit of the resulting model should be examined, particularly if the purpose of the model is to estimate probabilities of event occurrences. While various goodness-of-fit tests have been proposed, the properties of these tests have been studied under the assumption that observations selected were independent and identically distributed. Increasingly, epidemiologists are using large-scale sample survey data when fitting logistic regression models, such as the National Health Interview Survey or the National Health and Nutrition Examination Survey. Unfortunately, for such situations no goodness-of-fit testing procedures have been developed or implemented in available software. To address this problem, goodness-of-fit tests for logistic regression models when data are collected using complex sampling designs are proposed. Properties of the proposed tests were examined using extensive simulation studies and results were compared to traditional goodness-of-fit tests. A Stata ado function svylogitgof for estimating the F-adjusted mean residual test after svylogit fit is available at the author's website http://www.people.vcu.edu/~kjarcher/Research/Data.htm.     

Regression models for estimating product life cycle cost

Product life cycle cost (LCC) is defined as the cost that is incurred in all stages of the life cycle of a product, including product creation, use and disposal. In recent years, LCC has become as crucial as product quality and functionality in deciding the success of a product in the market. In order to estimate LCC of new products, researchers have employed several (parametric) regression analysis models and artificial neural networks (ANN) on historical life cycle data with known costs. In this article, we conduct an empirical study on performance of five popular non-parametric regression models for estimating LCC under different simulated environments. These environments are set by varying the number of cost drivers (independent variables), the size of sample data, the noise degree of sample data, and the bias degree of sample data. Statistical analysis of the results recommend best LCC estimation models for variable environments in stages of the product life cycle. These findings are validated with real-world data from previous work.     

A semi-supervised regression model for mixed numerical and categorical variables

In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.