This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed a kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same, Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting a reference indicator and adopting those statistics corrected for normality.
Least-Squares Analysis* ; Models, Structural ; Sample Size

PURPOSE: Peak urinary flow rate is a widely used parameter in the diagnosis and evaluation of treatment erect of BPH because of its objectiveness and non-in vasiveness. The peak urinary flow rate, however is different with each voided volume in the same patient and tends to decrease even in an asymptomatic man with increasing ages. Therefore we need an adjusted peak urinary flow rate corresponding with the age and voided volume. This adjusted peak urinary flow rate can be used to evaluate the voiding function more easily in the same patient periodically or In the different patient of various voided volume and ages. MATERIALS AND METHODS: Data on age, volume voided and peak urinary flow rate were accumulated from 216 male aged from 10 to 80 who were free of voiding symptoms. All combinations of peak urinary flow rate, age and volume voided were tested for equation of bet fit by the least squares method with search for the equation providing least residual standard deviation with SAS package. RESULTS: When the peak urinary flow rate is defined as a function of age and voided volume, the equation is Q=35.01+0.086A-0.0031A2-1612/V(Q: peak urinary flow rate, A: age, V: voided volume). At the point of population means for volume voided(247.5ml) arid age(35.2) the reference peak urinary flow rate was 27.7m1/sec. Adjusted peak flow rate can be obtained by subtracting the difference between the measured and expected peak flow rate(expected minus measured) from the reference peak flow rate. To make the adjusted peak flow rate obtained easily with measured peak flow rate, age and voided volume nomogram that incorporates the equation has been designed. In our nomogram an adjusted peak flow rate < 19.9 ml/sec or > 1.3 standard deviation below mean should be considered suspicious for obstruction. CONCLUSIONS: Nomogram for adjusted peak flow rate that incorporates the age, voided volume and measured peak flow rate would be satisfactory for clinical use.
Diagnosis ; Humans ; Least-Squares Analysis ; Male* ; Nomograms

Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm^-1, 2 854 cm^-1, 1 623 cm^-1, and 1 535 cm^-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm^-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.
Algorithms ; Animals ; Fourier Analysis ; Least-Squares Analysis ; Machine Learning ; Swine

This article describes methods used to produce a RBRVS (resource-based relative value scales), a common scale from two specialties (internal medicine and general surgery) and explains the newly developed extrapolation process within each specialty. To produce a common scale, we selected six 'same' services as linking services common to both specialties. Then we used the bi-weighted least squares method to locate all the same services on a single, common scale. By using the same method, we tried to extrapolate all the services within each specialty, not by the method of Kelly et al, dividing all the services within the specialty into families (small homogeneous groups of services) to apply charge-based ratios. To compare both methods, we extrapolated all the services of general surgery according to each method. With the correlation analysis to compare both results to American RVUs, we found that general surgery's RVUs from our own extrapolation method turned out to be more highly correlated with American RVUs than from Kelly's extrapolation method. Consequently, extrapolation with bi-weighted least squares method gave reasonable results.
Human ; *Internal Medicine ; Least-Squares Analysis ; *Relative Value Scales

OBJECTIVES: Congenital muscular torticollis, a common disorder that refers to the shortening of the sternocleidomastoid in infants, is sensitive to correction through physical therapy when treated early. If physical therapy is unsuccessful, surgery is required. In this study, we developed a support vector regression model for congenital muscular torticollis to investigate the prognosis of the physical therapy treatent in infants. METHODS: Fifty-nine infants with congenital muscular torticollis received physical therapy until the degree of neck tilt was less than 5degrees. After treatment, the mass diameter was reevaluated. Based on the data, a support vector regression model was applied to predict the prognoses. RESULTS: 10-, 20-, and 50-fold cross-tabulation analyses for the proposed model were conducted based on support vector regression and conventional multi-regression method based on least squares. The proposed methodbased on support vector regression was robust and enabled the effective analysis of even a small amount of data containing outliers. CONCLUSIONS: The developed support vector regression model is an effective prognostic tool for infants with congenital muscular torticollis who receive physical therapy.
Humans ; Infant ; Least-Squares Analysis ; Neck ; Prognosis ; Torticollis

The research of sleep staging is not only the basis of diagnosing sleep related diseases, but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hotspot and made some achievements. Feature extraction and feature classification are two key technologies in automatic sleep staging system. In order to achieve effective automatic sleep staging, we proposed a new automatic sleep staging method which combines the energy features and least squares support vector machines (LS-SVM). Firstly, we used FIR band-pass filter to extract the energy features of Pz-Oz channel sleep electroencephalogram (EEG) signals, and compared them with those from wavelet packet transform method. Then we designed an LS-SVM classifier to realize the automatic sleep stage classification. The research showed that FIR band-pass filter (with the Kaiser window) performed better than wavelet packet transform (WPT) for energy feature extraction just in terms of the data from the Sleep-EDF Database and the LS-SVM classifier (with the RBF Kernel function) designed was good, and the automatic sleep staging method proposed in this paper was better than many similar methods from other studies with an average accuracy of 88.89% and had a very prosperous application future.
Electroencephalography ; Humans ; Least-Squares Analysis ; Sleep Stages ; Support Vector Machine

The quantity of biomass, glucose concentration and ethanol concentration are important parameters in ethanol fermentation. Traditional methods are usually based on samples for off-line measurement, which not only requires multiple instruments for test and analysis but also consumes notable time and effort, and therefore is inconvenient for real-time process control and optimization. In this study, an in-situ detection method based on the near-infrared (NIR) spectroscopy is proposed for measuring the above process parameters in real time. The in-situ measurement is carried out by using an immersion type NIR spectroscopy. A multi-output prediction model for simultaneously estimating the quantity of glucose, biomass and ethanol is established based on a multi-output least-squares support vector regression algorithm. The experimental results show that the proposed method can precisely measure the quantity of glucose, biomass and ethanol during the ethanol fermentation process. Compared to the existing partial-least-squares method for modeling and prediction of individual components, the proposed method could evidently improve the measurement accuracy and reliability.
Ethanol ; Fermentation ; Least-Squares Analysis ; Reproducibility of Results ; Spectroscopy, Near-Infrared

PURPOSE: The aim of this study was to evaluate the influence of polishing methods on the color stability of composite resins. MATERIALS AND METHODS: Two bulk-fill and four conventional resin composites were filled in cylindrical molds (6 mm diameter, 4 mm height) and light-cured. The specimens were stored in 34℃ distilled water for 24 h. Spectrophotometer was used to determine the color value according to the CIE L(*)a(*)b(*) color space. Each group was divided into three groups according to polishing methods (n = 5). Group 1 was control group (Mylar strip group), group 2 was polished with PoGo, and group 3 was polished with Sof-Lex Spiral wheels. Color evaluation was performed weekly for 4 weeks after immersion in 34℃ distilled water. The results were analyzed by generalized least squares method (P < 0.05). RESULTS: Generalized least squares analysis revealed that Sof-Lex Spiral wheels group showed the significantly lower ΔE values compared to PoGo and control group (P < 0.05). The ΔE values of polished group showed the significantly lower than the ΔE values of unpolished group (P < 0.05). Regarding color changes of composite resins, there was no significant difference between the ΔE values of Filtek Z250 and Filtek Z350 XT Universal restorative in all time intervals (P < 0.05). Tetric N-Ceram Bulk Fill showed the significantly lower ΔE values compared to other composite resins in 1, 2, 3 weeks (P < 0.05). CONCLUSION: Within the limitations of this study, polishing methods influence the color stabilities of composite resins. The group polished with Sof-Lex Spiral Wheels showed more resistance to discoloration than group polished with PoGo.
Absorption ; Composite Resins ; Fungi ; Immersion ; Least-Squares Analysis ; Methods ; Water

This study explores the emulsifying material basis of Angelicae Sinensis Radix volatile oil (ASRVO) based on partial least squares (PLS) method and hydrophile-lipophile balance (HLB) value.The turbidity of ASRVO emulsion samples from Gansu,Yunnan,and Qinghai was determined and the chemical components in the emulsion were analyzed by GC-MS.The PLS model was established with the chemical components as the independent variable and the turbidity as the dependent variable and evaluated with indexes R~2X and R~2Y.The chemical components which were in positive correlation with the turbidity were selected and the HLB values were calculated to determine the emulsification material basis of ASRVO.The PLS models for the 81 emulsion samples had high R~2X and R~2Y values,which showed good fitting ability.Seven chemical components,2-methoxy-4-vinylphenol,trans-ligustilide,3-butylidene-1(3H)-isobenzofuranone,dodecane,1-methyl-4-(1-methylethylidene)-cyclohexene,trans-beta-ocimene,and decane,had positive correlation with turbidity.Particularly,the HLB value of 2-methoxy-4-vinylphenol was 4.4,which was the HLB range of surfactants to be emulsifiers and 2-methoxy-4-vinylphenol was positively correlated with turbidity of the ASRVO emulsion samples from the main producing area.Therefore,2-methoxy-4-vinylphenol was the emulsifying material basis of ASRVO.The selected emulsifying substances can lay a foundation for exploring the emulsification mechanism and demulsification solution of ASRVO.
China ; Emulsions ; Least-Squares Analysis ; Oils, Volatile ; Surface-Active Agents

In order to realize rapid and non-destructive identification of the origin of Gardeniae Fructus, a technical method based on hyperspectral imaging technology was established in this study. Spectral information of Gardeniae Fructus samples from eight production origins was acquired from visible NIR(410-990 nm, VNIR) and short wavelength NIR(950-2 500 nm, SWIR) bands based on hyperspectral imaging techniques. The average spectral reflectance within the region of interest was extracted and calculated using the ENVI 5.3 software, resulting in 1 600 sample data. The visible short wavelength infrared band(fused bands) spectral data covering the range 410-2 500 nm were obtained after combining the spectral data of VNIR and SWIR. Data were de-noised by five common preprocessing methods, including multivariate scatter correction, Savitzky-Golay smoothing, standard normal variate, first derivative(FD), and second derivative from VNIR, SWIR, and fused bands(VNIR+SWIR). Partial least squares discriminant analysis, linear support vector classification(LinearSVC), and random forest were used to establish the model for origin identification of Gardeniae Fructus. The results indicated that the identification model of Gardeniae Fructus origin established after FD pretreatment of the spectral data in the fused bands could yield good results. According to the confusion matrix evaluation results, the model prediction set using LinearSVC reached 100% accuracy, so the optimum identification model of Gardeniae Fructus origin was determined as fusion bands-FD-LinearSVC. Therefore, the hyperspectral imaging technology can achieve rapid, nondestructive, and accurate identification of Gardeniae Fructus samples of different origins, which provides a technical reference for the differential detection of Gardeniae Fructus and other Chinese medicines.
Gardenia ; Hyperspectral Imaging ; Fruit ; Least-Squares Analysis ; Technology