Fitting Degree Of Model Research Articles

GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Abstract. A Mw=6.5 earthquake struck the Jiuzhaigou region of Sichuan Province, China, at 21:19 LT on Tuesday, 8 August 2017, and triggered a large number of landslides. For mitigating the damages of earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region, a comprehensive landslide susceptibility mapping was attempted with an integrated weighted index model by combining the frequency ratio and the analytical hierarchy process approaches under a GIS-based environment in the heavily earthquake-affected Zhangzha town of the Jiuzhaigou region. For this purpose, a total number of 842 earthquake-triggered landslides were visually interpreted and located from Sentinel-2A images acquired before and after the earthquake at first, and then the recognized landslides were randomly split into two groups to establish the earthquake-triggered landslide inventory, among which 80 % of the landslides were used for training the integrated model and the remaining 20 % for validation. Nine landslide controlling factors were considered including slope, aspect, elevation, lithology, distance from faults, distance from rivers, land use–land cover, normalized difference vegetation index and peak ground acceleration. The frequency ratio was utilized to evaluate the contribution of each landslide controlling factor to landslide occurrence, and the analytical hierarchy process was used to analyse the mutual relationship between landslide controlling factors. Finally, the landslide susceptibility map was produced by using weighted overlay analysis. Furthermore, an area under the curve approach was adopted to comprehensively evaluate the performance of the integrated weighted index model, including the degree of model fit and model predictive capability. The results demonstrated the reliability and feasibility of the integrated weighted index model in earthquake-triggered landslide susceptibility mapping at a regional scale. The generated map can help engineers and decision makers assess and mitigate hazards of the earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region.

Quantification of computational geometric congruence in surface-based registration for spinal intra-operative three-dimensional navigation.

Background ContextComputer-assisted navigation (CAN) may guide spinal instrumentation, and requires alignment of patient anatomy to imaging. Iterative closest-point (ICP) algorithms register anatomical and imaging surface datasets, which may fail in the presence of geometric symmetry (congruence), leading to failed registration or inaccurate navigation. Here we computationally quantify geometric congruence in posterior spinal exposures, and identify predictors of potential navigation inaccuracy.MethodsMidline posterior exposures were performed from C1-S1 in four human cadavers. An optically-based CAN generated surface maps of the posterior elements at each level. Maps were reconstructed to include bilateral hemilamina, or unilateral hemilamina with/without the base of the spinous process. Maps were fitted to symmetrical geometries (cylindrical/spherical/planar) using computational modelling, and the degree of model fit quantified based on the ratio of model inliers to total points.Geometric congruence was subsequently assessed clinically in 11 patients undergoing midline exposures in the cervical/thoracic/lumbar spine for posterior instrumented fusion.ResultsIn cadaveric testing, increased cylindrical/spherical/planar symmetry was seen in the high-cervical and subaxial cervical spine relative to the thoracolumbar spine (p<0.001). Extension of unilateral exposures to include the ipsilateral base of the spinous process decreased symmetry independent of spinal level (p<0.001).In clinical testing, increased cylindrical/spherical/planar symmetry was seen in the subaxial cervical relative to the thoracolumbar spine (p<0.001), and in the thoracic relative to the lumbar spine (p<0.001). Symmetry in unilateral exposures was decreased by 20% with inclusion of the ipsilateral base of the spinous process.ConclusionsGeometric congruence is most evident at C1 and the subaxial cervical spine, warranting greater vigilance in navigation accuracy verification. At all levels, inclusion of the base of the spinous process in unilateral registration decreases the likelihood of geometric symmetry and navigation error. This work is important to allow the extension of line-of-sight based registration techniques to minimally-invasive unilateral approaches.

Preparation of Rhus chinensis total phenolic acid pellets by extrusion-spheronisation method

Extrusion-spheronisation method was used to prepare Rhus chinensis total phenolic acid pellets. The formula and preparation of R. chinensis total phenolic acid pellets were optimized. The formulas( drug loading capacity,diluent,wetting agent and anti-sticking agent) were determined by the single factor test with yield,appearance and performance as the indexes. The preparation was optimized by Box-Behnken design and response surface method,with the rate of extrusion,rate of spheronization and time of spheronization as the independent variables and the overall desirability value of yield,friability and roundness as the dependent variables. The optimal formula of pellets was as follows: drug loading capacity 28. 7%,MCC-lactose 9 ∶1,silicon dioxide as anti-sticking agent,and 60% ethanol as wetting agent. The optimal preparation was determined as follows: the rate of extrusion was 43 r·min-1,the rate of spheronization was 1 800 r·min-1,and the time of spheronization was 4 min. The absolute deviation between predicted value and estimated value under the conditions was less than 5. 0%,with a high degree of model fit. The preparation parameters obtained were accurate,reliable and reproducible. Under scanning electron microscopy( SEM),R. chinensis total phenolic acid pellets were uniform in diameter,round and smooth. The optimal formulation and process are stable and feasible for preparing R. chinensis total phenolic acid pellets.

Model of radial basis functions based on surface slope for optical freeform surfaces.

The model fitting degree of optical freeform surfaces is of utmost design importance. We develop a model with radial basis functions based on the surface slope (RBF-slope) for optical freeform surfaces with asymmetric structures. The RBF-slope model improves the basis-function distribution for circular apertures and establishes a relationship between shape factor and local surface slope, which provides the model with better fitting ability than the conventional RBF model (RBF-direct); fitting experiments for off-axis conic surfaces, "bumpy" paraboloids, and the design of a single mirror magnifier demonstrate the efficacy of our approach. Our method can effectively improve aberration balancing of optical freeform surfaces, resulting in high-quality imaging.

GP.01 Quantification of computational geometric congruence in surface-based registration for spinal intra-operative three-dimensional navigation

Background: Computer-assisted navigation (CAN) may guide spinal instrumentation, and requires alignment of patient anatomy to imaging. Iterative-Closest-Point algorithms register anatomical and imaging datasets, which may fail in the presence of significant geometric congruence leading to inaccurate navigation. We computationally quantify geometric congruence in posterior spinal exposures, and identify predictors of potential navigation inaccuracy. Methods: Midline posterior exposures were performed from C1-S1 in four human cadavers. An optically-based CAN generated surface maps of the posterior elements at each level. Maps were reconstructed to include bilateral hemilamina, or unilateral hemilamina with/without the base of the spinous process. Maps were fitted to symmetrical geometries (cylindrical/spherical/planar) using computational modelling, and the degree of model fit quantified. Results: Increased cylindrical/spherical/planar symmetry was seen in the subaxial cervical spine relative to the high-cervical and thoracolumbar spine (p&lt;0.001). Inclusion of the base of the spinous process decreased symmetry independent of spinal level (p&lt;0.001). Registration with bilateral vs. unilateral hemilamina did not significantly reduce geometric symmetry. Conclusions: Geometric congruence is most evident at C1 and the subaxial cervical spine, warranting greater vigilance in navigation accuracy verification. At all levels, inclusion of the base of the spinous process in unilateral registration decreases the likelihood of geometric symmetry and navigation error.

Uncertainty characterization in the retrieval of an atmospheric point release

The study proposes a methodology in a recent inversion technique, called as Renormalization, to characterize the uncertainties in the reconstruction of a point source. The estimates are derived for measuring the inversion error, the degree of model fit towards measurements (model determination coefficient) and the confidence intervals for the retrieved point source parameters (mainly, location and strength). The inversion error is reflected through an angular estimate which measures the deviation between the measured and predicted concentrations. The uncertainty estimation methodology is evaluated for point source reconstruction studies, using real measurements from two field experiments, known as Fusion Field Trials 2007 (FFT07) in flat terrain and Mock Urban Setting Test (MUST) in urban like terrain. In FFT07 and MUST experiments, the point source location is retrieved with an average Euclidean distance of 22 m and 15 m respectively. The source strength is retrieved, on average, within a factor of 1.5 in both the datasets. The inversion error is observed as 24o and 21o in FFT07 and MUST experiment, respectively. The 95% confidence interval estimates show that the uncertainty in the retrieved parameters is relatively large in approximately 50% FFT07 and 30% MUST trials in spite of their closeness towards true source parameters. For a comparative analysis, the interval estimates are also compared with a more general method of uncertainty estimation, Residual Bootstrap Sampling. In most of the trials, we observed that the intervals estimates with the present method are comparable (within 10–20% variations) to bootstrap estimates. The proposed methodology provides near accurate and computationally efficient uncertainty estimates in comparison to the methods based on Hessian and sampling procedures.

A Value Classification of Electronic Product Reviews Based on Maximum Entropy

Reviews of electronic product are important resources for manufacturers to gather feedbacks from customers. They provide an important reference for customers to make a decision on product purchase. Electronic product reviews with high quality is valuable, and reviews analysis is becoming an open issue. Based on the binary feature function, a maximum entropy classification of electronic product reviews is proposed to identify the valuable reviews. Considering the simpleness of the theme in reviews, a text classifier integrated with the features of descriptive anaphor is provided to enhance the model's fitting degree. Furthermore, the feature library is constructed by the Term frequency-inverse document frequency (TF-IDF) algorithm to locate the description, and the descriptive anaphors are identified by means of the minimum Inflectional phrase (IP) parsing tree. The experiment shows the proposed classifier has the relatively high stability and precision, which makes the reviews much more usable for manufacturers to make product strategy and for customers to make a decision.

Application of multiple seasonal autoregressive integrated moving average model in predicting the mumps incidence

To establish multiple seasonal autoregressive integrated moving average model(ARIMA) according to mumps disease incidence in Hunan province, and to predict the mumps incidence from May 2015 to April 2016 in Hunan province by the model. The data were downloaded from "Disease Surveillance Information Reporting Management System" in China Information System for Disease Control and Prevention. The monthly incidence of mumps in Hunan province was collected from January 2004 to April 2015 according to the onset date, including clinical diagnosis and laboratory confirmed cases. The predictive analysis method was the ARIMA model in SPSS 18.0 software, the ARIMA model was established on the monthly incidence of mumps from January 2004 to April 2014, and the date from May 2014 to April 2015 was used as the testing sample, Box-Ljung Q test was used to test the residual of the selected model. Finally, the monthly incidence of mumps from May 2015 to April 2016 was predicted by the model. The peak months of the mumps incidence were May to July every year, and the secondary peak months were November to January of the following year, during January 2004 to April 2014 in Hunan province. After the data sequence was handled by smooth sequence, model identification, establishment and diagnosis, the ARIMA(2,1,1) × (0,1,1)(12) was established, Box-Ljung Q test found, Q=8.40, P=0.868, the residual sequence was white noise, the established model to the data information extraction was complete, the model was reasonable. The R(2) value of the model fitting degree was 0.871, and the value of BIC was -1.646, while the average absolute error of the predicted value and the actual value was 0.025/100 000, the average relative error was 13.004%. The relative error of the model for the prediction of the mumps incidence in Hunan province was small, and the predicting results were reliable. Using the ARIMA(2,1,1) ×(0,1,1)(12) model to predict the mumps incidence from April 2016 to May 2015 in Hunan province, the peak months of the mumps incidence were May to July, and the secondary peak months were November to January of the following year, the incidence of the peak month was close to the same period. The ARIMA(2,1,1)×(0,1,1)(12) model is well fitted the trend of the mumps disease incidence in Hunan province, it has some practical value for the prevention and control of the disease.

Research of foreign fibers in cotton yarn defect model based on regression analysis

The detecting of foreign fiber may not be very effective, particularly around the detection zone where many types of foreign fibers may coexist. In order to eliminate the fibers more effectively, a model has been established to detect foreign fiber faults in yarn. Relevant data were collected through investigation of a number of standard samples, with the length and area of foreign fibers as the independent variables, and the number of defects as the dependent variable, which were combined using linear regression theory to establish a regression equation for different fiber defects. The equations to find the regression coefficients, which include the model fitting degree, the Durbin–Watson value, the standard error, and the Cook distance, were rigorously tested, and the regression equation was eventually compiled to produce the yarn faults model. When the fiber detection equipment recognizes fibers with a foreign profile, the calculated profile fiber size is used in a corresponding regression equation which obtains the defect points and compares them with each other, so that foreign fibers which are potentially more dangerous can be identified and preferentially eliminated. In order to verify the model, spinning experiments are performed. The actual defects from the experiment are compared with the predicted theoretical defects from the equation, and the prediction accuracy was found to reach more than 95%, showing that the foreign fiber yarn faults model, which lays a theoretical foundation for foreign fiber detection, is accurate and effective.

Estimation of grass yield in large region on geographically weighted regression model

Abstract. The grass yield embodies its productivity，and also is ground for developing animal husbandry production management. Now the remote sensing technology has been becoming an efficient and feasible mean to estimate the grass yield. In the study, the thought about Geographically Weighted Regression (GWR) was involved in estimating the grass yield. The special characteristics of samples measured on field were considered, and then each sample has a local function covering area around. And the parameters for the function are decided by the weighted function which is associated with the spatial distance between the sample and others around. GWR is a good solution to the model without spatial stationarity, as a consequence a significant model-fitting degree comes out. Based on GWR model an ideal production of grassland can be estimated. In this study, Qinghai province, about 0.72 million square kilometres, was taken as an example. The province is an important one on the Qinghai Tibet Plateau. Here the grassland not only closely relates with the local animal husbandry economy, but also directly affects the regional ecosystem security. Landsat TM data in 2013 and samples on field were used to estimate the production. As input parameters, OSAVI and FVC have high correlation coefficient more than 97% with grass yield. There were 201 samples involved in modelling, and the accuracy is 87.27%, above about 47% than that of multiple linear regression model, a widely used traditional statistic model. Another 220 samples were used to verify the results, and here the accuracy can reach 81.3%. Out results indicated that in 2013 the yield of grass in Qinghai province is 1.018*108 ton. The difference between our data and that from professional sector is less than 10%.

Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha

To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence. EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model. After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83. The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.

Analysis of Shanghai Composite Index Variation Based on Regression Analysis

In this paper, through collecting data of Shanghai Composite Index since 2007, we analyze overall trend of the Shanghai stock market after the financial crisis, and carry on the forecast to the future trend in order to provide a meaningful guidance for people’s investment securities. Because the fitting results of simple regression is not good, we consider the long-term trend, seasonal fluctuations, cyclical fluctuations, irregular variables and other factors. We also add lagged variables and establish an ARIMA model through SPSS statistical analysis software. The fitting degree of model we built is good and the effect of prediction is significant improvement in the analysis.

Dimensional model for estimating factors influencing childhood obesity: path analysis based modeling.

The main objective of this study is to identify and develop a comprehensive model which estimates and evaluates the overall relations among the factors that lead to weight gain in children by using structural equation modeling. The proposed models in this study explore the connection among the socioeconomic status of the family, parental feeding practice, and physical activity. Six structural models were tested to identify the direct and indirect relationship between the socioeconomic status and parental feeding practice general level of physical activity, and weight status of children. Finally, a comprehensive model was devised to show how these factors relate to each other as well as to the body mass index (BMI) of the children simultaneously. Concerning the methodology of the current study, confirmatory factor analysis (CFA) was applied to reveal the hidden (secondary) effect of socioeconomic factors on feeding practice and ultimately on the weight status of the children and also to determine the degree of model fit. The comprehensive structural model tested in this study suggested that there are significant direct and indirect relationships among variables of interest. Moreover, the results suggest that parental feeding practice and physical activity are mediators in the structural model.

Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

This paper aims to develop a novel hybrid model for assessing landslide susceptibility at the regional scale using multisource data to produce a landslide susceptibility map of the Zigui–Badong area near the Three Gorges Reservoir, China. This area is subject to anthropogenic influences because the reservoir's water level fluctuates cyclically between 145 and 175m; in addition, the area suffers from extreme rainfall events due to the local climate. The area has experienced significant and widespread landslide events in recent years. In our study, a novel hybrid model is proposed to produce landslide susceptibility maps using geographical information systems (GIS) and remote sensing. The hybrid model is based on rough set (RS) theory and a support vector machine (SVM). RS theory is employed as an attribute reduction tool to identify the significant environmental parameters of a landslide, and an SVM is used to predict landslide susceptibility. Four data domains were considered in this research: geological, geomorphological, hydrology, and land cover. The original group of 20 environmental parameters and 202 landslides were used as the inputs to produce a landslide susceptibility map. According to the map, 19.7% of the study area was identified as medium- and high-susceptibility zones encompassing 89.5% of the historical landslides. The results indicate high levels of landslide hazard in and around the main inhabited areas, such as Badong County and other towns, as well as in rural residential areas and transportation areas along the Yangtze River and its tributaries. The predicted map indicates a good correlation between the classified high hazard areas and slope failures confirmed in the field. Furthermore, the quality of the proposed model was comprehensively evaluated, including the degree of model fit, the robustness of the model, the uncertainty associated with the probabilistic estimate, and the model prediction skill. The proposed model was also compared with the general SVM, which demonstrated that the hybrid model has superior prediction skill and higher reliability and confirmed the usefulness of the proposed model for landslide susceptibility mapping at a regional scale.

Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance

Measurement invariance (MI) is a pre-requisite for comparing latent variable scores across groups. The current paper introduces the concept of approximate MI building on the work of Muthén and Asparouhov and their application of Bayesian Structural Equation Modeling (BSEM) in the software Mplus. They showed that with BSEM exact zeros constraints can be replaced with approximate zeros to allow for minimal steps away from strict MI, still yielding a well-fitting model. This new opportunity enables researchers to make explicit trade-offs between the degree of MI on the one hand, and the degree of model fit on the other. Throughout the paper we discuss the topic of approximate MI, followed by an empirical illustration where the test for MI fails, but where allowing for approximate MI results in a well-fitting model. Using simulated data, we investigate in which situations approximate MI can be applied and when it leads to unbiased results. Both our empirical illustration and the simulation study show approximate MI outperforms full or partial MI In detecting/recovering the true latent mean difference when there are (many) small differences in the intercepts and factor loadings across groups. In the discussion we provide a step-by-step guide in which situation what type of MI is preferred. Our paper provides a first step in the new research area of (partial) approximate MI and shows that it can be a good alternative when strict MI leads to a badly fitting model and when partial MI cannot be applied.

A Model for the Training Effects in Swimming Demonstrates a Strong Relationship between Parasympathetic Activity, Performance and Index of Fatigue

Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister’s two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R2 = 0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R2 = 0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of ‘Negative Influences’ rather than increases in ‘Positive Influences’. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p = 0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.

On Correlated Errors

On Correlated Errors

Research on Technological Innovation Evolution Based on Self-organization Theory: the Case of China Communication Industry

The technological innovation evolution is a selforganization process. There are complex characteristics in technological innovation system. From the perspective of system theory this paper illuminated the self-organization characteristics of technological innovation system and analyzes the self-organization mechanisms of the innovation process and characteristics of openness, dynamic, nonlinearity, fluctuation and indetermination and constructed the self-organization process model of technological innovation and diffusion then carried on the confirmation with statistical software to the model fitting degree and the model parameters, using the data of China communication market, obtained the quite satisfied result. These models have realized the integration of the natural sciences and the social sciences, and developed a new method for studying on the evolution and diffusion process of the technological innovation and diffusion.

Optimal landslide susceptibility zonation based on multiple forecasts

Environmental and multi-temporal landslide information for an area in Umbria, Italy, was exploited to produce four single and two combined landslide susceptibility zonations. The 78.9 km 2 study area was partitioned in 894 slope units, and the single susceptibility zonations were obtained through linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and by training a neural network (NN). The presence or absence of landslides in the slope units in the period from pre-1941 to 1996 (training set) was used as the dependent variable for the terrain classification. Next, adopting a regression approach, two “optimal” combinations of the four single zonations were prepared. The single and the combined zonations were tested against landslides in the 9-year period from 1997 to 2005 (validation set). Different metrics were used to evaluate the quality of the susceptibility zonations, including degree of model fit, uncertainty in the probability estimates, and model prediction skills. These metrics showed that the degree of model fit was not a good indicator of the model forecasting skills. Zonations obtained through classical multivariate classification techniques (LDA, QDA and LR) produced superior predictions when compared to the NN model, that over fitted the landslide information in the training set. LDA and LR produced less uncertain zonations than QDA and NN. The combined models resulted in a reduced number of errors and in less uncertain predictions; an important result that suggests that the combination of landslide susceptibility zonations can provide “optimal” susceptibility assessments.

Criteria for performance evaluation

AbstractUsing a cognitive task (mental calculation) and a perceptual-motor task (stylized golf putting), we examined differential proficiency using the CWS index and several other quantitative measures of performance. The CWS index (Weiss &amp; Shanteau, 2003) is a coherence criterion that looks only at internal properties of the data without incorporating an external standard. In Experiment 1, college students (n = 20) carried out 2- and 3-digit addition and multiplication problems under time pressure. In Experiment 2, experienced golfers (n = 12), also college students, putted toward a target from nine different locations. Within each experiment, we analyzed the same responses using different methods. For the arithmetic tasks, accuracy information (mean absolute deviation from the correct answer, MAD) using a coherence criterion was available; for golf, accuracy information using a correspondence criterion (mean deviation from the target, also MAD) was available. We ranked the performances of the participants according to each measure, then compared the orders using Spearman’s rs. For mental calculation, the CWS order correlated moderately (rs =.46) with that of MAD. However, a different coherence criterion, degree of model fit, did not correlate with either CWS or accuracy. For putting, the ranking generated by CWS correlated .68 with that generated by MAD. Consensual answers were also available for both experiments, and the rankings they generated correlated highly with those of MAD. The coherence vs. correspondence distinction did not map well onto criteria for performance evaluation.