COX MULTIVARIATE REGRESSION ANALYSIS OF RECURRENCE FACTORS FOR COLONIC CARCINOMA

Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.

Statistical Evaluation and Regression Analysis of Vehicle Sound Quality

A statistical approach to evaluate the subjective perception of the annoyance caused by the vehicle noise was presented in this paper. After recording the noises of Sanfeng, Huali and Xiali at speeds of 30, 40, 50, 60, 70 and 80 km/h respectively, the annoyance of the vehicle noises was evaluated in the testing room using paired comparison method, and the sound quality metrics and subjective annoyance were then distilled. Loudness, sharpness, roughness, periodicity and impulsiveness were selected for each of the vehicle noises. By correlation analysis method, it can be found that loudness has a higher correlation （0.91） with annoyance than other parameters. Meanwhile, sharpness, periodicity, roughness and impulsiveness have correlation with subjective perception with correlation coefficients being 0.84, -0.82, 0.62 and 0.87, respectively. The result of multiple regression analysis shows that calculated annoyance obtained by the regression equation can explain the perceptual annoyance and the regressed evaluation model is feasible to evaluate the sound quality of vehicle.

Mapping QTL for Categorical Traits with Multivariate Regression

Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presence or absence). It has been shown that such a binary trait also can be analyzed with the simple linear regression, subject to virtually no loss in power compared to the generalized linear model analysis. Binary trait is a special case of a multiple categorical trait (e.g., low, medium or high). We propose a mechanism to decompose a multiple categorical trait into an array of correlated binary variables. The categorical trait turned multiple binary traits are analyzed with a multivariate linear regression method. Turning the problem of categorical trait mapping into that of multivariate mapping allows the exploration of pleiotropic effects of QTL for different categories. Efficiency of the method is verified through a series of simulation experiments.

Operational method for determining bottom hole pressure in mechanized oil producing wells,based on the application of multivariate regression analysis

One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.

Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

Using multivariate adaptive regression splines to develop relationship between rock quality designation and permeability

The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.

Regression Analysis of Nutrient Elements between Soil and Tobacco Leaves and Their Influences on Aroma Quality of Flue-cured Tobacco

This study aimed to analyze the relationship between nutrient elements K, Ca, Mg, Cu, Mn, Zn and Fe in tobacco-planting soils and tobacco leaves from six main tobacco-producing areas, and to investigate the influences of these elements on chemical composition and aroma components in tobacco leaves. Results showed that there were certain relationship between contents of nutrient elements in tobacco-planting soils and contents of corresponding elements in tobacco leaves; various elements exhibited different influences on the aroma quality of flue-cured tobacco. Based on the actual situation of nutrient contents in soils from different tobaccoproducing areas, contents of various elements in tobacco leaves should be regulated by soil fertilization and foliar spraying, thereby improving the aroma quality of flue-cured tobacco.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

On the Regression-Tensor Analysis of the Hardening Process of Metal Coatings

This paper constructs and studies a nonlinear multivariate regression-tensor model for substantiation of necessary/sufficient conditions of optimization of technological calculation of multifactor physical and chemical process of hardening of complex composite media for metal coatings. An adaptive a posteriori procedure for parametric formation of the target quality functional of integrative physical and mechanical properties of the designed metal coating has been proposed. The results of the research may serve as elements of a mathematical language when creating automated design of precision nanotechnologies for surface hardening of complex composite metal coatings on the basis of complex tribological and anticorrosive tests.

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

Quality of life of hospitalized patients after lung cancer operation and analysis of influencing factors

Objective:To explore the current status and influencing factors of quality of life in patients with lung cancer after surgery in a tertiary hospital in Hainan province.Methods:To investigate the influencing factors of quality of life of lung cancer patients after surgery in a tertiary hospital in Hainan province by cross‑sectional survey method.Results:The scores of insomnia,appetite loss,constipation and pain in 186 lung cancer patients after surgery in a tertiary hospital in Hainan Province were significantly higher than the reference value.Multiple linear regression analysis showed that older patients(>60 years)had lower scores in physical function domain(β=-0.193),and female patients had more appetite loss symptoms(β=0.245).Compared with other minority ethnic groups,Han ethnic group had lower scores in role function domain(β=0.179),more severe fatigue symptoms(β=-0.162),and higher general health level(β=0.166).Patients with employee medical insurance had lower scores of emotional function(β=0.194),cognitive function(β=0.281),the lowest score in social function(β=0.188),and severe pain in other parts(β=-0.227).Smokers had less cough symptoms(β=0.175)and more arm and shoulder pain symptoms(β=-0.21)than non‑smokers.Patients with secondhand smoke exposure had lower cognitive function scores(β=-0.158)and more obvious symptoms of oral ulcer(β=0.185).Patients who drank alcohol frequently(drinking frequency>1 time/day)had more severe cough symptoms(β=0.27).Patients with small number of children(0‑1)had milder cough symptoms(β=0.178).Patients who did not understand the disease had obvious symptoms of arm and shoulder pain(β=0.151).Patients with early pathological stage(stageⅠ‑Ⅱ)had more severe shortness of breath(β=-0.159)and pain(β=-0.181).The symptoms of appetite loss were more obvious in patients living in cities(β=0.192).The symptoms of peripheral neuropathy were more obvious(β=0.174).Patients who often consumed pickulated food had severe pain symptoms(β=-0.219),and pain in other parts was obvious(β=-0.149).Male patients had obvious alopecia symptoms(β=-0.306).Conclusion:Age,ethnicity,residence,type of medical insurance,number of children,pathological stage of lung cancer,smoking,second‑hand smoke exposure,alcohol consumption,and frequent consumption of pickled food were related to the quality of life of lung cancer patients in hospital after surgery.Medical staff and family members should pay attention to the emotional communication of patients during the treatment of lung cancer patients in hospital after surgery.Patients should avoid exposure to smoking,alcohol and second‑hand smoke,and reduce consumption of pickled food.

The Method for Optimum Estimation of COVID-19 Variant Type Virus Infection Status Analysis by the Multivariate Analysis Considering the Environmental Variability Impact in Japan

Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.

GROUNDWATER QUALITY AND CONTAMINATION INDEX MAPPING IN CHANGCHUN CITY,CHINA

Groundwater in Changchun City, Jilin Province of China tends to be influenced by human activities. Chemical types of groundwater were detected in both shallow and deep groundwater were: HCO3--Ca2+ and HCO3--Ca2+·Mg2+ or HCO3--Mg2+·Ca2+; SO42--Ca2+ and SO42--Ca2+·Mg2+; Cl--Ca2+; and CO32--Na+. The deteriorations of groundwater quality due to the increase of TDS, NO3-+ NO2-(as Nitrogen) and TH contents have been observed from 1991 to 1998. Scatter analyses showed strong positive correlations between Ca2+, Cl-and NO3-ions and weak negative correlations between the depth of water table and Ca2+, SO42-, Cl-and NO3-ions. A mapping of contaminant index based on Chinese standard of groundwater showed that a large proportion of the groundwater in 1998 was deteriorated by human process. Despite their low values of sodium adsorption ratio (SAR), the most of the sampled wells were not suitable for drinking and agriculture purposes due to higher contents of NO3-, NO2-and Mn2+ ions.

Evaluation of Chinese Cabbage Sensory Quality and Its Relationship with Contents of Main Nutrient Components

In order to establish a comprehensive evaluation system for quality of Chinese cabbage, 28 Chinese cabbage cultivars or inbreeds were selected as ex- perimental materials to identify the raw and cooked taste and determine the nutrient component contents, and the subjective factors （taste） and objective factors （nutrient component contents） influencing the sensory quality of Chinese cabbage were ana- lyzed by multiple regression and path analysis. The results showed that two regres- sion equations were obtained： yr （raw quality）=0.310 3＋0.254 0xl （juiciness）＋0.176 2 x2 （sweetness）＋0.221 6x3 （crispness）＋0.319 9x4 （flavor）; Yc （cooked quality）=0.204 4＋ 0.250 9 xs （residue）＋0.246 9x6 （cooked sweetness）＋0.182 5x7 （softness）＋0.323 lx8 （cooked flavor）. The path analysis results showed that among the sensory indices, the total coefficients of sweetness and juiciness were 0.445 3 and 0.427 6, respec- tively. It indicated they were the main factors affecting the raw quality of Chinese cab- bage, followed by flavor and crispness. Sweetness, with total coefficient of 0.515 2, showed the greatest effect on the cooked quality of Chinese cabbage, followed by residue, cooked flavor and softness. The effects of nutrient component contents on the sensory quality of Chinese cabbage were further analyzed, and the regression equa- tions were obtained： y, =-32.192 0＋0.389 3xw （moisture content）＋1.169 8x~ （soluble sugar content）; Yc = 7.497 1＋0.732 6xs - 5.668 8x, （organic acid content）-2.176 3x~ （crude fiber content）. The standardized regression coefficients of soluble sugar con- tent were all highest with the value of 0.580 7 in the raw quality regression equa- tion and 0.325 0 in the cooked quality one, indicating that among the six nutrient components, the soluble sugar content was the main factor affecting both raw and cooked quality of Chinese cabbage. This study will provide certain basis for quality evaluation, selection and breeding of high-quality Chinese cabbage.

Family empowerment and quality of life of parents raising children with Developmental Disabilities in 78 Japanese families

Objectives: The families of these children experience distress both at the time of diagnosis and afterward.A top priority is to understand family empowerment,family function,and family members' quality of life (QoL) and to effectively support these families in Japan.The objective of this study was to assess the actual conditions of families living with children having DDs and to explore the factors associated with family empowerment and parents' QoL.Methods: We surveyed ninety-three parents (78 mothers,15 fathers) from 78 families which lived with children with DDs in the capital region of Japan.We assessed two main outcomes using the Japanese versions of the following instruments: Family Empowerment Scale (FES),World Health Organization Quality of Life 26 (WHOQOL26),and other six outcomes.Correlation and multiple regression analyses were conducted.Results: No medication,cooperation with child rearing,assistance from a developmental support center,solved problems related to child rearing,and higher scores in Problem Solving contributed to higher FES scores.Higher WHOQOL26 scores were related to being a full-time housewife,higher self-esteem,no developmental support,a broad emotional support network,higher scores in Problem Solving and Role Function,and lower scores in Affective Reaction and General Function.Conclusions: We revealed that family empowerment and QoL of parents rearing children with DDs in Japan were affected by various subscales of family function and other family attributes.Effective interventions for improving family empowerment and QoL should be researched in the future.

多元质量特性预报：MULTIVARIATE回归分析的应用

对现象之间客观存在的因果关系建立回归分析模型,这是实际中较为普遍的做法.在这篇文章中,我们根据MULTIVARIATE回归分析的基本原理,利用从生产现场采集的观测数据,对产品两个质量特性及其五个关键影响因素之间的关系建立了多重多元回归分析方程,为说明MULTIVARIATE回归应用的可行性,我们还结合实例给出了因变量向量估计的两种形式,以及无条件预报的置信区间。

Relationship Between Economic Growth and Water Environmental Quality of Anshan City in Northeast China

Based on the decomposition model of environmental quality and univariate regression model,the relationships of industrial wastewater drainage with economic scale,economic structure,and technological level in Anshan,a mining city in Northeast China,were studied. The results showed that,due to scale effect,the drainage of three important industrial wastewater pollutants(COD,NH3-N and petroleum) increased 8505t,671t and 384t,respectively,and due to structure effect,those pollutants drainage increased 3996t,174t and 120t from 2001 to 2006. While due to technological effect,the drainage of COD,NH3-N and petroleum reduced 4452t,458t and 331t,and due to cross effect,those pollutants drainage reduced 7270t,575t and 476t simultaneously. Meantime,the relationships between household consumption structure and domestic sewage discharge were analyzed,and domestic sewage discharges in different income levels were also compared. The results showed that,the domestic sewage discharges would increase 376t with 1000 yuan(RMB) increased in the traffic and communication consumption,and they would be 344t,219t,428t,1873t,respectively,in housing consumption,food consumption,medical consumption,miscellaneous commodity consumption. The proportion of domestic sewage discharge increased for high income residents significantly,but reduced for lower income residents. The industrial wastewater pollutants drainage tends to be reduced by technical progress,while domestic sewage discharge will be a more important factor for urban water environment quality.

Mapping methods for output-based objective speech quality assessment using data mining

Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.