Skin Sensitive Difference of Human Body Sections under Clothing - Multiple Analysis of Skin Surface Temperature Changes

A new researching method on clothing comfort perception is developed.By it the skin surface temperature changes and subjective psychological perception of human body sections stimulated by the same cold stimulation are studied.With the multiple comparison analysis method the changing laws of skin temperature of main human body sections is obtained.

Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

Enterprise Value Valuation-BYD as an Example Based on SWOT Model and Multiple Regression Model

BYD is one of the largest new energy vehicle companies in China.Analyzing its scenario and the factors that affect its value helps to understand and identify development opportunities and potential problems.On one hand,this paper makes a qualitative analysis of BYD,using SWOT model to study the internal capability and external environment of BYD.On the other hand,the multiple regression model is used for quantitative analysis of BYD’s enterprise value,and the model is established based on three factors:enterprise fundamentals,investor behavior and psychology,and macroeconomic policy uncertainty,and the stepwise regression is carried out.The results show that the increase of institutional investors’shareholding ratio,the increase of investor sentiment index,and the increase of M2 growth rate will increase the overall enterprise value,while the increase of economic policy uncertainty will decrease the enterprise value.

Stability of mine ventilation system based on multiple regression analysis

In order to overcome the disadvantages of diagonal connection structures that are complex and for which it is difficult to derive the discriminant of the airflow directions of airways, we have applied a multiple regression method to analyze the effect, of changing the rules of mine airflows, on the stability of a mine ventilation system. The amount of air （ Qj ） is determined for the major airway and an optimum regression equation was derived for Qi as a function of the independent variable （ Ri ）, i.e., the venti- lation resistance between different airways. Therefore, corresponding countermeasures are proposed according to the changes in airflows. The calculated results agree very well with our practical situation, indicating that multiple regression analysis is simple, quick and practical and is therefore an effective method to analyze the stability of mine ventilation systems.

Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Multiple Regression Analysis of Influencing Factors on Yield of New Sugarcane Variety Yuetang 03-373

[Objectives]The purpose of this study was to provide reference for cultivation and promotion of a new sugarcane variety Yuetang 03-373,on the basis of analyzing and summarizing the characters of the variety.[Methods]Correlation,multiple regression and path analyses were performed for the yield and yield components of Yuetang 03-373.[Results]Correlation analysis shows that cane yield was significantly correlated with millable stalk number,stalk length and stalk diameter,and among them,the correlation with millable stalk number was the strongest.Multiple regression and path analyses show that millable stalk number contributed the most to cane yield,followed by stalk length,and stalk diameter contributed the least.The regression equation of cane yield against the three yield components was y=-2.8713+1.5497x1+5.8990x2-395.4294x3(R=0.9672**).[Conclusions]Millable stalk number and stalk length were the important and major factors for high yield of Yuetang 03-373,indicating that Yuetang 03-373 is a sugarcane variety of millable stalk type.In cultivation,full play should be given to the advantage of Yuetang 03-373 in millable stalk number,as well as stalk length(plant height),in order to achieve the purpose of increasing yield.

Using Multiple Correspondence Analysis to Measure Multidimensional Poverty in Congo

The following analysis is based on a multidimensional understanding of poverty using a nonmonetary basic needs approach. It is ground on data from the first survey on household living conditions for poverty assessment, conducted by the National Institute of Statistics of Congo in 2005. Multiple Correspondence Analysis is applied to construct a composite indicator by aggregating several attributes likely to reflect the poverty of individuals or households. The application shows that Congolese households are not affected by the same type of poverty. Three types of non-monetary poverty are identified: infrastructure poverty, vulnerability of human existence and poverty of comfort. These households were then classified according to the composite indicator of Poverty. The results show that the incidence of poverty corresponds to the weight of poor class of about 70.67%.

Completely Distinguishing Individual A-genome Chromosomes and Their Karyotyping Analysis by Multiple BAC-FISH

Multiple BAC-FISH is a powerful tool for modern cytogenetic researching in both animals and plants.But in cotton,this technique is unavailable due to the high percentage of repetitive sequences.

Predicting urbanization level by main element analysis and multiple linear regression---taking Xiantao district in Hubei Province as an example

In this paper we firstly select main factors relating to urbanization level of Xiantao District in Hubei Province by main element, then, make model of urbanization level by analysis of multiple liner regression, and lastly predict its urbanization level

Comparison of seismic fragility analysis methods for arch dams

This study focuses on the seismic fragility analysis of arch dams.The multiple stripe analysis(MSA),cloud analysis(CLA),and incremental dynamic analysis(IDA)methods are compared.A comprehensive dam-reservoir-foundation rock system,which considers the opening of contraction joints,the nonlinearity of dam concrete and foundation rock,the radiation damping effect of semi-unbounded foundation,and the compressibility of reservoir water,is used as a numerical example.225,80,and 15 earthquake records are selected for MSA,CLA,and IDA,respectively.The results show that MSA provides satisfactory fragility analysis,while both CLA and IDA have assumptions that may lead to deviations.Therefore,MSA is the most reliable method among the three methods and is recommended for the fragility analysis of arch dams.It is also shown that the choice of demand level affects the reliability of fragility curves and the effect of the material uncertainty on the fragility of the dam is not significant.

RET proto-oncogene mutation analysis in a pedigree with multiple endocrine neoplasia 2A

Objective To discuss clinical diagnosis and treatment of multiple endocrine neoplasia ( MEN) 2A,and report the mutation of RET proto-oncogene in a pedigree of three patients with MEN 2A. Methods Bilateral adrenalectomy was performed on two of the three

Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques.The coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the results.It indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,respectively.Multiple parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.

Quantitative Analysis of Heavy Metals in Water Based on LIBS with an Automatic Device for Sample Preparation

Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.

Numerical and Dimensional Analysis for Prediction of Line Heating Residual Deformations

Line heating process is a very complex phenomenon as a variety of factors affects the amount of residual deformations. Numerical thermal and mechanical analysis of line heating for prediction of residual deformation is time consuming. In the present work dimensional analysis has been presented to obtain a new relationship between input parameters and resulting residual deformations during line heating process. The temperature distribution and residual deformations for 6 mm, 8 mm, 10 mm and 12 mm thick steel plates were numerically estimated and compared with experimental and published results. Extensive data generated through a validated FE model were used to find co-relationship between the input parameters and the resulting residual deformation by multiple regression analysis. The results obtained from the deformation equations developed in this work compared well with those of the FE analysis with a drop in the computation time in the order of 100 (computational time required for FE analysis is around 7 200 second to 9 000 seconds and where the time required for getting the residual deformation by developed equations is only 60 to 90 seconds).

Clinical characteristics and prognostic analysis of gastroenteropancreatic neuroendocrine neoplasm(GEP-NEN)

Objective： The aim of the study was to analyze the clinicopathologic characteristics of gastroenteropancreatic neuroendocrine neoplasm （GEP-NEN） and to explore the prognostic factors for patients and differences of immunohisto- chemical markers between neuroendocrine tumor （NET） and neuroendocrine carcinoma （NEC）. Methods： Retrospective reviews were conducted for the charts of 119 patients with GEP-NEN at the Affiliated Hospital of Qingdao University （China） from August 2003 to December 2013. Kaplan-Meier method was used to do the overall survivals analysis for the patients at different levels of predictive factors. Meanwhile, Cox proportional hazard model was used to select independent risk factors of surJival. Analysis of variance was used to compare the expression of immunohistochemical markers among different patho- logical grades. Results：Among 119 patients, pancreas （45/119, 37.82%） and rectum （33/119, 27.73%） were mostly involved. The onset age of GEP-NEN in female group was younger than that of the male group. There were 13 deaths （10.92%） during 18.9 （0.1-133.4） months follow-up period. Multivariate analysis indicated that neural invasion, gender and pathological grades of NET and NEC were independent risk factors. In neuroendocrine neoplasm （NEN）, Syn expression in G2 was higher than G1 and G3, while CgA showed no significant difference. All markers showed no significant differences between NET and NEC. Conclusion： GEP-NEN may occur at multiple sites of digestive system and lack specific clinical manifestations. Syn expression detected for the prognosis of G1, G2 and G3 tumors have clinical significance. Neural invasion, sex and patho- logical grades were independent prognostic factors for GEP-NEN patients. No significant difference was found in different pathological grades of NET and NEC.

Development of a bifurcation analysis approach based on gPROMS platform

A bifurcation analysis approach is developed based on the process simulator gPROMS platform, which can automatically trace a solution path, detect and pass the bifurcation points and check the stability of solutions. The arclength continuation algorithm is incorporated as a process entity in gPROMS to overcome the limit of turning points and get multiple solutions with respect to a user-defined parameter. The bifurcation points are detected through a bifurcation test function τ which is written in C ++ routine as a foreign object connected with gPROMS through Foreign Process Interface. The stability analysis is realized by evaluating eigenvalues of the Jacobian matrix of each steady state solution. Two reference cases of an adiabatic CSTR and a homogenous azeotropic distillation from literature are studied, which successfully validate the reliability of the proposed approach. Besides the multiple steady states and Hopf bifurcation points, a more complex homoclinic bifurcation behavior is found for the distillation case compared to literature.

An empirical analysis on community residents' perception and major influencing factors of rural tourism development

With the rapid development of rural tourism in China,community residents,as important stakeholders in the development of rural tourism,their perceptions and attitudes directly affect the sustainable and healthy development of local rural tourism.Taking the community residents of Xiaogucheng Village in Hangzhou as the research object,using the methods of field interviews and questionnaires,a multiple regression model was established to conduct an empirical analysis on the perception and main factors affecting the development of rural tourism of community residents.The results show that the development of rural tourism in villages with better economic development is not as popular as expected;Where community residents have made ideological progress and are willing to participate in tourism development,the development effect of rural tourism is remarkable;In addition,community residents also hope that their personal abilities can be combined with rural tourism for common development;The destruction of community environment has a slight impact on the development of rural tourism,which shows that the attention is not enough.Finally,based on the analysis conclusion,it provides new ideas and inspiration for the sustainable development of rural tourism:improving the community residents’participation in rural tourism system,establishing the guidance mechanism of community residents’tourism vocational education,and consolidating the achievements of community ecological environment management.