Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug use...Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.展开更多
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...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.展开更多
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 modeli...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.展开更多
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 develo...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.展开更多
In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression an...In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression analysis and fuzzy inference system, this paper tries to develop predictive models to estimate overbreak caused by blasting at the Alborz Tunnel. To develop the models, 202 datasets were utilized, out of which 182 were used for constructing the models. To validate and compare the obtained results,determination coefficient(R2) and root mean square error(RMSE) indexes were chosen. For the fuzzy model, R2 and RMSE are equal to 0.96 and 0.55 respectively, whereas for regression model, they are 0.41 and 1.75 respectively, proving that the fuzzy predictor performs, significantly, better than the statistical method. Using the developed fuzzy model, the percentage of overbreak was minimized in the Alborz Tunnel.展开更多
We studied relations between natural seedling reproduction and above ground environment in a longleaf pine ecosystem. Forty-eight 0.05 ha circular plots were sampled under single-tree selection, group-tree selection a...We studied relations between natural seedling reproduction and above ground environment in a longleaf pine ecosystem. Forty-eight 0.05 ha circular plots were sampled under single-tree selection, group-tree selection and control stands in three main longleaf pine areas in south Alabama, USA. We measured six above-ground environment factors, viz. canopy closure, stand density, basal area, average tree height, understory cover and PAR under canopy. We employed forward, back-ward and stepwise selection regression to produce one model. Three main variables：canopy closure, stand density and basal area, were left in the model; light, PAR and understory cover were not incorporated into the model at the 0.10 significance level. Basal area was a positive pa-rameter, while canopy closure and stand density were negative parame-ters. Canopy closure was the main parameter in the model. The model proved to be meaningful, and has potential to provide useful guidance for future work.展开更多
BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery(BCS).Preoperative imaging examinations are frequently employed to assess the surgical ma...BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery(BCS).Preoperative imaging examinations are frequently employed to assess the surgical margin.AIM To investigate the role and value of preoperative imaging examinations[magnetic resonance imaging(MRI),molybdenum target,and ultrasound]in evaluating margins for BCS.METHODS A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021.The study gathered preoperative imaging data(MRI,ultrasound,and molybdenum target examination)and intraoperative and postoperative pathological information.Based on their BCS outcomes,patients were categorized into positive and negative margin groups.Subsequently,the patients were randomly split into a training set(226 patients,approximately 70%)and a validation set(97 patients,approximately 30%).The imaging and pathological information was analyzed and summarized using R software.Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS.A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis.This study aims to identify the risk factors associated with failure in BCS.RESULTS The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS.These factors comprise non-mass enhancement(NME)on dynamic contrastenhanced MRI,multiple focal vascular signs around the lesion on MRI,tumor size exceeding 2 cm,type III timesignal intensity curve,indistinct margins on molybdenum target examination,unclear margins on ultrasound examination,and estrogen receptor(ER)positivity in immunohistochemistry.LASSO regression was additionally employed in this study to identify four predictive factors for the model:ER,molybdenum target tumor type(MT Xmd Shape),maximum intensity projection imaging feature,and lesion type on MRI.The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set.Particularly,the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.CONCLUSION The proposed column chart model effectively predicts the success of BCS for breast cancer.The model utilizes preoperative ultrasound,molybdenum target,MRI,and core needle biopsy pathology evaluation results,all of which align with the real-world scenario.Hence,our model can offer dependable guidance for clinical decisionmaking concerning BCS.展开更多
The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics h...The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.展开更多
COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to ano...COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray.On these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic.In this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final size.Several regression analysis models have been applied for data analysis of COVID-19 of Egypt.In this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 dataset.Thus,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month.In addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020.Secondly,the final total size for cases 1.6676Et05 cases.The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.展开更多
基金supported by the National Scientific Research Mega-Project under the 12th Five-Year Plan of China(2012ZX10001001)
文摘Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.
文摘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.
文摘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.
基金supported by the Soft Science Project of Zhejiang Province(Grant No.2020C 35084)Scientific Research Project of Qianjiang College of Hangzhou Normal University
文摘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.
文摘In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression analysis and fuzzy inference system, this paper tries to develop predictive models to estimate overbreak caused by blasting at the Alborz Tunnel. To develop the models, 202 datasets were utilized, out of which 182 were used for constructing the models. To validate and compare the obtained results,determination coefficient(R2) and root mean square error(RMSE) indexes were chosen. For the fuzzy model, R2 and RMSE are equal to 0.96 and 0.55 respectively, whereas for regression model, they are 0.41 and 1.75 respectively, proving that the fuzzy predictor performs, significantly, better than the statistical method. Using the developed fuzzy model, the percentage of overbreak was minimized in the Alborz Tunnel.
文摘We studied relations between natural seedling reproduction and above ground environment in a longleaf pine ecosystem. Forty-eight 0.05 ha circular plots were sampled under single-tree selection, group-tree selection and control stands in three main longleaf pine areas in south Alabama, USA. We measured six above-ground environment factors, viz. canopy closure, stand density, basal area, average tree height, understory cover and PAR under canopy. We employed forward, back-ward and stepwise selection regression to produce one model. Three main variables：canopy closure, stand density and basal area, were left in the model; light, PAR and understory cover were not incorporated into the model at the 0.10 significance level. Basal area was a positive pa-rameter, while canopy closure and stand density were negative parame-ters. Canopy closure was the main parameter in the model. The model proved to be meaningful, and has potential to provide useful guidance for future work.
文摘BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery(BCS).Preoperative imaging examinations are frequently employed to assess the surgical margin.AIM To investigate the role and value of preoperative imaging examinations[magnetic resonance imaging(MRI),molybdenum target,and ultrasound]in evaluating margins for BCS.METHODS A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021.The study gathered preoperative imaging data(MRI,ultrasound,and molybdenum target examination)and intraoperative and postoperative pathological information.Based on their BCS outcomes,patients were categorized into positive and negative margin groups.Subsequently,the patients were randomly split into a training set(226 patients,approximately 70%)and a validation set(97 patients,approximately 30%).The imaging and pathological information was analyzed and summarized using R software.Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS.A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis.This study aims to identify the risk factors associated with failure in BCS.RESULTS The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS.These factors comprise non-mass enhancement(NME)on dynamic contrastenhanced MRI,multiple focal vascular signs around the lesion on MRI,tumor size exceeding 2 cm,type III timesignal intensity curve,indistinct margins on molybdenum target examination,unclear margins on ultrasound examination,and estrogen receptor(ER)positivity in immunohistochemistry.LASSO regression was additionally employed in this study to identify four predictive factors for the model:ER,molybdenum target tumor type(MT Xmd Shape),maximum intensity projection imaging feature,and lesion type on MRI.The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set.Particularly,the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.CONCLUSION The proposed column chart model effectively predicts the success of BCS for breast cancer.The model utilizes preoperative ultrasound,molybdenum target,MRI,and core needle biopsy pathology evaluation results,all of which align with the real-world scenario.Hence,our model can offer dependable guidance for clinical decisionmaking concerning BCS.
基金supported by the National Natural Science Foundation of China Key Project under Grant No.70933003the National Natural Science Foundation of China under Grant Nos.70871109 and 71203247
文摘The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
文摘COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray.On these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic.In this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final size.Several regression analysis models have been applied for data analysis of COVID-19 of Egypt.In this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 dataset.Thus,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month.In addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020.Secondly,the final total size for cases 1.6676Et05 cases.The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.
