Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized Logistic Regression Model

Logistic regression has been proved as a promising method for machine learning,which focuses on the problem of classification.In this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-norm is responsible for yielding a sparse logistic regression classifier and thel_(2)-norm for keeping betlter classification accuracy.To solve thel_(1)-l_(2)-regularized logistic regression model,we develop an alternating direction method of multipliers with embedding limitedlBroyden-Fletcher-Goldfarb-Shanno(L-BFGS)method.Furthermore,we implement our model for binary classification problems by using real data examples selected from the University of California,Irvine Machines Learning Repository(UCI Repository).We compare our numerical results with those obtained by the well-known LIBSVM and SVM-Light software.The numerical results show that ourl_(1)-l_(2)-regularized logisltic regression model achieves better classification and less CPU Time.

To set up a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the efficacy of Chinese herbal medicines

In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,could Chinese herbal medicines efficacy also be applied to predict the hepatotoxicity of Chinese herbal medicines?Therefore,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on Chinese herbal medicines efficacy has been tentatively set up to study the correlations of hepatotoxic and nonhepatotoxic Chinese herbal medicines with efficacy by using a chi-square test for two-way unordered categorical data.Logistic regression prediction model was established and the accuracy of the prediction by this model was evaluated.It has been found that the hepatotoxicity and nonhepatotoxicity of Chinese herbal medicines were weakly related to the efficacy,and the coefficient was 0.295.There were 20 variables from Chinese herbal medicines efficacy analyzed with unconditional logistic regression,and 6 variables,rectifying Qi and relieving pain,clearing heat and disinhibiting dampness,invigorating blood and stopping pain,invigorating blood and relieving swelling,killing worms and relieving fright were chosen to establish the logistic regression prediction model,with the optimal cutoff value being 0.250.Dissipating cold and relieving pain(DCRP),clearing heat and disinhibiting dampness,invigorating blood and relieving pain(IBRP),invigorating blood and relieving swelling,killing worms,and relieving fright were the variables to affect the hepatotoxicity and the established logistic regression prediction model had predictive power for hepatotoxicity of Chinese herbal medicines to a certain degree.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units

Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.

A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.

基于Logistic回归模型的TC4零件激光熔化沉积工艺参数分析（英文）

In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.

Impact of forest governance and enforcement on deforestation and forest degradation at the district level:A study in West Bengal State,India

According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affected by deforestation and forest degradation.The paper attempts to study the impact of forest governance,enforcement and socio-economic factors on deforestation and forest degradation at the local level in West Bengal State,India.The study was based on questionnaire survey data during 2020–2021 collected from three western districts(Purulia,Bankura,and Paschim Medinipur)where deforestation and poverty rates are higher than other districts in West Bengal State.The total number of selected villages was 29,and the total sample households were 693.A stratified random sampling technique was used to collect data,and a questionnaire was followed.Forest governance and enforcement indices were constructed using United Nation Development Programme(UNDP)methodology and a step-wise logistic regression model was used to identify the factors affecting deforestation and forest degradation.The result of this study showed that four factors(illegal logging,weak forest administration,encroachment,and poverty)are identified for the causes of deforestation and forest degradation.It is observed that six indices of forest governance(rule of law,transparency,accountability,participation,inclusiveness and equitability,and efficiency and effectiveness)are relatively high in Purulia District.Moreover,this study shows that Purulia and Bankura districts follow medium forest governance,while Paschim Medinipur District has poor forest governance.The enforcement index is found to be highest in Purulia District(0.717)and lowest for Paschim Medinipur District(0.257).Finally,weak forest governance,poor socio-economic conditions of the households,and weak enforcement lead to the deforestation and forest degradation in the study area.Therefore,governments should strengthen law enforcement and encourage sustainable forest certification schemes to combat illegal logging.

Choice behavior of commuters’rail transit mode during the COVID-19 pandemic based on logistic model

To understand whether commuters will take rail transit during the COVID-19 pandemic,a logistic regression model was constructed from three aspects of personal attributes,travel attributes and perception of COVID-19 based on 559 valid questionnaires.The results show that:occupation,commuting tools before the COVID-19 pandemic,walking time from residence to the nearest subway station,the possibility of being infected in private car and the possibility of being infected in public transport have significant influence on the commuters’choice of rail transit.Self-employed people and freelancers,commuters who used non-public transport before the COVID-19 pandemic,and commuters who take longer to walk from their residences to the nearest subway station are less likely to commute by rail transit during the COVID-19 pandemic.Commuters who think that the risk of being infected with the virus in public transport is higher have a lower probability of choosing rail transit.The confidence in bus/subway/taxi/taxi-hailing of commuters who do not choose to commute by rail transit during the COVID-19 pandemic is not high.The study of this paper can provide reference for the formulation of urban rail transit control measures during the COVID-19 pandemic,so as to formulate more perfect measures to ensure the safety of the returning workers.

An In-Depth Analysis of the Causes of Road Accidents in Developing Countries: Case Study of Douala-Dschang Highway in Cameroon

This paper is aimed at identifying the risk factors that mainly contribute to reckless driving and other related causes of road accidents along the Douala-Dschang highway of Cameroon. The research work started with the collection of accident reports for 2018 and 2019 from security officials in charge of road safety and the police stations of the different localities included in the sample of the study. Three hundred and eighty-two (382) road accidents reports were collected and analyzed using the 2020 version logit regression model of XLSTAT. From these analyses, it appears that, of the 382 accidents recorded during this period, six factors were identified and classified as follows: causes of accidents related to speed and carelessness, location of the accident, type of vehicle at fault, day the accident occurred, time of the accident and the age of drivers involved. These results could contribute to reduce the gravity of accidents along the Douala-Dschang highway and develop other policies in the program for road safety. In addition, this study can as much as possible equally contribute to reorienting road construction trends and development techniques in our environment.

Analysis of Influential Factors on Agricultural Surplus Labor Professionalization During China's Economic Downturn

This paper contributed to the pool of studies about agricultural surplus labor in China, also acted as the root to the imminent settlement of the issues concerning agriculture, countryside and farmers. Using data from survey of agricultural surplus labor in 2012, which covered three provinces in northern, midwestem and southern parts of China, this paper analyzed the influential factors on agricultural surplus labor professionalization by adoption of a logistic regression model. It showed that agricultural surplus labor shortage could be explained by low-quality professionalization. It was a feasible and effective way to solve the issue of workforce shortage during economic downturn by improving agricultural surplus labor＇s professionalization.

Contraceptive Method Choice Among Newly Married Couples and Influential Factors in Shanghai Municipality

A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.

Improving Disease Prevalence Estimates Using Missing Data Techniques

The prevalence of a disease in a population is defined as the proportion of people who are infected. Selection bias in disease prevalence estimates occurs if non-participation in testing is correlated with disease status. Missing data are commonly encountered in most medical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce the study power, and lead to invalid conclusions. The goal of this study is to illustrate how to estimate prevalence in the presence of missing data. We consider a case where the variable of interest (response variable) is binary and some of the observations are missing and assume that all the covariates are fully observed. In most cases, the statistic of interest, when faced with binary data is the prevalence. We develop a two stage approach to improve the prevalence estimates;in the first stage, we use the logistic regression model to predict the missing binary observations and then in the second stage we recalculate the prevalence using the observed data and the imputed missing data. Such a model would be of great interest in research studies involving HIV/AIDS in which people usually refuse to donate blood for testing yet they are willing to provide other covariates. The prevalence estimation method is illustrated using simulated data and applied to HIV/AIDS data from the Kenya AIDS Indicator Survey, 2007.

An improved window opening behavior model involving the division of the dummy variable’s interval level:Case study of an office building in Xi’an during summer

Window opening behavior significantly impacts indoor air quality,thermal comfort,and energy consumption.A field measurement was carried out in three typical rooms(a standard office,a meeting room and a smoking office)within an office building.The window state and the physical environment were continuously recorded during the measured periods.Three typical window opening behaviors were found in the measured samples,namely,active,moderate,and passive.The common logistic regression coefficient indicated that solar radiation exhibited the greatest effect on window opening behavior in the smoking office and standard office.Typically,window opening behavior in the meeting room was the most strongly correlated with time of the day,mainly because of the meeting schedule for occupants in the meeting room.This study discussed the dividing principles involved in setting the dummy variable interval level(discretizing continuous variables and dividing them into different intervals),and proposed a method to determine the optimal interval level of each variable.The improved model led to the increase in the prediction accuracy rate of the window being opened by 2.0%and 3.3%according to the comparison with the original model based on dummy variables and the common model based on continuous variables,respectively.This study can provide a reference value for simulating energy consumption in office buildings in the future.

Novel Prognostic Models for Predicting the 180-day Outcome for Patients with Hepatitis-B Virus-related Acute-on-chronic Liver Failure

Background and Aims:It remains difficult to forecast the 180-day prognosis of patients with hepatitis B virus-acuteon-chronic liver failure(HBV-ACLF)using existing prognostic models.The present study aimed to derive novel-innovative models to enhance the predictive effectiveness of the 180-day mortality in HBV-ACLF.Methods:The present cohort study examined 171 HBV-ACLF patients(non-survivors,n=62;survivors,n=109).The 27 retrospectively collected parameters included the basic demographic characteristics,clinical comorbidities,and laboratory values.Backward stepwise logistic regression(LR)and the classification and regression tree(CART)analysis were used to derive two predictive models.Meanwhile,a nomogram was created based on the LR analysis.The accuracy of the LR and CART model was detected through the area under the receiver operating characteristic curve(AUROC),compared with model of end-stage liver disease(MELD)scores.Results:Among 171 HBV-ACLF patients,the mean age was 45.17 years-old,and 11.7%of the patients were female.The LR model was constructed with six independent factors,which included age,total bilirubin,prothrombin activity,lymphocytes,monocytes and hepatic encephalopathy.The following seven variables were the prognostic factors for HBV-ACLF in the CART model:age,total bilirubin,prothrombin time,lymphocytes,neutrophils,monocytes,and blood urea nitrogen.The AUROC for the CART model(0.878)was similar to that for the LR model(0.878,p=0.898),and this exceeded that for the MELD scores(0.728,p<0.0001).Conclusions:The LR and CART model are both superior to the MELD scores in predicting the 180-day mortality of patients with HBV-ACLF.Both the LR and CART model can be used as medical decision-making tools by clinicians.

中国城镇居民住房产权与类型选择

The rapid growth in the number of urban migrants in China has brought about a lack of housing for migrants.The housing preferences and factors influencing those for urban migrants in China are examined using data from the China Migrant Dynamic Survey(CMDS)conducted in 2017.This study demonstrates that urban migrants in China typically rent their homes and that factors such as household life cycle,education,hukou type,occupation,range and duration of movement,and social integration have a major impact on these decisions.Large households,high levels of education,accompanying family migration,marriage,non-agricultural hukou,employment in state-owned enterprises,and high levels of societal integration with local society all increase the likelihood that migrants will purchase houses.Migration-related housing decisions are significantly influenced by regional disparities in economic growth.Because housing is more expensive in the economically developed eastern areas than in the central and western regions,migrants there are less likely to be able to buy a home.To preserve the rights of migrants,local governments should progressively change their housing policies,and housing developers should pay closer attention to the trends and preferences of migrants in terms of housing choice.

Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit,Which is Better?

Choice of appropriate mapping units is important in landslide susceptibility mapping(LSM).There are various possible units for this choice,while it remains unclear which one is better in performance.The purpose of this study is to make a quantitative comparison of two commonly-used units:slope-unit(SU)and raster-unit(RU)based on the landslides triggered by the 2013 Minxian,Gansu,China Mw5.9 earthquake.Ten landslide influencing factors were considered in this analysis.For each type of mapping units,the 70%samples were randomly selected and trained 20 times on the LR model,yielding 20 susceptibility maps,and the remaining 30%samples were tested for the accuracy of the modeling outcome.Different metrics,including the mean probability,model uncertainty,and model prediction skills,were used to evaluate the quality of the susceptibility maps.The results show that the resultant probability maps using two mapping units can largely predict the distribution of actual landslides,on which the high susceptibility area corresponds to the landslide-prone area.The AUC(area under curve)values,ranging from 0.8 to 0.86,show that the prediction ability of two mapping units is roughly the same.While comparing with the RU,the use of SU can lower the model uncertainties caused by the variation of training sets.We converted the RU-based assessment results into SU-based scheme.The results show that two assessment results are well fitted with good linear relationship,which implies that it is feasible to convert the RU-based landslide susceptibility mapping into the SU-based scheme.This analysis indicates that compared with the RU,the SU cannot improve the performance and accuracy of seismic landslide susceptibility mapping.

Driving forces analysis of reservoir wetland evolution in Beijing during 1984-2010

The reservoir wetland, which is the largest artificial wetland in Beijing, constitutes one of the important urban ecological infrastructures. Considering two elements of natural environment and socio-economy, this paper established the driving factor indexing system of Beijing reservoir wetland evolution. Natural environment driving factors include precipitation, temperature, entry water and groundwater depth; social economic driving factors include resident population, urbanization rate and per capita GDP. Using multi-temporal Landsat TM images from 1984 to 2010 in Beijing, the spatial extent and the distribution of Beijing reservoir wetlands were extracted, and the change of the wetland area about the three decade years were analyzed. Logistic regression model was used to explore for each of the three periods： from 1984 to 1998, from 1998 to 2004 and from 2004 to 2010. The results showed that the leading driving factors and their influences on reservoir wetland evolution were different for each period. During 1984-1998, two natural environment indices： average annual precipitation and entry water index were the major factors driving the increase in wetland area with the contribution rate of Logistic regression being 5.78 and 3.50, respectively, and caused the wetland growth from total area of 104.93 km 2 to 219.96 km 2 . From 1998 to 2004, as the impact of human activities intensified the main driving factors were the number of residents, groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41, 9.18, and 7.77, respectively, and caused the wetland shrinkage rapidly from the total area of 219.96 km 2 to 95.71 km 2 . During 2004-2010, reservoir wetland evolution was impacted by both natural and socio-economic factors, and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22, respectively, and caused the wetland total area growth slightly to 109.73 km 2 .