Despite concerted efforts to create employment opportunities and the realized economic growth between 2000 and 2005, the unemployment rate in Namibia currently stands at 27.4%, according to the Labour Force Survey rel...Despite concerted efforts to create employment opportunities and the realized economic growth between 2000 and 2005, the unemployment rate in Namibia currently stands at 27.4%, according to the Labour Force Survey released in April 2013. The percentage of employed males in Namibia stands at 41.6% while that of employed females stand at 28.8% according to the National Human Resources Plan of May 2013. Analysts have put the blame on adverse climatic conditions, limited levels of skills, access to finance, and the structure of the economy. The frustration and discomfort caused by unemployment, especially among the youth, can threaten the country＇s peace and stability as it negatively impacts on the standard of living, crime rates, family happiness, and drug abuse.To date, studies on employment in Namibia have mainly concentrated on the micro and macro econometric approaches. It is important to examine how bio-demographic characteristics affect employment. This paper uses data from the 2010 Income and expenditure survey to establish the bio-demographic determinants of employment by fitting a binary logistic model. The outcome variable is employment status which is dichotomous. The independent variables which were guided by review of related literature and availability of data in the Income and Expenditure survey data set, included age-group, region, place of residence, marital status, education level, and gender. Results indicated that employment prospects in Namibia were influenced by the region, gender, marital status, and education level.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This eva...This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.展开更多
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n...In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.展开更多
BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recogn...BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.展开更多
BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate ...BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.展开更多
Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malays...Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.展开更多
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 ri...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.展开更多
Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(AN...Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.展开更多
Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence...Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.展开更多
Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides...Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics(ROC) curve, spatially agreed area approach and seed cell area index(SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.展开更多
The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data ...The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.展开更多
BACKGROUND Focal nodular hyperplasia(FNH)has very low potential risk,and a tendency to spontaneously resolve.Hepatocellular adenoma(HCA)has a certain malignant tendency,and its prognosis is significantly different fro...BACKGROUND Focal nodular hyperplasia(FNH)has very low potential risk,and a tendency to spontaneously resolve.Hepatocellular adenoma(HCA)has a certain malignant tendency,and its prognosis is significantly different from FNH.Accurate identification of HCA and FNH is critical for clinical treatment.AIM To analyze the value of multi-parameter ultrasound index based on logistic regression for the differential diagnosis of HCA and FNH.METHODS Thirty-one patients with HCA were included in the HCA group.Fifty patients with FNH were included in the FNH group.The clinical data were collected and recorded in the two groups.Conventional ultrasound,shear wave elastography,and contrast-enhanced ultrasound were performed,and the lesion location,lesion echo,Young’s modulus(YM)value,YM ratio,and changes of time intense curve(TIC)were recorded.Multivariate logistic regression analysis was used to screen the indicators that can be used for the differential diagnosis of HCA and FNH.A ROC curve was established for the potential indicators to analyze the accuracy of the differential diagnosis of HCA and FNH.The value of the combined indicators for distinguishing HCA and FNH were explored.RESULTS Multivariate logistic regression analysis showed that lesion echo(P=0.000),YM value(P=0.000)and TIC decreasing slope(P=0.000)were the potential indicators identifying HCA and FNH.In the ROC curve analysis,the accuracy of the YM value distinguishing HCA and FNH was the highest(AUC=0.891),which was significantly higher than the AUC of the lesion echo and the TIC decreasing slope(P<0.05).The accuracy of the combined diagnosis was the highest(AUC=0.938),which was significantly higher than the AUC of the indicators diagnosing HCA individually(P<0.05).This sensitivity was 91.23%,and the specificity was 83.33%.CONCLUSION The combination of lesion echo,YM value and TIC decreasing slope can accurately differentiate between HCA and FNH.展开更多
The Wenchuan earthquake on May 12,2008 caused numerous collapses,landslides,barrier lakes,and debris flows.Landslide susceptibility mapping is important for evaluation of environmental capacity and also as a guide for...The Wenchuan earthquake on May 12,2008 caused numerous collapses,landslides,barrier lakes,and debris flows.Landslide susceptibility mapping is important for evaluation of environmental capacity and also as a guide for post-earthquake reconstruction.In this paper,a logistic regression model was developed within the framework of GIS to map landslide susceptibility.Qingchuan County,a heavily affected area,was selected for the study.Distribution of landslides was prepared by interpretation of multi-temporal and multi-resolution remote sensing images(ADS40 aerial imagery,SPOT5 imagery and TM imagery,etc.) and field surveys.The Certainly Factor method was used to find the influencial factors,indicating that lithologic groups,distance from major faults,slope angle,profile curvature,and altitude are the dominant factors influencing landslides.The weight of each factor was determined using a binomial logistic regression model.Landslide susceptibility mapping was based on spatial overlay analysis and divided into five classes.Major faults have the most significant impact,and landslides will occur most likely in areas near the faults.Onethird of the area has a high or very high susceptibility,located in the northeast,south and southwest,including 65.3% of all landslides coincident with the earthquake.The susceptibility map can reveal the likelihood of future failures,and it will be useful for planners during the rebuilding process and for future zoning issues.展开更多
This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on m...This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research. Thirty seven research articles published between 2000 and 2018 which employed logistic regression as the main statistical tool as well as six text books on logistic regression were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, model fitting, reporting and interpreting were presented. Upon perusing the literature, considerable deficiencies were found in both the use and reporting of LR. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Also, most studies did not report on validation analysis, regression diagnostics or goodness-of-fit measures;measures which authenticate the robustness of the LR model. Here, we demonstrate a good example of the application of the LR model using data obtained on a cohort of pregnant women and the factors that influence their decision to opt for caesarean delivery or vaginal birth. It is recommended that researchers should be more rigorous and pay greater attention to guidelines concerning the use and reporting of LR models.展开更多
Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use chan...Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made.This paper tends to apply logistic regression to model urban growth in the Jiayu county of Hubei province,China.It is applied in a GIS environment to calculate variables and,then,in SPSS to discover the relationships between urban growth and the driving forces.The relative operating characteristic(ROC) shows the modeling accuracy with the curve 0.891 with standard er-ror 0.001.A probability map is generated finally to predict where urban growth will occur as a result of the computation.The result shows the model simulates urban growth well in the county scale.展开更多
Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for model...Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as ＂likely enough to release warning messages＂. The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.展开更多
This paper proposed a new method for quantitative assessment of visual detectability of damage based on logistic regression,using the Probability of Detection(POD)as a criterion.Experiments were performed to establish...This paper proposed a new method for quantitative assessment of visual detectability of damage based on logistic regression,using the Probability of Detection(POD)as a criterion.Experiments were performed to establish the massive hit/miss data of visual inspection.Authoritative investigations verified the reliability of the data.The prediction function concluded comprises more than one flaw size parameters,including the depth and diameter of the dents.The results show that the depth and diameter of the dents are pivotal for the evaluation of detectability;the type of detection,the detection distance,and the qualifications of personnel are critical external factors to be considered.This function,with an accuracy rate of nearly 85%,is capable of predicting the visual detection probability of impact damage under various detection environments,which will provide a reference for the damage tolerance design of composite materials and field maintenance in the NonDestructive Testing(NDT)field.展开更多
With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistica...With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.展开更多
文摘Despite concerted efforts to create employment opportunities and the realized economic growth between 2000 and 2005, the unemployment rate in Namibia currently stands at 27.4%, according to the Labour Force Survey released in April 2013. The percentage of employed males in Namibia stands at 41.6% while that of employed females stand at 28.8% according to the National Human Resources Plan of May 2013. Analysts have put the blame on adverse climatic conditions, limited levels of skills, access to finance, and the structure of the economy. The frustration and discomfort caused by unemployment, especially among the youth, can threaten the country＇s peace and stability as it negatively impacts on the standard of living, crime rates, family happiness, and drug abuse.To date, studies on employment in Namibia have mainly concentrated on the micro and macro econometric approaches. It is important to examine how bio-demographic characteristics affect employment. This paper uses data from the 2010 Income and expenditure survey to establish the bio-demographic determinants of employment by fitting a binary logistic model. The outcome variable is employment status which is dichotomous. The independent variables which were guided by review of related literature and availability of data in the Income and Expenditure survey data set, included age-group, region, place of residence, marital status, education level, and gender. Results indicated that employment prospects in Namibia were influenced by the region, gender, marital status, and education level.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金supported by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Educationsupported by the National Natural Scientific Foundation of China under Grant No.NSFC12131006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Science under Grant No.CI2023C063YLL。
文摘This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.
基金supported by National Natural Science Foundation of China(Grant 62573375)the Natural Science Foundation of Hebei Province(Grant F2024203038)+2 种基金the Science and Technology Research and Development Plan Project of Qinhuangdao City(Grant 202302B048)the Provincial Key Laboratory Performance Subsidy Project(Grant 22567612H)the Shandong Provincial Natural Science Foundation Youth Project(ZR2023QF044)。
文摘In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.
基金Supported by High-level Professional Groups in Gangdong Province,No.GSPZYQ2020101Guangdong Province Educational Research Planning Project,No.2024GXJK742。
文摘BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.
基金Supported by Zhangjiajie"Xiao He(Young Talent)"Project,No.2024XHRC03Jishou University School-Level Research Project.
文摘BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.
文摘Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.
基金This paper was financially supported by NSC96-2628-E-366-004-MY2 and NSC96-2628-E-132-001-MY2
文摘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.
文摘Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.
基金supported by the Project of the 12th Five-year National Sci-Tech Support Plan of China(2011BAK12B09)China Special Project of Basic Work of Science and Technology(2011FY110100-2)
文摘Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.
文摘Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics(ROC) curve, spatially agreed area approach and seed cell area index(SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.
基金supported by National Key Natural Science Foundation of China (Grant No. 50635010)
文摘The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.
基金Supported by Zhejiang Natural Science Foundation,NO.LY16H160004Ningbo Yinzhou District Agricultural and Social Development Science and Technology Project,NO.Yinke 2018-74
文摘BACKGROUND Focal nodular hyperplasia(FNH)has very low potential risk,and a tendency to spontaneously resolve.Hepatocellular adenoma(HCA)has a certain malignant tendency,and its prognosis is significantly different from FNH.Accurate identification of HCA and FNH is critical for clinical treatment.AIM To analyze the value of multi-parameter ultrasound index based on logistic regression for the differential diagnosis of HCA and FNH.METHODS Thirty-one patients with HCA were included in the HCA group.Fifty patients with FNH were included in the FNH group.The clinical data were collected and recorded in the two groups.Conventional ultrasound,shear wave elastography,and contrast-enhanced ultrasound were performed,and the lesion location,lesion echo,Young’s modulus(YM)value,YM ratio,and changes of time intense curve(TIC)were recorded.Multivariate logistic regression analysis was used to screen the indicators that can be used for the differential diagnosis of HCA and FNH.A ROC curve was established for the potential indicators to analyze the accuracy of the differential diagnosis of HCA and FNH.The value of the combined indicators for distinguishing HCA and FNH were explored.RESULTS Multivariate logistic regression analysis showed that lesion echo(P=0.000),YM value(P=0.000)and TIC decreasing slope(P=0.000)were the potential indicators identifying HCA and FNH.In the ROC curve analysis,the accuracy of the YM value distinguishing HCA and FNH was the highest(AUC=0.891),which was significantly higher than the AUC of the lesion echo and the TIC decreasing slope(P<0.05).The accuracy of the combined diagnosis was the highest(AUC=0.938),which was significantly higher than the AUC of the indicators diagnosing HCA individually(P<0.05).This sensitivity was 91.23%,and the specificity was 83.33%.CONCLUSION The combination of lesion echo,YM value and TIC decreasing slope can accurately differentiate between HCA and FNH.
基金supported by State Key Fundamental Research Program (973) project (2008CB425802)the National natural Science Foundation of China (Grant No. 40801009)
文摘The Wenchuan earthquake on May 12,2008 caused numerous collapses,landslides,barrier lakes,and debris flows.Landslide susceptibility mapping is important for evaluation of environmental capacity and also as a guide for post-earthquake reconstruction.In this paper,a logistic regression model was developed within the framework of GIS to map landslide susceptibility.Qingchuan County,a heavily affected area,was selected for the study.Distribution of landslides was prepared by interpretation of multi-temporal and multi-resolution remote sensing images(ADS40 aerial imagery,SPOT5 imagery and TM imagery,etc.) and field surveys.The Certainly Factor method was used to find the influencial factors,indicating that lithologic groups,distance from major faults,slope angle,profile curvature,and altitude are the dominant factors influencing landslides.The weight of each factor was determined using a binomial logistic regression model.Landslide susceptibility mapping was based on spatial overlay analysis and divided into five classes.Major faults have the most significant impact,and landslides will occur most likely in areas near the faults.Onethird of the area has a high or very high susceptibility,located in the northeast,south and southwest,including 65.3% of all landslides coincident with the earthquake.The susceptibility map can reveal the likelihood of future failures,and it will be useful for planners during the rebuilding process and for future zoning issues.
文摘This study explored and reviewed the logistic regression (LR) model, a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, with emphasis on medical research. Thirty seven research articles published between 2000 and 2018 which employed logistic regression as the main statistical tool as well as six text books on logistic regression were reviewed. Logistic regression concepts such as odds, odds ratio, logit transformation, logistic curve, assumption, selecting dependent and independent variables, model fitting, reporting and interpreting were presented. Upon perusing the literature, considerable deficiencies were found in both the use and reporting of LR. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Also, most studies did not report on validation analysis, regression diagnostics or goodness-of-fit measures;measures which authenticate the robustness of the LR model. Here, we demonstrate a good example of the application of the LR model using data obtained on a cohort of pregnant women and the factors that influence their decision to opt for caesarean delivery or vaginal birth. It is recommended that researchers should be more rigorous and pay greater attention to guidelines concerning the use and reporting of LR models.
文摘Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made.This paper tends to apply logistic regression to model urban growth in the Jiayu county of Hubei province,China.It is applied in a GIS environment to calculate variables and,then,in SPSS to discover the relationships between urban growth and the driving forces.The relative operating characteristic(ROC) shows the modeling accuracy with the curve 0.891 with standard er-ror 0.001.A probability map is generated finally to predict where urban growth will occur as a result of the computation.The result shows the model simulates urban growth well in the county scale.
基金the New Technology Generalization Project of China Meteorological Administration (CMATG2004M05)
文摘Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as ＂likely enough to release warning messages＂. The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.
基金supported by COMAC Beijing Aeronautical Science&Technology Research Institute。
文摘This paper proposed a new method for quantitative assessment of visual detectability of damage based on logistic regression,using the Probability of Detection(POD)as a criterion.Experiments were performed to establish the massive hit/miss data of visual inspection.Authoritative investigations verified the reliability of the data.The prediction function concluded comprises more than one flaw size parameters,including the depth and diameter of the dents.The results show that the depth and diameter of the dents are pivotal for the evaluation of detectability;the type of detection,the detection distance,and the qualifications of personnel are critical external factors to be considered.This function,with an accuracy rate of nearly 85%,is capable of predicting the visual detection probability of impact damage under various detection environments,which will provide a reference for the damage tolerance design of composite materials and field maintenance in the NonDestructive Testing(NDT)field.
基金founded by the National Natural Science Foundation of China(81202283,81473070,81373102 and81202267)Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China(10KJA330034 and11KJA330001)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20113234110002)the Priority Academic Program for the Development of Jiangsu Higher Education Institutions(Public Health and Preventive Medicine)
文摘With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.
