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Genetic variation of height growth rhythm between clones of Larix kaempferi × L. gmelini based on logistic models 被引量:1
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作者 Chunming Li Hui Xia +4 位作者 Hui Bai Hongmei Wang Yajuan Xing Xiyang Zhao Xiaomei Sun 《Journal of Forestry Research》 SCIE CAS CSCD 2018年第5期1387-1394,共8页
Fifty-three larch interspecific hybrid clones(Larix kaempferi × L.gmelini) and their parent clones were used for growth curve analysis of height variations.The growth curves of the 55 clones were 'S'-shaped a... Fifty-three larch interspecific hybrid clones(Larix kaempferi × L.gmelini) and their parent clones were used for growth curve analysis of height variations.The growth curves of the 55 clones were 'S'-shaped and 36 exhibited similar curves as the male parent.The coefficients of the logistic models were higher than 0.943,indicating that our results were effective in the simulation of the growth curves.ANOVA analysis showed significant differences in height of different clones (P/0.01).Average date of maximum height growth was Day 173,and average duration of rapid growth lasted for 50 days.Annual average increase in height was 9.7cm d^(-1) and daily average increase was 0.2 cm.The ratio of GR to the total annual increase in height ranged from 51.2 to 68.8%,with the average being 59.8%.There was a positive correlation between k values and plant heights which benefited from the evaluation of early plant height.There was also a positive correlation between GR(growth stage),GD(plant height) and annual increase in height.These results are informative to the evaluation of the elite clone selection and provide a theoretical basis for breeding and management. 展开更多
关键词 Larix kaempferi ×L. gmelini Hybrid clones logistic modeling Plant height variation
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Enhancing PDF Malware Detection through Logistic Model Trees
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作者 Muhammad Binsawad 《Computers, Materials & Continua》 SCIE EI 2024年第3期3645-3663,共19页
Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection a... Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape. 展开更多
关键词 Malware detection PDF files logistic model tree feature selection CYBERSECURITY
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Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression
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作者 Yuqiao Zhou Xinyang Xu Wenjing Ma 《Applied Mathematics》 2024年第1期51-64,共14页
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, cluste... 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. 展开更多
关键词 Glass Composition L1 Regularization logistic Regression Model K-Means Clustering Analysis Elbow Rule Parameter Verification
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Prediction of cyanotic and acyanotic congenital heart disease using machine learning models
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作者 Sana Shahid Haris Khurram +2 位作者 Apiradee Lim Muhammad Farhan Shabbir Baki Billah 《World Journal of Clinical Pediatrics》 2024年第4期15-24,共10页
BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicti... BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan. 展开更多
关键词 Congenital heart disease Cyanotic heart disease Acyanotic heart disease logistic regression model Artificial neural network
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Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships 被引量:3
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作者 Cheng-Wu CHEN Hsien-Chueh Peter YANG +2 位作者 Chen-Yuan CHEN Alex Kung-Hsiung CHANG Tsung-Hao CHEN 《China Ocean Engineering》 SCIE EI 2008年第1期43-56,共14页
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
关键词 binary logistic regression cumulative logistic regression model GOODNESS-OF-FIT internal solitary wave amplitude-based transmission rate
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Partial Oscillation of m-dimensional Logistic Ecologic Models 被引量:1
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作者 Luo Qi(Department of Basic Science, Wuhan Yejin University of Science and Technology, Wuhan 430081, China) 《Wuhan University Journal of Natural Sciences》 CAS 1998年第1期5-10,共6页
We present and discuss the partial oscillation with respect to equilibrium state ofm-dimensional Logistic delay ecologic models, and obtain some simple criteria.
关键词 logistic ecologic model partial oscillation diffusion CONSTANT Liapunov functional
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Performance Comparison Between Logistic and Generalized Surplus-Production Models Applied to the Sillago sihama Fishery in Pakistan 被引量:1
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作者 Sher Khan Panhwar Shabir Ali Amir +1 位作者 Muhsan Ali Kalhoro1 LIU Qun 《Journal of Ocean University of China》 SCIE CAS 2012年第3期401-407,共7页
The catch and effort data of Sillago sihama fishery in Pakistani waters were used to investigate the performance of two closely related stock assessment models: logistic and generalized surplus-production models. Comp... The catch and effort data of Sillago sihama fishery in Pakistani waters were used to investigate the performance of two closely related stock assessment models: logistic and generalized surplus-production models. Compared with the generalized production model, the logistic model produced more reasonable estimates for parameters such as maximum sustainable yield. The Akaike's Information Criterion values estimated at 4.265 and -51.152 respectively by the logistic and generalized models. Simulation analyses of the S. sihama fishery showed that the estimated and observed abundance indices for the logistic model were closer than those for the generalized production model. Standardized residuals were distributed closer for logistic model, but exhibited a slightly increasing trend for the generalized model. Statistical outliers were seen in 1989 and 1993 for the logistic model, and in 1981 and 1999 for the generalized model. Simulated results revealed that the logistic estimates were close to the true value for low CVs (coefficients of variation) but widely dispersed for high CVs. In contrast, the generalized model estimates were loose for all CV levels. The estimated production model curve parameter was not reasonable at all the tested levels of white noise. With the increase in white noise R2 for the catch per unit effort decreased. Therefore, we conclude that the logistic model performs more reasonably than the generalized production model. 展开更多
关键词 Pakistan Sillago sihama logistic surplus-production model generalized surplus-production model
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Adequacy of Logistic models for describing the dynamics of COVID-19 pandemic
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作者 Abdallah Abusam Razan Abusam Bader Al-Anzi 《Infectious Disease Modelling》 2020年第1期536-542,共7页
Logistic models have been widely used for modelling the ongoing COVID-19 pandemic.This study used the data for Kuwait to assess the adequacy of the two most commonly used logistic models(Verhulst and Richards models)f... Logistic models have been widely used for modelling the ongoing COVID-19 pandemic.This study used the data for Kuwait to assess the adequacy of the two most commonly used logistic models(Verhulst and Richards models)for describing the dynamics COVID-19.Specifically,the study assessed the predictive performance of these two models and the practical identifiability of their parameters.Two model calibration approaches were adopted.In the first approach,all the data was used to fit the models as per the heuristic model fitting method.In the second approach,only the first half of the data was used for calibrating the models,while the other half was left for validating the models.Analysis of the obtained calibration and validation results have indicated that parameters of the two models cannot be identified with high certainty from COVID-19 data.Further,the models shown to have structural problems as they could not predict reasonably the validation data.Therefore,they should not be used for long-term predictions of COVID-19.Suggestion have been made for improving the performances of the models. 展开更多
关键词 Infectious disease modeling logistic growth models Parameter identification Model performance COVID-19 in Kuwait
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Impulsive Logistic Model for Gray Leaf Spots Caused by Cercospora zeae-maydi 被引量:1
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作者 王新一 李丽梅 李海春 《Plant Diseases and Pests》 CAS 2010年第3期9-10,43,共3页
[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observ... [ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observed and recorded. Impulsive Logistic Model was used to simulate the development process of the disease, which was compared with actual incidence. [ Result] Artificial inoculation tests showed that impulsive Logistic Model could reflect time dynamic of C. zeae-maydi. Through derivation, exponential growth phase was from maize seedling emergence to eady July in each year, logistic phase was from early July to late August, terminal phase was from eady September to the end of maize growth stage. [ Conclusion] The derivation result from model was consistent with the development biological laws of C. zeae-maydi. 展开更多
关键词 C. zeae-maydi Impulsive logistic Model Epidemic phase Control time
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Synergistic effects of planting density and nitrogen fertilization on chlorophyll degradation and leaf senescence after silking in maize
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作者 Tianqiong Lan Lunjing Du +9 位作者 Xinglong Wang Xiaoxu Zhan Qinlin Liu Gui Wei Chengcheng Lyu Fan Liu Jiaxu Gao Dongju Feng Fanlei Kong Jichao Yuan 《The Crop Journal》 SCIE CSCD 2024年第2期605-613,共9页
Regulating planting density and nitrogen(N)fertilization could delay chlorophyll(Chl)degradation and leaf senescence in maize cultivars.This study measured changes in ear leaf green area(GLA_(ear)),Chl content,the act... Regulating planting density and nitrogen(N)fertilization could delay chlorophyll(Chl)degradation and leaf senescence in maize cultivars.This study measured changes in ear leaf green area(GLA_(ear)),Chl content,the activities of Chl a-degrading enzymes after silking,and the post-silking dry matter accumulation and grain yield under multiple planting densities and N fertilization rates.The dynamic change of GLA_(ear)after silking fitted to the logistic model,and the GLA_(ear) duration and the GLAearat 42 d after silking were affected mainly by the duration of the initial senescence period(T_(1))which was a key factor of the leaf senescence.The average chlorophyllase(CLH)activity was 8.3 times higher than pheophytinase activity and contributed most to the Chl content,indicating that CLH is a key enzyme for degrading Chl a in maize.Increasing density increased the CLH activity and decreased the Chl content,T1,GLAear,and GLA_(ear) duration.Under high density,appropriate N application reduced CLH activity,increased Chl content,prolonged T1,alleviated high-density-induced leaf senescence,and increased post-silking dry matter accumulation and grain yield. 展开更多
关键词 DENSITY Nitrogen fertilization Leaf senescence Chlorophyll-degrading enzyme logistic model
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Comparison of machine learning models for gully erosion susceptibility mapping 被引量:8
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作者 Alireza Arabameri Wei Chen +6 位作者 Marco Loche Xia Zhao Yang Li Luigi Lombardo Artemi Cerda Biswajeet Pradhan Dieu Tien Bui 《Geoscience Frontiers》 SCIE CAS CSCD 2020年第5期1609-1620,共12页
Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it o... Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application. 展开更多
关键词 Oil erosion GIS Alternating decision tree model logistic model tree model
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Extracting Vegetation Phenology Metrics in Changbai Mountains Using an Improved Logistic Model 被引量:4
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作者 LI Ming WU Zhengfang +1 位作者 QIN Lijie MENG Xiangjun 《Chinese Geographical Science》 SCIE CSCD 2011年第3期304-311,共8页
Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this m... Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics. 展开更多
关键词 logistic model SPOT/NDVI phenology metrics Changbai Mountains
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Predictors of work injury in underground mines—an application of a logistic regression model 被引量:5
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作者 P. S. Paul 《Mining Science and Technology》 EI CAS 2009年第3期282-289,共8页
Mine accidents and injuries are complex and generally characterized by several factors starting from personal to technical, and technical to social characteristics.In this study, an attempt has been made to identify t... Mine accidents and injuries are complex and generally characterized by several factors starting from personal to technical, and technical to social characteristics.In this study, an attempt has been made to identify the various factors responsible for work related injuries in mines and to estimate the risk of work injury to mine workers.The prediction of work injury in mines was done by a step-by-step multivariate logistic regression modeling with an application to case study mines in India.In total, 18 variables were considered in this study.Most of the variables are not directly quantifiable.Instruments were developed to quantify them through a questionnaire type survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.Four variables, age, negative affectivity, job dissatisfaction, and physical hazards, bear significant discriminating power for risk of injury to the workers, comparing between cases and controls in a multivariate situation while controlling all the personal and socio-technical variables.The analysis reveals that negatively affected workers are 2.54 times more prone to injuries than the less negatively affected workers and this factor is a more important risk factor for the case-study mines.Long term planning through identification of the negative individuals, proper counseling regarding the adverse effects of negative behaviors and special training is urgently required.Care should be taken for the aged and experienced workers in terms of their job responsibility and training requirements.Management should provide a friendly atmosphere during work to increase the confidence of the injury prone miners. 展开更多
关键词 mine safety logistic model case control study occupational injury
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PERSISTENCE AND EXTINCTION OF A STOCHASTIC LOGISTIC MODEL WITH DELAYS AND IMPULSIVE PERTURBATION 被引量:2
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作者 卢春 丁效华 《Acta Mathematica Scientia》 SCIE CSCD 2014年第5期1551-1570,共20页
A stochastic logistic model with delays and impulsive perturbation is proposed and investigated. Sufficient conditions for extinction are established as well as nonpersistence in the mean, weak persistence and stochas... A stochastic logistic model with delays and impulsive perturbation is proposed and investigated. Sufficient conditions for extinction are established as well as nonpersistence in the mean, weak persistence and stochastic permanence. The threshold between weak persistence and extinction is obtained. Furthermore, the theoretical analysis results are also derivated with the help of numerical simulations. 展开更多
关键词 logistic model white noise DELAY PERSISTENCE impulsive perturbation
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Transitions in a Logistic Growth Model Induced by Noise Coupling and Noise Color 被引量:2
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作者 SHI Jin ZHU Shi-Qun 《Communications in Theoretical Physics》 SCIE CAS CSCD 2006年第1X期175-182,共8页
With unified colored noise approximation, the logistic growth model is used to analyze cancer cell population when colored noise is included. It is found that both the coupling between noise terms and the noise color... With unified colored noise approximation, the logistic growth model is used to analyze cancer cell population when colored noise is included. It is found that both the coupling between noise terms and the noise color can induce continuous first-order-like and re-entrance-like phase transitions in the system. The coupling and the noise color can also increase tumor cell growth for small number of cell mass and repress tumor cell growth for large number of cell mass. It is shown that the approximate analytic expressions are consistent with the numerical simulations. 展开更多
关键词 logistic growth model first-order-like phase transition re-entrance-like phase transition colored noise
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Predictors of in-hospital mortality by logistic regression analysis among melioidosis patients in Northern Malaysia:A retrospective study 被引量:1
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作者 Kamaruddin Mardhiah Nadiah Wan-Arfah +2 位作者 Nyi Nyi Naing Muhammad Radzi Abu Hassan Huan-Keat Chan 《Asian Pacific Journal of Tropical Medicine》 SCIE CAS 2021年第8期356-363,共8页
Objective:To identify the predictors of mortality among in-hospital melioidosis patients.Methods:A total of 453 patients in Hospital Sultanah Bahiyah,Kedah,and Hospital Tuanku Fauziah,Perlis with culture-confirmed mel... Objective:To identify the predictors of mortality among in-hospital melioidosis patients.Methods:A total of 453 patients in Hospital Sultanah Bahiyah,Kedah,and Hospital Tuanku Fauziah,Perlis with culture-confirmed melioidosis were retrospectively included in the study.Advanced multiple logistic regression was used to obtain the final model of predictors of mortality from melioidosis.The analysis was performed using STATA/SE 14.0.Results:A total of 50.11%(227/453)of the patients died at the hospital,and a majority(86.75%,393/453)of cases were bacteremic.The logistic regression estimated that the bacteremic type of melioidosis,low platelet count,abnormal white blood cell counts,and increased urea value were predictors of mortality.The results showed that bacteremic melioidosis increased the risk of death by 4.39 times(OR 4.39,95%CI 1.83-10.55,P=0.001)compared to non-bacteremic melioidosis.Based on laboratory test,the adjusted ORs from the final model showed that all three blood investigations were included as the associated factors of mortality for the disease[high white blood cell(>10×10^(9)/L):OR 2.43,95%CI1.41-4.17,P<0.001;low white blood cell(<4×10^(9)/L):OR 3.82,95%CI 1.09-13.34,P=0.036;low platelet(<100×10^(9)/L):OR 4.19,95%CI 1.89-9.30,P<0.001;high urea(>7800μmol/L):OR 5.53,95%CI 2.50-12.30,P<0.001;and low level of urea(<2500μmol/L):OR 3.52,95%CI 1.71-7.23,P=0.001].Conclusions:Routine blood investigations during a hospital admission can early identify predictors of mortality in melioidosis patients. 展开更多
关键词 MELIOIDOSIS Infectious disease MORTALITY PREDICTORS Prognostic factors logistic model
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The walking distance decay law of amenity selection based on binary logistic model 被引量:1
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作者 Xu Dandan Bian Yang +1 位作者 Shu Shinan Rong Jian 《Journal of Southeast University(English Edition)》 EI CAS 2020年第1期88-97,共10页
The current measuring methods of walkability,such as the walk score,consider that walking distance decay laws for all amenities are the same,which is not applicable to typical communities in China with plentiful resou... The current measuring methods of walkability,such as the walk score,consider that walking distance decay laws for all amenities are the same,which is not applicable to typical communities in China with plentiful resources.Therefore,the walking distance decay laws of multi-type and multi-scale facilities are studied.Firstly,based on the residents'amenity selection survey,the walking distance decay law of residents'choice of amenity was studied from three aspects,including the law of all amenities,the laws of different types of amenities and the laws of different scales of amenities.It was proved that the walking distance decay laws of different kinds of amenities showed a significant difference.Secondly,different amenities'acceptable walking distance and optimum walking distance were obtained according to previous studies and the decay curve.Amenities with higher attraction and/or a larger scale showed a longer acceptable walking distance and optimum walking distance.Finally,the binary logistic model was used to describe the relationships between walking distance,amenity type,amenity scale and the probability of one amenity being selected,the prediction accuracy of which reached 80.4%.The calculated probability obtained from the model can be used as the decay coefficient of amenities in the measurement of walkability,providing a reference for the site selection and evaluation of amenities. 展开更多
关键词 WALKABILITY walking distance distance decay amenity binary logistic model
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Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure 被引量:1
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作者 P.S.Anoop V.Sugumaran 《Structural Durability & Health Monitoring》 EI 2017年第2期191-208,共18页
Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A diffe... Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully. 展开更多
关键词 Machine learning Vibration ACCELEROMETER Statistical Features Histogram Features logistic model tree(LMT) Tyre pressure monitoring system
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POSITIVE PERIODIC SOLUTION FOR A NONAUTONOMOUS LOGISTIC MODEL WITH LINEAR FEEDBACK REGULATION 被引量:1
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作者 Ding Xiaoquan Cheng Shuhan 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2006年第3期302-312,共11页
A nonautonomous delayed logistic model with linear feedback regulation is proposed in this paper. Sufficient conditions are derived for the existence, uniqueness and global asymptotic stability of positive periodic so... A nonautonomous delayed logistic model with linear feedback regulation is proposed in this paper. Sufficient conditions are derived for the existence, uniqueness and global asymptotic stability of positive periodic solution of the model 展开更多
关键词 logistic model periodic solution global asymptotic stability linear feedback regulation.
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A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET 被引量:1
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作者 庞素琳 邓飞其 王燕鸣 《Acta Mathematica Scientia》 SCIE CSCD 2007年第1期125-136,共12页
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 o... 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. 展开更多
关键词 logistic regression model AR(1) model AR(2) model VOLATILITY
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