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
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
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
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
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
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
In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cla...In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.展开更多
Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application...Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.展开更多
In this paper,we formulate and analyze a new fractional-order Logistic model with feedback control,which is different from a recognized mathematical model proposed in our very recent work.Asymptotic stability of the p...In this paper,we formulate and analyze a new fractional-order Logistic model with feedback control,which is different from a recognized mathematical model proposed in our very recent work.Asymptotic stability of the proposed model and its numerical solutions are studied rigorously.By using the Lyapunov direct method for fractional dynamical systems and a suitable Lyapunov function,we show that a unique positive equilibrium point of the new model is asymptotically stable.As an important consequence of this,we obtain a new mathematical model in which the feedback control variables only change the position of the unique positive equilibrium point of the original model but retain its asymptotic stability.Furthermore,we construct unconditionally positive nonstandard finite difference(NSFD)schemes for the proposed model using the Mickens’methodology.It is worth noting that the constructed NSFD schemes not only preserve the positivity but also provide reliable numerical solutions that correctly reflect the dynamics of the new fractional-order model.Finally,we report some numerical examples to support and illustrate the theoretical results.The results indicate that there is a good agreement between the theoretical results and numerical ones.展开更多
Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmit...Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the <span style="font-family:Verdana;">main </span><span style="font-family:Verdana;">effects of genes and gene-covariate interaction</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;">. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results </span><span style="font-family:Verdana;">showed M235T is associat</span></span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female</span><span style="font-family:Verdana;">.</span>展开更多
Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance...Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with genetic algorithm is formulated . It is found that the company can produce an effective and efficient schedules to deploy the manpower and equipment resources. Simulation is used to verify the method and a MATLAB program is used to code the genetic algorithm. This model i s further illustrated by a case study in Hong Kong and the benefit elaborated. F inally, a conclusion is made to summarize the experience of this project and pro vide further improvement.展开更多
The massive scale of new-generation rural-urban migrants in China has attracted extensive scholarly attention in recent years.While previous studies on China’s rural migrant workers focus on migrants’settlement inte...The massive scale of new-generation rural-urban migrants in China has attracted extensive scholarly attention in recent years.While previous studies on China’s rural migrant workers focus on migrants’settlement intentions,migrants’family migration decision-making and the intergenerational differences between the old-generation migrants and new-generation migrants are underexplored.Based on the data of the 2017 China Migrants Dynamic Survey,this paper adopts a multilevel logistic regression approach to explore family and destination factors influencing the family migration decisions of China’s new generation of rural migrant workers.The empirical results reveal that both the migrants’family and destination attributes significantly influence their family migration decision.The demographic and socioeconomic characteristics of the family have been pivotal factors underlying the family migration decision of China’s new generation rural-urban migrants,while 16.9%of the chances are explained by between-destination differences.Self-employed migrants with housing properties in host cities,long migration duration and high-income levels are more likely to migrate with their family members.Yet,the possibility of family migration is found to be significantly and negatively correlated with the age,education level,number of children and inter-provincial mobility of the new generation of migrant workers.In addition,new-generation rural-urban migrants’family migration is more likely to be found in cities with service-oriented industry structures,better environmental quality,and higher hukou barriers which is possibly related to more job opportunities.These research findings not only complement the existing literature on China’s new generation of rural urban migrants,but also have important policy implications for reforming the hukou system and enhancing social integration of the rural-to-urban migrant population.展开更多
The conformable fractional derivative method has been utilized in order to examine the logistic model with constant harvesting.Such method introduces a generalization to the classical analysis of Logistic model,and he...The conformable fractional derivative method has been utilized in order to examine the logistic model with constant harvesting.Such method introduces a generalization to the classical analysis of Logistic model,and hence the features of the Logistic model,such as subcritical and supercritical harvesting,have been investigated in a view of fractional calculus.The positive auxiliary parameter,σ,with dimension of time is implemented to maintain the dimensionality of the system.The significant information of such parameter to the population has been discussed.The population expressions,obtained by conformable description,are compared with the expressions of the classical derivative.This comparison shows that the non-integer expressions are in a parallel line with that of the classical one.展开更多
A mathematical model capable of providing a forecast of future consumption and import of natural gas is essential for the planning of the Brazilian energy matrix. The aim of this study is to compare three mathematical...A mathematical model capable of providing a forecast of future consumption and import of natural gas is essential for the planning of the Brazilian energy matrix. The aim of this study is to compare three mathematical models, logistic model or model of Verhulst, exponential model or the model of Malthus and the model of von Bertalanffy to analyze the possibilities of these models to describe the evolution of production, import and consumption of natural gas in Brazil, from data provided by the energy balance of the Ministry of Mines and Energy (MME) from 1970 to 2009. A projection of the production and the import of natural gas up to 2017 is made with the models studied in this article and compared with the Brazilian Ten-Year Plan for Expansion of Energy (PDE). At the end of this paper a comparison with the Hubbert model for Brazilian natural gas production is made. These data were adjusted to use the differential equations which describe the models of population growth. All the computer work used in this article: graphics, resolution of differential equations, calculations of linearization and the least squares fitting was prepared in the software MatLab. The results obtained by means of graphs show that the population dynamics models (logistic, exponential and von Bertalanffy) can be applied in modeling the production, import and consumption of natural gas in Brazil.展开更多
By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Base...By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Based on the growth performance of different fungi at different temperatures and humidity,we use the method of systematic cluster to divide the fungi into 5 categories,and introduce competition levels as the viability of different species of fungi.We have established a logistic model that introduces competition levels to obtain a fungal habitat model.The fungal habitat model includes predictions about the relative advantages and disadvantages for each species and combinations of species likely to persist,and do so for different environments including arid,semi-arid,temperate,arboreal,and tropical rain forests.展开更多
In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,co...In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,could Chinese herbal medicines efficacy also be applied to predict the hepatotoxicity of Chinese herbal medicines?Therefore,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on Chinese herbal medicines efficacy has been tentatively set up to study the correlations of hepatotoxic and nonhepatotoxic Chinese herbal medicines with efficacy by using a chi-square test for two-way unordered categorical data.Logistic regression prediction model was established and the accuracy of the prediction by this model was evaluated.It has been found that the hepatotoxicity and nonhepatotoxicity of Chinese herbal medicines were weakly related to the efficacy,and the coefficient was 0.295.There were 20 variables from Chinese herbal medicines efficacy analyzed with unconditional logistic regression,and 6 variables,rectifying Qi and relieving pain,clearing heat and disinhibiting dampness,invigorating blood and stopping pain,invigorating blood and relieving swelling,killing worms and relieving fright were chosen to establish the logistic regression prediction model,with the optimal cutoff value being 0.250.Dissipating cold and relieving pain(DCRP),clearing heat and disinhibiting dampness,invigorating blood and relieving pain(IBRP),invigorating blood and relieving swelling,killing worms,and relieving fright were the variables to affect the hepatotoxicity and the established logistic regression prediction model had predictive power for hepatotoxicity of Chinese herbal medicines to a certain degree.展开更多
The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integ...The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.展开更多
According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affe...According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affected by deforestation and forest degradation.The paper attempts to study the impact of forest governance,enforcement and socio-economic factors on deforestation and forest degradation at the local level in West Bengal State,India.The study was based on questionnaire survey data during 2020–2021 collected from three western districts(Purulia,Bankura,and Paschim Medinipur)where deforestation and poverty rates are higher than other districts in West Bengal State.The total number of selected villages was 29,and the total sample households were 693.A stratified random sampling technique was used to collect data,and a questionnaire was followed.Forest governance and enforcement indices were constructed using United Nation Development Programme(UNDP)methodology and a step-wise logistic regression model was used to identify the factors affecting deforestation and forest degradation.The result of this study showed that four factors(illegal logging,weak forest administration,encroachment,and poverty)are identified for the causes of deforestation and forest degradation.It is observed that six indices of forest governance(rule of law,transparency,accountability,participation,inclusiveness and equitability,and efficiency and effectiveness)are relatively high in Purulia District.Moreover,this study shows that Purulia and Bankura districts follow medium forest governance,while Paschim Medinipur District has poor forest governance.The enforcement index is found to be highest in Purulia District(0.717)and lowest for Paschim Medinipur District(0.257).Finally,weak forest governance,poor socio-economic conditions of the households,and weak enforcement lead to the deforestation and forest degradation in the study area.Therefore,governments should strengthen law enforcement and encourage sustainable forest certification schemes to combat illegal logging.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagn...BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.展开更多
Food security issues become one of the critical concerns and top priority areas for Ethiopia.This study analyzed rural households’food security status and its determinants in Minjar Shenkora woreda of Amhara Regional...Food security issues become one of the critical concerns and top priority areas for Ethiopia.This study analyzed rural households’food security status and its determinants in Minjar Shenkora woreda of Amhara Regional State and Ada’a woreda of Oromia Regional State.Data were collected from 240 randomly selected rural farm households.The study employed both descriptive statistics and a binary logistic regression model to estimate the status and determinants of households’food security,respectively.The findings indicated that the average dietary energy available for food secured households was 2,860.6 kilo calorie per day while 1,891.7 kilo calorie per day for the insecure group.According to the findings of the binary logit model,factors such as education level,farm size,livestock ownership,cooperatives membership,off-farm income and credit access have positive and significant effects on household food security.While household size has a negative and significant effect on household food security.The results recommend that interventions should target at improving rural financial services and off-farm activities that increase households’income and focusing on those most significant variables when attempting to enhance household food security.展开更多
文摘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.
文摘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.
基金financially supported by the National Key Research and Development Program of China(2022YFD190160304)Natural Science Foundation of Sichuan Province(2022NSFSC0013)+1 种基金Sichuan Maize Innovation Team Construction Project(SCCXTD-2022-02)National Key Research and Development Program of China(2018YFD0301206)。
文摘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.
文摘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.
基金supported by Universiti Sultan Zainal Abidin(grant number:Uni SZA/2018/DPU/16)
文摘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.
文摘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.
文摘In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.
文摘Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.
文摘In this paper,we formulate and analyze a new fractional-order Logistic model with feedback control,which is different from a recognized mathematical model proposed in our very recent work.Asymptotic stability of the proposed model and its numerical solutions are studied rigorously.By using the Lyapunov direct method for fractional dynamical systems and a suitable Lyapunov function,we show that a unique positive equilibrium point of the new model is asymptotically stable.As an important consequence of this,we obtain a new mathematical model in which the feedback control variables only change the position of the unique positive equilibrium point of the original model but retain its asymptotic stability.Furthermore,we construct unconditionally positive nonstandard finite difference(NSFD)schemes for the proposed model using the Mickens’methodology.It is worth noting that the constructed NSFD schemes not only preserve the positivity but also provide reliable numerical solutions that correctly reflect the dynamics of the new fractional-order model.Finally,we report some numerical examples to support and illustrate the theoretical results.The results indicate that there is a good agreement between the theoretical results and numerical ones.
文摘Transmission disequilibrium test (TDT) is a popular family based genetic association method. Under multiplicative assumption, a conditional logistic regression for matched pair, affected offspring with allele transmitted from parents and pseudo-offspring (control) with allele non-transmitted from parents, was built to detect the <span style="font-family:Verdana;">main </span><span style="font-family:Verdana;">effects of genes and gene-covariate interaction</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;">. When there exist genotype uncertainties, expectation-maximization (EM) algorithm was adopted to estimate the coefficients. The transmission model was applied to detect the association between M235T polymorphism in AGT gene and essential hypertension (ESH). Most of parents are not available in the 126 families from HongKong Chinese population. The results </span><span style="font-family:Verdana;">showed M235T is associat</span></span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> with hypertension and there is interaction between M235T and the case’s sex. The allele T is higher risk for male than female</span><span style="font-family:Verdana;">.</span>
文摘Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with genetic algorithm is formulated . It is found that the company can produce an effective and efficient schedules to deploy the manpower and equipment resources. Simulation is used to verify the method and a MATLAB program is used to code the genetic algorithm. This model i s further illustrated by a case study in Hong Kong and the benefit elaborated. F inally, a conclusion is made to summarize the experience of this project and pro vide further improvement.
基金supported by the National Natural Science Foundation of China(Project Number:NSFC 71403193).
文摘The massive scale of new-generation rural-urban migrants in China has attracted extensive scholarly attention in recent years.While previous studies on China’s rural migrant workers focus on migrants’settlement intentions,migrants’family migration decision-making and the intergenerational differences between the old-generation migrants and new-generation migrants are underexplored.Based on the data of the 2017 China Migrants Dynamic Survey,this paper adopts a multilevel logistic regression approach to explore family and destination factors influencing the family migration decisions of China’s new generation of rural migrant workers.The empirical results reveal that both the migrants’family and destination attributes significantly influence their family migration decision.The demographic and socioeconomic characteristics of the family have been pivotal factors underlying the family migration decision of China’s new generation rural-urban migrants,while 16.9%of the chances are explained by between-destination differences.Self-employed migrants with housing properties in host cities,long migration duration and high-income levels are more likely to migrate with their family members.Yet,the possibility of family migration is found to be significantly and negatively correlated with the age,education level,number of children and inter-provincial mobility of the new generation of migrant workers.In addition,new-generation rural-urban migrants’family migration is more likely to be found in cities with service-oriented industry structures,better environmental quality,and higher hukou barriers which is possibly related to more job opportunities.These research findings not only complement the existing literature on China’s new generation of rural urban migrants,but also have important policy implications for reforming the hukou system and enhancing social integration of the rural-to-urban migrant population.
文摘The conformable fractional derivative method has been utilized in order to examine the logistic model with constant harvesting.Such method introduces a generalization to the classical analysis of Logistic model,and hence the features of the Logistic model,such as subcritical and supercritical harvesting,have been investigated in a view of fractional calculus.The positive auxiliary parameter,σ,with dimension of time is implemented to maintain the dimensionality of the system.The significant information of such parameter to the population has been discussed.The population expressions,obtained by conformable description,are compared with the expressions of the classical derivative.This comparison shows that the non-integer expressions are in a parallel line with that of the classical one.
文摘A mathematical model capable of providing a forecast of future consumption and import of natural gas is essential for the planning of the Brazilian energy matrix. The aim of this study is to compare three mathematical models, logistic model or model of Verhulst, exponential model or the model of Malthus and the model of von Bertalanffy to analyze the possibilities of these models to describe the evolution of production, import and consumption of natural gas in Brazil, from data provided by the energy balance of the Ministry of Mines and Energy (MME) from 1970 to 2009. A projection of the production and the import of natural gas up to 2017 is made with the models studied in this article and compared with the Brazilian Ten-Year Plan for Expansion of Energy (PDE). At the end of this paper a comparison with the Hubbert model for Brazilian natural gas production is made. These data were adjusted to use the differential equations which describe the models of population growth. All the computer work used in this article: graphics, resolution of differential equations, calculations of linearization and the least squares fitting was prepared in the software MatLab. The results obtained by means of graphs show that the population dynamics models (logistic, exponential and von Bertalanffy) can be applied in modeling the production, import and consumption of natural gas in Brazil.
文摘By using the OLS model,an equation for the rate of decomposing wood by a variety of fungi was established.We analyzed the effects of various fungi in the experimental data under different temperature and humidity.Based on the growth performance of different fungi at different temperatures and humidity,we use the method of systematic cluster to divide the fungi into 5 categories,and introduce competition levels as the viability of different species of fungi.We have established a logistic model that introduces competition levels to obtain a fungal habitat model.The fungal habitat model includes predictions about the relative advantages and disadvantages for each species and combinations of species likely to persist,and do so for different environments including arid,semi-arid,temperate,arboreal,and tropical rain forests.
基金This work was supported by the Project of National Natural Science Foundation of China(No.82074306)the Shenzhen Health and Family Planning System Research Project(No.SZBC2018007)the Project of Traditional Chinese Medicine Bureau of Guangdong Province(No.20201073).
文摘In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,could Chinese herbal medicines efficacy also be applied to predict the hepatotoxicity of Chinese herbal medicines?Therefore,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on Chinese herbal medicines efficacy has been tentatively set up to study the correlations of hepatotoxic and nonhepatotoxic Chinese herbal medicines with efficacy by using a chi-square test for two-way unordered categorical data.Logistic regression prediction model was established and the accuracy of the prediction by this model was evaluated.It has been found that the hepatotoxicity and nonhepatotoxicity of Chinese herbal medicines were weakly related to the efficacy,and the coefficient was 0.295.There were 20 variables from Chinese herbal medicines efficacy analyzed with unconditional logistic regression,and 6 variables,rectifying Qi and relieving pain,clearing heat and disinhibiting dampness,invigorating blood and stopping pain,invigorating blood and relieving swelling,killing worms and relieving fright were chosen to establish the logistic regression prediction model,with the optimal cutoff value being 0.250.Dissipating cold and relieving pain(DCRP),clearing heat and disinhibiting dampness,invigorating blood and relieving pain(IBRP),invigorating blood and relieving swelling,killing worms,and relieving fright were the variables to affect the hepatotoxicity and the established logistic regression prediction model had predictive power for hepatotoxicity of Chinese herbal medicines to a certain degree.
基金supported by the Chinese National Special Fund for Agro-scientific Research in the Public Interest (201003025 and 201103022)the National Key Research and Development Program of China (2018YFD0201004)the Discipline Construction Project of Liaoning Academy of Agricultural Sciences, China (2019DD082612)。
文摘The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.
文摘According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affected by deforestation and forest degradation.The paper attempts to study the impact of forest governance,enforcement and socio-economic factors on deforestation and forest degradation at the local level in West Bengal State,India.The study was based on questionnaire survey data during 2020–2021 collected from three western districts(Purulia,Bankura,and Paschim Medinipur)where deforestation and poverty rates are higher than other districts in West Bengal State.The total number of selected villages was 29,and the total sample households were 693.A stratified random sampling technique was used to collect data,and a questionnaire was followed.Forest governance and enforcement indices were constructed using United Nation Development Programme(UNDP)methodology and a step-wise logistic regression model was used to identify the factors affecting deforestation and forest degradation.The result of this study showed that four factors(illegal logging,weak forest administration,encroachment,and poverty)are identified for the causes of deforestation and forest degradation.It is observed that six indices of forest governance(rule of law,transparency,accountability,participation,inclusiveness and equitability,and efficiency and effectiveness)are relatively high in Purulia District.Moreover,this study shows that Purulia and Bankura districts follow medium forest governance,while Paschim Medinipur District has poor forest governance.The enforcement index is found to be highest in Purulia District(0.717)and lowest for Paschim Medinipur District(0.257).Finally,weak forest governance,poor socio-economic conditions of the households,and weak enforcement lead to the deforestation and forest degradation in the study area.Therefore,governments should strengthen law enforcement and encourage sustainable forest certification schemes to combat illegal logging.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.
文摘Food security issues become one of the critical concerns and top priority areas for Ethiopia.This study analyzed rural households’food security status and its determinants in Minjar Shenkora woreda of Amhara Regional State and Ada’a woreda of Oromia Regional State.Data were collected from 240 randomly selected rural farm households.The study employed both descriptive statistics and a binary logistic regression model to estimate the status and determinants of households’food security,respectively.The findings indicated that the average dietary energy available for food secured households was 2,860.6 kilo calorie per day while 1,891.7 kilo calorie per day for the insecure group.According to the findings of the binary logit model,factors such as education level,farm size,livestock ownership,cooperatives membership,off-farm income and credit access have positive and significant effects on household food security.While household size has a negative and significant effect on household food security.The results recommend that interventions should target at improving rural financial services and off-farm activities that increase households’income and focusing on those most significant variables when attempting to enhance household food security.
