BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced N...BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.展开更多
AIM: To construct tree models for classification of diffuse large B-cell lymphomas (DLBCL) by chromosome copy numbers, to compare them with cDNA microarray classification, and to explore models of multi-gene, multi-st...AIM: To construct tree models for classification of diffuse large B-cell lymphomas (DLBCL) by chromosome copy numbers, to compare them with cDNA microarray classification, and to explore models of multi-gene, multi-step and multi-pathway processes of DLBCL tumorigenesis. METHODS: Maximum-weight branching and distancebased models were constructed based on the comparative genomic hybridization (CGH) data of 123 DLBCL samples using the established methods and software of Desper et al . A maximum likelihood tree model was also used to analyze the data. By comparing with the results reported in literature, values of tree models in the classification of DLBCL were elucidated. RESULTS: Both the branching and the distance-based trees classified DLBCL into three groups. We combined the classification methods of the two models and classified DLBCL into three categories according to their characteristics. The first group was marked by +Xq, +Xp, -17p and +13q; the second group by +3q, +18q and +18p; and the third group was marked by -6q and +6p. This chromosomal classification was consistent with cDNA classification. It indicated that -6q and +3q were two main events in the tumorigenesis of lymphoma. CONCLUSION: Tree models of lymphoma established from CGH data can be used in the classification of DLBCL. These models can suggest multi-gene, multistep and multi-pathway processes of tumorigenesis. Two pathways, -6q preceding +6q and +3q preceding+18q, may be important in understanding tumorigenesis of DLBCL. The pathway, -6q preceding +6q, may have a close relationship with the tumorigenesis of non-GCB DLBCL.展开更多
In order to effectively avoid the defects of a traditional discounted cash flow method, a trinomial tree pricing model of the real option is improved and used to forecast the investment price of mining. Taking Molybde...In order to effectively avoid the defects of a traditional discounted cash flow method, a trinomial tree pricing model of the real option is improved and used to forecast the investment price of mining. Taking Molybdenum ore as an example, a theoretical model for the hurdle price under the optimal investment timing is constructed. Based on the example data, the op- tion price model is simulated. By the model, mine investment price can be computed and forecast effectively. According to the characteristics of mine investment, cut-off grade, reserve estimation and mine life in different price also can be quantified. The result shows that it is reliable and practical to enhance the accuracy for mining investment decision.展开更多
In the present article it will be critically questioned the traditional entrepreneurship education approaches based on a narrow conception of competency, and their values. Assuming the perspective that to be an entrep...In the present article it will be critically questioned the traditional entrepreneurship education approaches based on a narrow conception of competency, and their values. Assuming the perspective that to be an entrepreneur is basically an attitude towards life and the world, there proposed holistic, constructivist and experiential processes and strategies for entrepreneurship education. The ＂entrepreneur XXI＂, must be able to undertake a social function of change, so, an economical and social development more human, ethical and intelligent. Under this assumption, the ＂Tree Model for the Development of Entrepreneurial Competencies＂, that will be discussed globally in the second part of this article, suggests a dynamic and experiential approach ofentrepreneurship education based on the qualification of people＇s behaviour, self-esteem, competencies and experiences; a profile of key behavioural and performance competencies （root）, experimental pedagogical procedures （trunk） and real results within group projects （fruits）. This model has been developed during the last decade （2001-2011）, using a multidisciplinary research-action procedure, within business, education （at different teaching levels） and social project environments.展开更多
OBJECTIVE: To explore the features of Traditional Chinese Medicine(TCM) syndromes in male infertility using computer-based analyses.METHODS: Latent class analysis was used to analyze the TCM syndrome data from 813 pat...OBJECTIVE: To explore the features of Traditional Chinese Medicine(TCM) syndromes in male infertility using computer-based analyses.METHODS: Latent class analysis was used to analyze the TCM syndrome data from 813 patients with male infertility and establish a latent tree model.RESULTS: A latent tree model with a Bayesian information criterion score of-11 263 was created.This model revealed that the characteristics of basic TCM syndromes in patients with male infertility were kidney Yang deficiency, kidney Qi deficiency,spleen Yang deficiency, liver Qi stagnation, Qi stagnation and blood stasis, and dump-heat; moreover,most patients with male infertility had complex syndromes(spleen-kidney Yang deficiency and liver Qi stagnation) rather than simple single syndromes.CONCLUSION: The hidden tree model analysis revealed the objective and quantitative complex relationships between the TCM symptoms of male infertility, and obtained the quantification and objective evidence of TCM syndromes in male infertility.展开更多
Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent ...Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.展开更多
Introduction: As far as adult and married women were concerned, when they occurred to “unplanned pregnancy”, they felt so surprised and concussive all the time. Besides, the unplanned pregnancy also affects the othe...Introduction: As far as adult and married women were concerned, when they occurred to “unplanned pregnancy”, they felt so surprised and concussive all the time. Besides, the unplanned pregnancy also affects the other members in the family system. Therefore, when married women have to face the choice: “birth” or “abortion”, they’ll consider lots of thoughts and different decision criteria and decision pattern under various influences on physician, mind, mental and society. The purpose of this study was to investigate the criteria considered and the decision patterns involved when adult married women decide whether to terminate or continue an unplanned pregnancy. Methods: The study uses the method—“Ethnographic Decision Tree Modeling” [1] to build model of the decision criteria and decision patterns involved when adult married women make a decision about their unplanned pregnancy. There are three process in the research method: “Pilot Study”—interview two groups, every group distinct 4 married adult women with unplanned pregnancies, which decide whether to terminate or continue an unplanned pregnancy, what is the items of decision characters affect to the choice: “birth” or “abortion”. “Building of the Model”, displays the importance in proper order of those items and build the modeling with these two groups of women. “Testing of the Model”: investigate the criteria considered and the decision patterns involved when adult married women decide whether to terminate or continue an unplanned pregnancy. The study interviewed 34 married adult women with 43 unplanned pregnancies totally. Results: The result of the study finds out 12 items of decision characters, including planning to get pregnant or not, stability of feelings for married partner, the points of view on life, was affected by mother, mother-in-law, an husband’s emphasis on male, the meanings of children, the financial burden, the plan an assignment of career and time, the past pregnant experiences, the status of raising children, the health of parents and fetus, the effect of living environment, and social and cultural vision. Besides, there are four decision patterns of married adult women with unplanned pregnancy are “receiving abortion positively”;“giving birth as long as getting pregnancy naturally”;“ the minds are hesitative and changeable”, and “being forced by important others.” Conclusion: By setting the decision model tree, we found several decision criteria and patterns, and possible modes actions to be taken, could offer to see the adult married women’s decision-making and struggles in mind about unplanned pregnancy.展开更多
The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the ...The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the target variable. To solve this problem, a new optimization method is proposed. Using this method, the probability that the predictors used in splitting rules in the optimized tree model have no functional relationships with the target variable is confined to less than 0.05. It is fairly convincing that the tree model given by the new method represents knowledge contained in the data.展开更多
In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regressio...In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen.展开更多
Safety Critical Systems (SCS) are those systems that may cause harm to the user(s) and/or the environment if operating outside of their prescribed specifications. Such systems are used in a wide variety of domains, su...Safety Critical Systems (SCS) are those systems that may cause harm to the user(s) and/or the environment if operating outside of their prescribed specifications. Such systems are used in a wide variety of domains, such as aerospace, automotive, railway transportation and healthcare. In this paper, we propose an approach to integrate safety analysis of SCSs within the Model Driven Engineering (MDE) system development process. The approach is based on model transformation and uses standard well-known techniques and open source tools for the modeling and analysis of SCSs. More specifically, the system modeled with the OMG’s standard systems modeling language, SysML, is automatically transformed in Fault Tree (FT) models, that can be analyzed with existing FT tools. The proposed model transformation takes place in two steps: a) generate FTs at the component level, in order to tackle complexity and enable reuse;and b) generate system level FTs by composing the components and their FTs. The approach is illustrated by applying it to a simplified industry-inspired case study.展开更多
Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamo...Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamomi and P.×cambivora,is a growing concern for sweet chestnut stands(Castanea sativa)in Europe.Since both pathogens are thermophilic organisms,ongoing climate change will likely exacerbate their impact.In this study,we applied species distribution modeling techniques to identify poten-tial substitutive species for sweet chestnut in the light of future climate scenarios SSP126 and SSP370 in southern Switzerland.Using the presence-only machine learning algorithm MaxEnt and leveraging occurrence data from the global dataset GBIF,we delineated the current and projected(2070-2100)distribution of 28 tree species.Several exotic species emerged as valuable alternatives to sweet chestnut,although careful consideration of all potential ecological consequences is required.We also identified several native tree species as promising substitutes,offering ecological benefits and potential adaptability to climatic conditions.Since species diversification fosters forest resilience,we also determined communities of alternative species that can be grown together.Our findings represent a valuable deci-sion tool for forest managers confronted with the challenges posed by ink disease and climate change.Given that,even in absence of disease,sweet chestnut is not a future-proof tree species in the study region,the identified species could offer a pathway toward resilient and sustainable forests within the entire chestnut belt.展开更多
Due to its ability to broaden the transport channel of droplets within the plant canopy and enhance their penetration capacity,air-assisted spray technology is widely used in orchard pesticide application.To achieve u...Due to its ability to broaden the transport channel of droplets within the plant canopy and enhance their penetration capacity,air-assisted spray technology is widely used in orchard pesticide application.To achieve uniform distribution of pesticide droplets in the tree canopy and obtain a higher pesticide utilization rate,it is crucial to clarify the coupling mechanism of the airflow field and droplet field generated by the air-assisted sprayer.This paper introduces a three-dimensional modeling method of the fruit tree canopy based on CFD(Computational Fluid Dynamics),offering a theoretical basis for analyzing the airflow demand calculation during different growth periods of the canopy.It also examines the interaction between canopy modeling and airflow,highlighting advancements in airflow regulation equipment and the effects of airflow speed and volume on spraying.The study shows that the precise regulation of airflow velocity and discharge rate is of importance for improving spraying efficiency.It finally points out that future research should focus on developing intelligent regulation equipment for efficient airflow-droplet control,using biomass sensing,which involves measuring the growth characteristics of the tree canopy,to meet the needs of orchards with diverse growth stages and canopy structures.This article could provide guidance for the future study of precision air-assisted spraying technology in orchards.展开更多
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
With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at lo...With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.展开更多
文摘BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.
基金Science and Technology Project of Guangzhou, No. 2002Z3-E4016 No. B30101, China
文摘AIM: To construct tree models for classification of diffuse large B-cell lymphomas (DLBCL) by chromosome copy numbers, to compare them with cDNA microarray classification, and to explore models of multi-gene, multi-step and multi-pathway processes of DLBCL tumorigenesis. METHODS: Maximum-weight branching and distancebased models were constructed based on the comparative genomic hybridization (CGH) data of 123 DLBCL samples using the established methods and software of Desper et al . A maximum likelihood tree model was also used to analyze the data. By comparing with the results reported in literature, values of tree models in the classification of DLBCL were elucidated. RESULTS: Both the branching and the distance-based trees classified DLBCL into three groups. We combined the classification methods of the two models and classified DLBCL into three categories according to their characteristics. The first group was marked by +Xq, +Xp, -17p and +13q; the second group by +3q, +18q and +18p; and the third group was marked by -6q and +6p. This chromosomal classification was consistent with cDNA classification. It indicated that -6q and +3q were two main events in the tumorigenesis of lymphoma. CONCLUSION: Tree models of lymphoma established from CGH data can be used in the classification of DLBCL. These models can suggest multi-gene, multistep and multi-pathway processes of tumorigenesis. Two pathways, -6q preceding +6q and +3q preceding+18q, may be important in understanding tumorigenesis of DLBCL. The pathway, -6q preceding +6q, may have a close relationship with the tumorigenesis of non-GCB DLBCL.
文摘In order to effectively avoid the defects of a traditional discounted cash flow method, a trinomial tree pricing model of the real option is improved and used to forecast the investment price of mining. Taking Molybdenum ore as an example, a theoretical model for the hurdle price under the optimal investment timing is constructed. Based on the example data, the op- tion price model is simulated. By the model, mine investment price can be computed and forecast effectively. According to the characteristics of mine investment, cut-off grade, reserve estimation and mine life in different price also can be quantified. The result shows that it is reliable and practical to enhance the accuracy for mining investment decision.
文摘In the present article it will be critically questioned the traditional entrepreneurship education approaches based on a narrow conception of competency, and their values. Assuming the perspective that to be an entrepreneur is basically an attitude towards life and the world, there proposed holistic, constructivist and experiential processes and strategies for entrepreneurship education. The ＂entrepreneur XXI＂, must be able to undertake a social function of change, so, an economical and social development more human, ethical and intelligent. Under this assumption, the ＂Tree Model for the Development of Entrepreneurial Competencies＂, that will be discussed globally in the second part of this article, suggests a dynamic and experiential approach ofentrepreneurship education based on the qualification of people＇s behaviour, self-esteem, competencies and experiences; a profile of key behavioural and performance competencies （root）, experimental pedagogical procedures （trunk） and real results within group projects （fruits）. This model has been developed during the last decade （2001-2011）, using a multidisciplinary research-action procedure, within business, education （at different teaching levels） and social project environments.
基金Supported by the Beijing University of Traditional Chinese Medicine Foundation(No.2015-JYB-JSMS099)the National Science Foundation of China(No.81473527)
文摘OBJECTIVE: To explore the features of Traditional Chinese Medicine(TCM) syndromes in male infertility using computer-based analyses.METHODS: Latent class analysis was used to analyze the TCM syndrome data from 813 patients with male infertility and establish a latent tree model.RESULTS: A latent tree model with a Bayesian information criterion score of-11 263 was created.This model revealed that the characteristics of basic TCM syndromes in patients with male infertility were kidney Yang deficiency, kidney Qi deficiency,spleen Yang deficiency, liver Qi stagnation, Qi stagnation and blood stasis, and dump-heat; moreover,most patients with male infertility had complex syndromes(spleen-kidney Yang deficiency and liver Qi stagnation) rather than simple single syndromes.CONCLUSION: The hidden tree model analysis revealed the objective and quantitative complex relationships between the TCM symptoms of male infertility, and obtained the quantification and objective evidence of TCM syndromes in male infertility.
基金sponsored by the Federal Highway Administration(FHWA)in cooperation with the American Association of State Highway and Transportation Officials(AASHTO)
文摘Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.
文摘Introduction: As far as adult and married women were concerned, when they occurred to “unplanned pregnancy”, they felt so surprised and concussive all the time. Besides, the unplanned pregnancy also affects the other members in the family system. Therefore, when married women have to face the choice: “birth” or “abortion”, they’ll consider lots of thoughts and different decision criteria and decision pattern under various influences on physician, mind, mental and society. The purpose of this study was to investigate the criteria considered and the decision patterns involved when adult married women decide whether to terminate or continue an unplanned pregnancy. Methods: The study uses the method—“Ethnographic Decision Tree Modeling” [1] to build model of the decision criteria and decision patterns involved when adult married women make a decision about their unplanned pregnancy. There are three process in the research method: “Pilot Study”—interview two groups, every group distinct 4 married adult women with unplanned pregnancies, which decide whether to terminate or continue an unplanned pregnancy, what is the items of decision characters affect to the choice: “birth” or “abortion”. “Building of the Model”, displays the importance in proper order of those items and build the modeling with these two groups of women. “Testing of the Model”: investigate the criteria considered and the decision patterns involved when adult married women decide whether to terminate or continue an unplanned pregnancy. The study interviewed 34 married adult women with 43 unplanned pregnancies totally. Results: The result of the study finds out 12 items of decision characters, including planning to get pregnant or not, stability of feelings for married partner, the points of view on life, was affected by mother, mother-in-law, an husband’s emphasis on male, the meanings of children, the financial burden, the plan an assignment of career and time, the past pregnant experiences, the status of raising children, the health of parents and fetus, the effect of living environment, and social and cultural vision. Besides, there are four decision patterns of married adult women with unplanned pregnancy are “receiving abortion positively”;“giving birth as long as getting pregnancy naturally”;“ the minds are hesitative and changeable”, and “being forced by important others.” Conclusion: By setting the decision model tree, we found several decision criteria and patterns, and possible modes actions to be taken, could offer to see the adult married women’s decision-making and struggles in mind about unplanned pregnancy.
文摘The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the target variable. To solve this problem, a new optimization method is proposed. Using this method, the probability that the predictors used in splitting rules in the optimized tree model have no functional relationships with the target variable is confined to less than 0.05. It is fairly convincing that the tree model given by the new method represents knowledge contained in the data.
文摘In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen.
文摘Safety Critical Systems (SCS) are those systems that may cause harm to the user(s) and/or the environment if operating outside of their prescribed specifications. Such systems are used in a wide variety of domains, such as aerospace, automotive, railway transportation and healthcare. In this paper, we propose an approach to integrate safety analysis of SCSs within the Model Driven Engineering (MDE) system development process. The approach is based on model transformation and uses standard well-known techniques and open source tools for the modeling and analysis of SCSs. More specifically, the system modeled with the OMG’s standard systems modeling language, SysML, is automatically transformed in Fault Tree (FT) models, that can be analyzed with existing FT tools. The proposed model transformation takes place in two steps: a) generate FTs at the component level, in order to tackle complexity and enable reuse;and b) generate system level FTs by composing the components and their FTs. The approach is illustrated by applying it to a simplified industry-inspired case study.
基金supported by the Pilot program“Adaptation to climate change”of the Swiss Federal Office for the Environment(FOEN,project E03)by the Interreg V A Italy Switzerland Cooperation Program 20142020(project MONGEFITOFOR).
文摘Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamomi and P.×cambivora,is a growing concern for sweet chestnut stands(Castanea sativa)in Europe.Since both pathogens are thermophilic organisms,ongoing climate change will likely exacerbate their impact.In this study,we applied species distribution modeling techniques to identify poten-tial substitutive species for sweet chestnut in the light of future climate scenarios SSP126 and SSP370 in southern Switzerland.Using the presence-only machine learning algorithm MaxEnt and leveraging occurrence data from the global dataset GBIF,we delineated the current and projected(2070-2100)distribution of 28 tree species.Several exotic species emerged as valuable alternatives to sweet chestnut,although careful consideration of all potential ecological consequences is required.We also identified several native tree species as promising substitutes,offering ecological benefits and potential adaptability to climatic conditions.Since species diversification fosters forest resilience,we also determined communities of alternative species that can be grown together.Our findings represent a valuable deci-sion tool for forest managers confronted with the challenges posed by ink disease and climate change.Given that,even in absence of disease,sweet chestnut is not a future-proof tree species in the study region,the identified species could offer a pathway toward resilient and sustainable forests within the entire chestnut belt.
基金supported by China Agriculture Research System of MOF and MARA(Grant No.CARS-28-21)Jiangsu Standard Orchard Intelligent Green Agricultural Machinery Research,Production and Application Integration Project(Grant No.JSYTH01).
文摘Due to its ability to broaden the transport channel of droplets within the plant canopy and enhance their penetration capacity,air-assisted spray technology is widely used in orchard pesticide application.To achieve uniform distribution of pesticide droplets in the tree canopy and obtain a higher pesticide utilization rate,it is crucial to clarify the coupling mechanism of the airflow field and droplet field generated by the air-assisted sprayer.This paper introduces a three-dimensional modeling method of the fruit tree canopy based on CFD(Computational Fluid Dynamics),offering a theoretical basis for analyzing the airflow demand calculation during different growth periods of the canopy.It also examines the interaction between canopy modeling and airflow,highlighting advancements in airflow regulation equipment and the effects of airflow speed and volume on spraying.The study shows that the precise regulation of airflow velocity and discharge rate is of importance for improving spraying efficiency.It finally points out that future research should focus on developing intelligent regulation equipment for efficient airflow-droplet control,using biomass sensing,which involves measuring the growth characteristics of the tree canopy,to meet the needs of orchards with diverse growth stages and canopy structures.This article could provide guidance for the future study of precision air-assisted spraying technology in orchards.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPIP:211-611-1443).
文摘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.
基金This study was funded by the National Natural Science Foundation of China(Grant No.41975027)the Natural Science Foundation of Jiangsu Province(Grant No.BK20171457)the National Key R&D Program on Monitoring,Early Warning and Prevention of Major Natural Disasters(Grant No.2017YFC1501401).
文摘With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
