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
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
Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour ...Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.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
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.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
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
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
The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid repro...The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.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
Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and compl...Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.展开更多
Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection...Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.展开更多
Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then ...Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.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
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
Underground cable faults, whether transient or permanent, are traceable to in-sulation failure problems, most of which are water tree initiated. Insulation breakdown, which usually leads to costly power outages, may b...Underground cable faults, whether transient or permanent, are traceable to in-sulation failure problems, most of which are water tree initiated. Insulation breakdown, which usually leads to costly power outages, may be prevented by taking pre-emptive actions. The most decisive pre-emptive action is one in which real-time tracking of water tree advancement within the cable insulation system is possible. Such pre-emptive actions, however, depend on accurate modeling of the phenomenon. Earlier water tree models are static in that they focused on the cable insulation property change at a time segment. Thus, they lack the properties needed for tracking water tree progress and for determining the onset of transient and permanent faults. This paper presents a new ap-proach to water tree modeling, focused on insulation degradation geometry in the form of parabolic expansion of water tree. We developed a dynamic model centered on the computation of the capacitance of a vented water tree as a function of time. The dynamic model accounts for the time-dependence of the radial growth of the water tree to track insulation degradation. The model was tested in predicting cross-linked polyethylene (XLPE) cable’s insulation lifespan. The result was found to be within the range of the recorded lifespan of field aged cables in the literature. Also, performance comparison with an earlier analytical model validated with COMSOL Hyperphysics software shows a signif-icant correlation between them.展开更多
A model of using binomial tree pricing formulae in a fuzzy market is proposed. In the fuzzy market, a price interval can be got according to the belief degree. The rule for the reasonability of the price interval is p...A model of using binomial tree pricing formulae in a fuzzy market is proposed. In the fuzzy market, a price interval can be got according to the belief degree. The rule for the reasonability of the price interval is proposed. The explicit expression of the interval is discussed in some special settings.展开更多
The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitabl...The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal. There have been many studies using the tree shrew （Tupaia belangeri） aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses. The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database （www.treeshrewdb.org） has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species. The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates. Hitherto, the tree shrew has been successfully used to create animal models for myopia, depression, breast cancer, alcohol-induced or non-alcoholic fatty liver diseases, herpes simplex virus type 1 （HSV-1） and hepatitis C virus （HCV） infections, to name a few. The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research. In this opinion paper, I attempt to summarize the recent research advances that have used the Chinese tree shrew, with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome, a proposal for the genome-based approach for creating animal models, and the genetic manipulation of the tree shrew. With more studies using this species and the application of cutting-edge gene editing techniques, the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.展开更多
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
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.
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.
文摘Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.
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 the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
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.
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.
基金supported by the National Nature Science Foundation of China(81325016,U1602221,81322038 and U1502222)
文摘The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.
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Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.
文摘Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.
文摘Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.
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.
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Underground cable faults, whether transient or permanent, are traceable to in-sulation failure problems, most of which are water tree initiated. Insulation breakdown, which usually leads to costly power outages, may be prevented by taking pre-emptive actions. The most decisive pre-emptive action is one in which real-time tracking of water tree advancement within the cable insulation system is possible. Such pre-emptive actions, however, depend on accurate modeling of the phenomenon. Earlier water tree models are static in that they focused on the cable insulation property change at a time segment. Thus, they lack the properties needed for tracking water tree progress and for determining the onset of transient and permanent faults. This paper presents a new ap-proach to water tree modeling, focused on insulation degradation geometry in the form of parabolic expansion of water tree. We developed a dynamic model centered on the computation of the capacitance of a vented water tree as a function of time. The dynamic model accounts for the time-dependence of the radial growth of the water tree to track insulation degradation. The model was tested in predicting cross-linked polyethylene (XLPE) cable’s insulation lifespan. The result was found to be within the range of the recorded lifespan of field aged cables in the literature. Also, performance comparison with an earlier analytical model validated with COMSOL Hyperphysics software shows a signif-icant correlation between them.
文摘A model of using binomial tree pricing formulae in a fuzzy market is proposed. In the fuzzy market, a price interval can be got according to the belief degree. The rule for the reasonability of the price interval is proposed. The explicit expression of the interval is discussed in some special settings.
基金supported by the grant of the National Natural Science Foundation of China(NSFC U1402224)the Chinese Academy of Sciences(CAS zsys-02)
文摘The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal. There have been many studies using the tree shrew （Tupaia belangeri） aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses. The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database （www.treeshrewdb.org） has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species. The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates. Hitherto, the tree shrew has been successfully used to create animal models for myopia, depression, breast cancer, alcohol-induced or non-alcoholic fatty liver diseases, herpes simplex virus type 1 （HSV-1） and hepatitis C virus （HCV） infections, to name a few. The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research. In this opinion paper, I attempt to summarize the recent research advances that have used the Chinese tree shrew, with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome, a proposal for the genome-based approach for creating animal models, and the genetic manipulation of the tree shrew. With more studies using this species and the application of cutting-edge gene editing techniques, the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.
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.
