Science.gov

Sample records for decision tree induction

  1. Inductive Decision Tree Analysis of the Validity Rank of Construction Parameters of Innovative Gear Pump after Tooth Root Undercutting

    NASA Astrophysics Data System (ADS)

    Deptuła, A.; Partyka, M. A.

    2017-02-01

    The article presents an innovative use of inductive algorithm for generating the decision tree for an analysis of the rank validity parameters of construction and maintenance of the gear pump with undercut tooth. It is preventet an alternative way of generating sets of decisions and determining the hierarchy of decision variables to existing the methods of discrete optimization.

  2. Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

    NASA Astrophysics Data System (ADS)

    Park, J.; Yoo, K.

    2013-12-01

    For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

  3. Computational prediction of blood-brain barrier permeability using decision tree induction.

    PubMed

    Suenderhauf, Claudia; Hammann, Felix; Huwyler, Jörg

    2012-08-31

    Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.

  4. Lazy decision trees

    SciTech Connect

    Friedman, J.H.; Yun, Yeogirl; Kohavi, R.

    1996-12-31

    Lazy learning algorithms, exemplified by nearest-neighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single {open_quotes}best{close_quotes} decision tree during the training phase, and this tree is then used to classify test instances. The tests at the nodes of the constructed tree are good on average, but there may be better tests for classifying a specific instance. We propose a lazy decision tree algorithm-LazyDT-that conceptually constructs the {open_quotes}best{close_quote} decision tree for each test instance. In practice, only a path needs to be constructed, and a caching scheme makes the algorithm fast. The algorithm is robust with respect to missing values without resorting to the complicated methods usually seen in induction of decision trees. Experiments on real and artificial problems are presented.

  5. A comparison of artificial neural net and inductive decision tree learning applied to the diagnosis of coronary artery disease

    SciTech Connect

    Silver, D.L.; Hurwitz, G.A.; Cradduck, T.D.

    1994-05-01

    A variety of artificial intelligence systems are available for applications within nuclear medicine. It is important to understand the strengths and weaknesses of these systems and the class of problems for which each is best. Two supervised machine learning systems, a back propagation neural network and an inductive decision tree, were applied to the classification of coronary artery disease given a set of diagnostic input parameters. A comparison indicates that both paradigms perform well depending upon the requirements of the user. We examined the setup complexity, learning and classification speed, training accuracy, ability to generalize to previously unseen cases, and the explanatory power of the internal representations generated by the learning systems. A database of 503 patient records composed of ten parameters was used for the analysis. The target response was a binary value of disease or no disease. The results indicate that the inductive decision tree learning system is the better choice for this class of problem. It is easier to setup and training takes less time. It has good explanatory power since it produces a printed decision tree of the internal representation of acquired knowledge. On the other hand, the artificial neural net provides better generalization for new test cases, and has greater classification accuracy.

  6. Quantum decision tree classifier

    NASA Astrophysics Data System (ADS)

    Lu, Songfeng; Braunstein, Samuel L.

    2013-11-01

    We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object.

  7. VR-BFDT: A variance reduction based binary fuzzy decision tree induction method for protein function prediction.

    PubMed

    Golzari, Fahimeh; Jalili, Saeed

    2015-07-21

    In protein function prediction (PFP) problem, the goal is to predict function of numerous well-sequenced known proteins whose function is not still known precisely. PFP is one of the special and complex problems in machine learning domain in which a protein (regarded as instance) may have more than one function simultaneously. Furthermore, the functions (regarded as classes) are dependent and also are organized in a hierarchical structure in the form of a tree or directed acyclic graph. One of the common learning methods proposed for solving this problem is decision trees in which, by partitioning data into sharp boundaries sets, small changes in the attribute values of a new instance may cause incorrect change in predicted label of the instance and finally misclassification. In this paper, a Variance Reduction based Binary Fuzzy Decision Tree (VR-BFDT) algorithm is proposed to predict functions of the proteins. This algorithm just fuzzifies the decision boundaries instead of converting the numeric attributes into fuzzy linguistic terms. It has the ability of assigning multiple functions to each protein simultaneously and preserves the hierarchy consistency between functional classes. It uses the label variance reduction as splitting criterion to select the best "attribute-value" at each node of the decision tree. The experimental results show that the overall performance of the proposed algorithm is promising.

  8. The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

    NASA Astrophysics Data System (ADS)

    Zaremotlagh, S.; Hezarkhani, A.

    2017-04-01

    Some evidences of rare earth elements (REE) concentrations are found in iron oxide-apatite (IOA) deposits which are located in Central Iranian microcontinent. There are many unsolved problems about the origin and metallogenesis of IOA deposits in this district. Although it is considered that felsic magmatism and mineralization were simultaneous in the district, interaction of multi-stage hydrothermal-magmatic processes within the Early Cambrian volcano-sedimentary sequence probably caused some epigenetic mineralizations. Secondary geological processes (e.g., multi-stage mineralization, alteration, and weathering) have affected on variations of major elements and possible redistribution of REE in IOA deposits. Hence, the geochemical behaviors and distribution patterns of REE are expected to be complicated in different zones of these deposits. The aim of this paper is recognizing LREE distribution patterns based on whole-rock chemical compositions and automatic discovery of their geochemical rules. For this purpose, the pattern recognition techniques including decision tree and neural network were applied on a high-dimensional geochemical dataset from Choghart IOA deposit. Because some data features were irrelevant or redundant in recognizing the distribution patterns of each LREE, a greedy attribute subset selection technique was employed to select the best subset of predictors used in classification tasks. The decision trees (CART algorithm) were pruned optimally to more accurately categorize independent test data than unpruned ones. The most effective classification rules were extracted from the pruned tree to describe the meaningful relationships between the predictors and different concentrations of LREE. A feed-forward artificial neural network was also applied to reliably predict the influence of various rock compositions on the spatial distribution patterns of LREE with a better performance than the decision tree induction. The findings of this study could be

  9. Creating Ensembles of Decision Trees Through Sampling

    SciTech Connect

    Kamath,C; Cantu-Paz, E

    2001-07-26

    Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an ensemble of classifiers and having them vote for the most popular class. This paper focuses on ensembles of decision trees that are created with a randomized procedure based on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or boosting) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe the first experiences with a novel randomized tree induction method that uses a sub-sample of instances at a node to determine the split. The empirical results show that ensembles generated using this approach yield results that are competitive in accuracy and superior in computational cost to boosting and bagging.

  10. Automatic design of decision-tree algorithms with evolutionary algorithms.

    PubMed

    Barros, Rodrigo C; Basgalupp, Márcio P; de Carvalho, André C P L F; Freitas, Alex A

    2013-01-01

    This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

  11. Reweighting with Boosted Decision Trees

    NASA Astrophysics Data System (ADS)

    Rogozhnikov, Alex

    2016-10-01

    Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software triggers [1]. In most cases, these are classification models used to select the “signal” events from data. Monte Carlo simulated events typically take part in training of these models. While the results of the simulation are expected to be close to real data, in practical cases there is notable disagreement between simulated and observed data. In order to use available simulation in training, corrections must be introduced to generated data. One common approach is reweighting — assigning weights to the simulated events. We present a novel method of event reweighting based on boosted decision trees. The problem of checking the quality of reweighting step in analyses is also discussed.

  12. Bayesian Evidence Framework for Decision Tree Learning

    NASA Astrophysics Data System (ADS)

    Chatpatanasiri, Ratthachat; Kijsirikul, Boonserm

    2005-11-01

    This work is primary interested in the problem of, given the observed data, selecting a single decision (or classification) tree. Although a single decision tree has a high risk to be overfitted, the induced tree is easily interpreted. Researchers have invented various methods such as tree pruning or tree averaging for preventing the induced tree from overfitting (and from underfitting) the data. In this paper, instead of using those conventional approaches, we apply the Bayesian evidence framework of Gull, Skilling and Mackay to a process of selecting a decision tree. We derive a formal function to measure `the fitness' for each decision tree given a set of observed data. Our method, in fact, is analogous to a well-known Bayesian model selection method for interpolating noisy continuous-value data. As in regression problems, given reasonable assumptions, this derived score function automatically quantifies the principle of Ockham's razor, and hence reasonably deals with the issue of underfitting-overfitting tradeoff.

  13. Creating ensembles of decision trees through sampling

    DOEpatents

    Kamath, Chandrika; Cantu-Paz, Erick

    2005-08-30

    A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

  14. Weighted Hybrid Decision Tree Model for Random Forest Classifier

    NASA Astrophysics Data System (ADS)

    Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.

    2016-06-01

    Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

  15. The clinical decision analysis using decision tree.

    PubMed

    Bae, Jong-Myon

    2014-01-01

    The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.

  16. The clinical decision analysis using decision tree

    PubMed Central

    Bae, Jong-Myon

    2014-01-01

    The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients’ value. PMID:25358466

  17. The decision tree approach to classification

    NASA Technical Reports Server (NTRS)

    Wu, C.; Landgrebe, D. A.; Swain, P. H.

    1975-01-01

    A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

  18. Comprehensive Decision Tree Models in Bioinformatics

    PubMed Central

    Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

    2012-01-01

    Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class

  19. A survey of decision tree classifier methodology

    NASA Technical Reports Server (NTRS)

    Safavian, S. Rasoul; Landgrebe, David

    1990-01-01

    Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  20. A survey of decision tree classifier methodology

    NASA Technical Reports Server (NTRS)

    Safavian, S. R.; Landgrebe, David

    1991-01-01

    Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  1. PRIA 3 Fee Determination Decision Tree

    EPA Pesticide Factsheets

    The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

  2. RE-Powering’s Electronic Decision Tree

    EPA Pesticide Factsheets

    Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

  3. Solar and Wind Site Screening Decision Trees

    EPA Pesticide Factsheets

    EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

  4. Parallel object-oriented decision tree system

    SciTech Connect

    Kamath; Chandrika , Cantu-Paz; Erick

    2006-02-28

    A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

  5. Automated critiquing of medical decision trees.

    PubMed

    Wellman, M P; Eckman, M H; Fleming, C; Marshall, S L; Sonnenberg, F A; Pauker, S G

    1989-01-01

    The authors developed a decision tree-critiquing program (called BUNYAN) that identifies potential modeling errors in medical decision trees. The program's critiques are based on the structure of a decision problem, obtained from an abstract description specifying only the basic semantic categories of the model's components. A taxonomy of node and branch types supplies the primitive building blocks for representing decision trees. Bunyan detects potential problems in a model by matching general pattern expressions that refer to these primitives. A small set of general principles justifies critiquing rules that detect four categories of potential structural problems: impossible strategies, dominated strategies, unaccountable violations of symmetry, and omission of apparently reasonable strategies. Although critiquing based on structure alone has clear limitations, principled structural analysis constitutes the core of a methodology for reasoning about decision models.

  6. Decision Tree Approach for Soil Liquefaction Assessment

    PubMed Central

    Gandomi, Amir H.; Fridline, Mark M.; Roke, David A.

    2013-01-01

    In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view. PMID:24489498

  7. Decision tree approach for soil liquefaction assessment.

    PubMed

    Gandomi, Amir H; Fridline, Mark M; Roke, David A

    2013-01-01

    In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view.

  8. Fast Image Texture Classification Using Decision Trees

    NASA Technical Reports Server (NTRS)

    Thompson, David R.

    2011-01-01

    Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

  9. CUDT: a CUDA based decision tree algorithm.

    PubMed

    Lo, Win-Tsung; Chang, Yue-Shan; Sheu, Ruey-Kai; Chiu, Chun-Chieh; Yuan, Shyan-Ming

    2014-01-01

    Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5 ∼ 55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

  10. Algorithms for optimal dyadic decision trees

    SciTech Connect

    Hush, Don; Porter, Reid

    2009-01-01

    A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

  11. IND - THE IND DECISION TREE PACKAGE

    NASA Technical Reports Server (NTRS)

    Buntine, W.

    1994-01-01

    A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The

  12. Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

    NASA Technical Reports Server (NTRS)

    Lee, Charles; Alena, Richard L.; Robinson, Peter

    2004-01-01

    We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

  13. Lower Bounds for Algebraic Decision Trees.

    DTIC Science & Technology

    1980-07-01

    Computational Model the General Method. Let W C R " be any set. A (d-th order) decision tree T for testing if Z E W is a ternary tree with each internal node...satisfies the inequality 2 hp(hTdn)> N. Proof. For each leaf I of T let V be the set of inputs N E R " leading to I and let It be the set of...Seidenberg 110). To use Bezout’s Theorem we suppose that p is a real polynomial in n variables with degree m, and we note that R can be chosen so that A = {p

  14. CUDT: A CUDA Based Decision Tree Algorithm

    PubMed Central

    Sheu, Ruey-Kai; Chiu, Chun-Chieh

    2014-01-01

    Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set. PMID:25140346

  15. Identification of metabolic syndrome using decision tree analysis.

    PubMed

    Worachartcheewan, Apilak; Nantasenamat, Chanin; Isarankura-Na-Ayudhya, Chartchalerm; Pidetcha, Phannee; Prachayasittikul, Virapong

    2010-10-01

    This study employs decision tree as a decision support system for rapid and automated identification of individuals with metabolic syndrome (MS) among a Thai population. Results demonstrated strong predictivity of the decision tree in classification of individuals with and without MS, displaying an overall accuracy in excess of 99%.

  16. Extracting decision rules from police accident reports through decision trees.

    PubMed

    de Oña, Juan; López, Griselda; Abellán, Joaquín

    2013-01-01

    Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules.

  17. Ventriculogram segmentation using boosted decision trees

    NASA Astrophysics Data System (ADS)

    McDonald, John A.; Sheehan, Florence H.

    2004-05-01

    Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

  18. 15 CFR Supplement No 1 to Part 732 - Decision Tree

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 15 Commerce and Foreign Trade 2 2014-01-01 2014-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000...

  19. 15 CFR Supplement 1 to Part 732 - Decision Tree

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000...

  20. 15 CFR Supplement No 1 to Part 732 - Decision Tree

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 15 Commerce and Foreign Trade 2 2013-01-01 2013-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000...

  1. 15 CFR Supplement 1 to Part 732 - Decision Tree

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 15 Commerce and Foreign Trade 2 2012-01-01 2012-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000...

  2. 15 CFR Supplement 1 to Part 732 - Decision Tree

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 15 Commerce and Foreign Trade 2 2011-01-01 2011-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000...

  3. Decision-Tree Formulation With Order-1 Lateral Execution

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

    A compact symbolic formulation enables mapping of an arbitrarily complex decision tree of a certain type into a highly computationally efficient multidimensional software object. The type of decision trees to which this formulation applies is that known in the art as the Boolean class of balanced decision trees. Parallel lateral slices of an object created by means of this formulation can be executed in constant time considerably less time than would otherwise be required. Decision trees of various forms are incorporated into almost all large software systems. A decision tree is a way of hierarchically solving a problem, proceeding through a set of true/false responses to a conclusion. By definition, a decision tree has a tree-like structure, wherein each internal node denotes a test on an attribute, each branch from an internal node represents an outcome of a test, and leaf nodes represent classes or class distributions that, in turn represent possible conclusions. The drawback of decision trees is that execution of them can be computationally expensive (and, hence, time-consuming) because each non-leaf node must be examined to determine whether to progress deeper into a tree structure or to examine an alternative. The present formulation was conceived as an efficient means of representing a decision tree and executing it in as little time as possible. The formulation involves the use of a set of symbolic algorithms to transform a decision tree into a multi-dimensional object, the rank of which equals the number of lateral non-leaf nodes. The tree can then be executed in constant time by means of an order-one table lookup. The sequence of operations performed by the algorithms is summarized as follows: 1. Determination of whether the tree under consideration can be encoded by means of this formulation. 2. Extraction of decision variables. 3. Symbolic optimization of the decision tree to minimize its form. 4. Expansion and transformation of all nested conjunctive

  4. Computational study of developing high-quality decision trees

    NASA Astrophysics Data System (ADS)

    Fu, Zhiwei

    2002-03-01

    Recently, decision tree algorithms have been widely used in dealing with data mining problems to find out valuable rules and patterns. However, scalability, accuracy and efficiency are significant concerns regarding how to effectively deal with large and complex data sets in the implementation. In this paper, we propose an innovative machine learning approach (we call our approach GAIT), combining genetic algorithm, statistical sampling, and decision tree, to develop intelligent decision trees that can alleviate some of these problems. We design our computational experiments and run GAIT on three different data sets (namely Socio- Olympic data, Westinghouse data, and FAA data) to test its performance against standard decision tree algorithm, neural network classifier, and statistical discriminant technique, respectively. The computational results show that our approach outperforms standard decision tree algorithm profoundly at lower sampling levels, and achieves significantly better results with less effort than both neural network and discriminant classifiers.

  5. An Algorithm for Anticipating Future Decision Trees from Concept-Drifting Data

    NASA Astrophysics Data System (ADS)

    Böttcher, Mirko; Spott, Martin; Kruse, Rudolf

    Concept-Drift is an important topic in practical data mining, since it is reality in most business applications. Whenever a mining model is used in an application it is already outdated since the world has changed since the model induction. The solution is to predict the drift of a model and derive a future model based on such a prediction. One way would be to simulate future data and derive a model from it, but this is typically not feasible. Instead we suggest to predict the values of the measures that drive model induction. In particular, we propose to predict the future values of attribute selection measures and class label distribution for the induction of decision trees. We give an example of how concept drift is reflected in the trend of these measures and that the resulting decision trees perform considerably better than the ones produced by existing approaches.

  6. Decision tree methods: applications for classification and prediction

    PubMed Central

    SONG, Yan-yan; LU, Ying

    2015-01-01

    Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure. PMID:26120265

  7. Decision Trees for Prediction and Data Mining

    DTIC Science & Technology

    2005-02-10

    ironic, as research in tree-structured methods was originally motivated by the desire for an interpretable alternative to standard methods such as...multiple linear regression and neural networks. Another problem with most tree construction algorithms is that their variable selection methods are biased...software, including well-known ones such as CART (Breiman, Friedman, Olshen and Stone 1984) and M5 (Quinlan 1992). With the excep- tion of the lesser

  8. Generating the Simple Decision Tree with Symbiotic Evolution

    NASA Astrophysics Data System (ADS)

    Otani, Noriko; Shimura, Masamichi

    In representing classification rules by decision trees, simplicity of tree structure is as important as predictive accuracy especially in consideration of the comprehensibility to a human, the memory capacity and the time required to classify. Trees tend to be complex when they get high accuracy. This paper proposes a novel method for generating accurate and simple decision trees based on symbiotic evolution. It is distinctive of symbiotic evolution that two different populations are evolved in parallel through genetic algorithms. In our method one's individuals are partial trees of height 1, and the other's individuals are whole trees represented by the combinations of the former individuals. Generally, overfitting to training examples prevents getting high predictive accuracy. In order to circumvent this difficulty, individuals are evaluated with not only the accuracy in training examples but also the correct answer biased rate indicating the dispersion of the correct answers in the terminal nodes. Based on our method we developed a system called SESAT for generating decision trees. Our experimental results show that SESAT compares favorably with other systems on several datasets in the UCI repository. SESAT has the ability to generate more simple trees than C5.0 without sacrificing predictive accuracy.

  9. Learning accurate very fast decision trees from uncertain data streams

    NASA Astrophysics Data System (ADS)

    Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

    2015-12-01

    Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

  10. An expert-guided decision tree construction strategy: an application in knowledge discovery with medical databases.

    PubMed Central

    Tsai, Y. S.; King, P. H.; Higgins, M. S.; Pierce, D.; Patel, N. P.

    1997-01-01

    With the steady growth in electronic patient records and clinical medical informatics systems, the data collected for routine clinical use have been accumulating at a dramatic rate. Inter-disciplinary research provides a new generation of computation tools in knowledge discovery and data management is in great demand. In this study, an expert-guided decision tree construction strategy is proposed to offer an user-oriented knowledge discovery environment. The strategy allows experts, based on their expertise and/or preference, to override inductive decision tree construction process. Moreover, by reviewing decision paths, experts could focus on subsets of data that may be clues to new findings, or simply contaminated cases. PMID:9357618

  11. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

    PubMed Central

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

  12. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    PubMed

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  13. Evaluation of Decision Trees for Cloud Detection from AVHRR Data

    NASA Technical Reports Server (NTRS)

    Shiffman, Smadar; Nemani, Ramakrishna

    2005-01-01

    Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

  14. Classification of posture and activities by using decision trees.

    PubMed

    Zhang, Ting; Tang, Wenlong; Sazonov, Edward S

    2012-01-01

    Obesity prevention and treatment as well as healthy life style recommendation requires the estimation of everyday physical activity. Monitoring posture allocations and activities with sensor systems is an effective method to achieve the goal. However, at present, most devices available rely on multiple sensors distributed on the body, which might be too obtrusive for everyday use. In this study, data was collected from a wearable shoe sensor system (SmartShoe) and a decision tree algorithm was applied for classification with high computational accuracy. The dataset was collected from 9 individual subjects performing 6 different activities--sitting, standing, walking, cycling, and stairs ascent/descent. Statistical features were calculated and the classification with decision tree classifier was performed, after which, advanced boosting algorithm was applied. The computational accuracy is as high as 98.85% without boosting, and 98.90% after boosting. Additionally, the simple tree structure provides a direct approach to simplify the feature set.

  15. Three-dimensional object recognition using similar triangles and decision trees

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly

    1993-01-01

    A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

  16. Boosting alternating decision trees modeling of disease trait information.

    PubMed

    Liu, Kuang-Yu; Lin, Jennifer; Zhou, Xiaobo; Wong, Stephen T C

    2005-12-30

    We applied the alternating decision trees (ADTrees) method to the last 3 replicates from the Aipotu, Danacca, Karangar, and NYC populations in the Problem 2 simulated Genetic Analysis Workshop dataset. Using information from the 12 binary phenotypes and sex as input and Kofendrerd Personality Disorder disease status as the outcome of ADTrees-based classifiers, we obtained a new quantitative trait based on average prediction scores, which was then used for genome-wide quantitative trait linkage (QTL) analysis. ADTrees are machine learning methods that combine boosting and decision trees algorithms to generate smaller and easier-to-interpret classification rules. In this application, we compared four modeling strategies from the combinations of two boosting iterations (log or exponential loss functions) coupled with two choices of tree generation types (a full alternating decision tree or a classic boosting decision tree). These four different strategies were applied to the founders in each population to construct four classifiers, which were then applied to each study participant. To compute average prediction score for each subject with a specific trait profile, such a process was repeated with 10 runs of 10-fold cross validation, and standardized prediction scores obtained from the 10 runs were averaged and used in subsequent expectation-maximization Haseman-Elston QTL analyses (implemented in GENEHUNTER) with the approximate 900 SNPs in Hardy-Weinberg equilibrium provided for each population. Our QTL analyses on the basis of four models (a full alternating decision tree and a classic boosting decision tree paired with either log or exponential loss function) detected evidence for linkage (Z >or= 1.96, p < 0.01) on chromosomes 1, 3, 5, and 9. Moreover, using average iteration and abundance scores for the 12 phenotypes and sex as their relevancy measurements, we found all relevant phenotypes for all four populations except phenotype b for the Karangar population

  17. The limitations of decision trees and automatic learning in real world medical decision making.

    PubMed

    Kokol, P; Zorman, M; Stiglic, M M; Malèiae, I

    1998-01-01

    The decision tree approach is one of the most common approaches in automatic learning and decision making. It is popular for its simplicity in constructing, efficient use in decision making and for simple representation, which is easily understood by humans. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. The training sets are usually large enough for learning algorithm to construct a hypothesis consistent with the underlying concept. But in real life it is often impossible to find the desired number of training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, unavailability of all attributes at the same time are the typical representatives. There are different ways to deal with some of these problems, but in a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions. We have measured the values of 24 attributes before and after the 82 operations of children in age between 2 and 10 years. The aim was to find the dependencies between attribute values and a child's predisposition to acidemia--the decrease of blood's pH. Our main interest was in discovering predisposition to two forms of acidosis, the metabolic acidosis and the respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects, which was complete (there were no missing attribute values), but on the other hand not large enough to avoid the effect of overfitting. A common approach to evaluation of a decision tree is the use of a test set. In our case we decided that instead of using a test set, we ask medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments on the generated trees can be found in this paper. The comments show, that

  18. Sinkhole hazard assessment in Minnesota using a decision tree model

    NASA Astrophysics Data System (ADS)

    Gao, Yongli; Alexander, E. Calvin

    2008-05-01

    An understanding of what influences sinkhole formation and the ability to accurately predict sinkhole hazards is critical to environmental management efforts in the karst lands of southeastern Minnesota. Based on the distribution of distances to the nearest sinkhole, sinkhole density, bedrock geology and depth to bedrock in southeastern Minnesota and northwestern Iowa, a decision tree model has been developed to construct maps of sinkhole probability in Minnesota. The decision tree model was converted as cartographic models and implemented in ArcGIS to create a preliminary sinkhole probability map in Goodhue, Wabasha, Olmsted, Fillmore, and Mower Counties. This model quantifies bedrock geology, depth to bedrock, sinkhole density, and neighborhood effects in southeastern Minnesota but excludes potential controlling factors such as structural control, topographic settings, human activities and land-use. The sinkhole probability map needs to be verified and updated as more sinkholes are mapped and more information about sinkhole formation is obtained.

  19. A modified classification tree method for personalized medicine decisions

    PubMed Central

    Tsai, Wan-Min; Zhang, Heping; Buta, Eugenia; O’Malley, Stephanie

    2015-01-01

    The tree-based methodology has been widely applied to identify predictors of health outcomes in medical studies. However, the classical tree-based approaches do not pay particular attention to treatment assignment and thus do not consider prediction in the context of treatment received. In recent years, attention has been shifting from average treatment effects to identifying moderators of treatment response, and tree-based approaches to identify subgroups of subjects with enhanced treatment responses are emerging. In this study, we extend and present modifications to one of these approaches (Zhang et al., 2010 [29]) to efficiently identify subgroups of subjects who respond more favorably to one treatment than another based on their baseline characteristics. We extend the algorithm by incorporating an automatic pruning step and propose a measure for assessment of the predictive performance of the constructed tree. We evaluate the proposed method through a simulation study and illustrate the approach using a data set from a clinical trial of treatments for alcohol dependence. This simple and efficient statistical tool can be used for developing algorithms for clinical decision making and personalized treatment for patients based on their characteristics. PMID:26770292

  20. The xeroderma pigmentosum pathway: decision tree analysis of DNA quality.

    PubMed

    Naegeli, Hanspeter; Sugasawa, Kaoru

    2011-07-15

    The nucleotide excision repair (NER) system is a fundamental cellular stress response that uses only a handful of DNA binding factors, mutated in the cancer-prone syndrome xeroderma pigmentosum (XP), to detect an astounding diversity of bulky base lesions, including those induced by ultraviolet light, electrophilic chemicals, oxygen radicals and further genetic insults. Several of these XP proteins are characterized by a mediocre preference for damaged substrates over the native double helix but, intriguingly, none of them recognizes injured bases with sufficient selectivity to account for the very high precision of bulky lesion excision. Instead, substrate versatility as well as damage specificity and strand selectivity are achieved by a multistage quality control strategy whereby different subunits of the XP pathway, in succession, interrogate the DNA double helix for a distinct abnormality in its structural or dynamic parameters. Through this step-by-step filtering procedure, the XP proteins operate like a systematic decision making tool, generally known as decision tree analysis, to sort out rare damaged bases embedded in a vast excess of native DNA. The present review is focused on the mechanisms by which multiple XP subunits of the NER pathway contribute to the proposed decision tree analysis of DNA quality in eukaryotic cells.

  1. A Novel Approach on Designing Augmented Fuzzy Cognitive Maps Using Fuzzified Decision Trees

    NASA Astrophysics Data System (ADS)

    Papageorgiou, Elpiniki I.

    This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from both symbolic and numeric data. Fuzzy theoretical techniques are used to fuzzify crisp decision trees in order to soften decision boundaries at decision nodes inherent in this type of trees. Comparisons between crisp decision trees and the fuzzified decision trees suggest that the later fuzzy tree is significantly more robust and produces a more balanced decision making. The approach proposed in this paper could incorporate any type of fuzzy decision trees. Through this methodology, new linguistic weights were determined in FCM model, thus producing augmented FCM tool. The framework is consisted of a new fuzzy algorithm to generate linguistic weights that describe the cause-effect relationships among the concepts of the FCM model, from induced fuzzy decision trees.

  2. Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

    PubMed

    Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

    2014-12-01

    Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed.

  3. Using histograms to introduce randomization in the generation of ensembles of decision trees

    DOEpatents

    Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

    2005-02-22

    A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

  4. The value of decision tree analysis in planning anaesthetic care in obstetrics.

    PubMed

    Bamber, J H; Evans, S A

    2016-08-01

    The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed.

  5. Career Path Suggestion using String Matching and Decision Trees

    NASA Astrophysics Data System (ADS)

    Nagpal, Akshay; P. Panda, Supriya

    2015-05-01

    High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.

  6. Probabilistic lung nodule classification with belief decision trees.

    PubMed

    Zinovev, Dmitriy; Feigenbaum, Jonathan; Furst, Jacob; Raicu, Daniela

    2011-01-01

    In reading Computed Tomography (CT) scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. Computer-Aided Diagnostic Characterization (CADc) systems can assist radiologists by offering a "second opinion"--predicting these semantic characteristics for lung nodules. In this work, we propose a way of predicting the distribution of radiologists' opinions using a multiple-label classification algorithm based on belief decision trees using the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four human radiologists for each one of the 914 nodules. Furthermore, we evaluate our multiple-label results using a novel distance-threshold curve technique--and, measuring the area under this curve, obtain 69% performance on the validation subset. We conclude that multiple-label classification algorithms are an appropriate method of representing the diagnoses of multiple radiologists on lung CT scans when ground truth is unavailable.

  7. Using Decision Trees for Comparing Pattern Recognition Feature Sets

    SciTech Connect

    Proctor, D D

    2005-08-18

    Determination of the best set of features has been acknowledged as one of the most difficult tasks in the pattern recognition process. In this report significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. Alternative functional forms for feature sets are also examined. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The method is applied to a problem for which a significant portion of the training set cannot be classified unambiguously.

  8. Classification of Liss IV Imagery Using Decision Tree Methods

    NASA Astrophysics Data System (ADS)

    Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

    2016-06-01

    Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

  9. Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

    ERIC Educational Resources Information Center

    Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

    2009-01-01

    The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

  10. An Improved Decision Tree for Predicting a Major Product in Competing Reactions

    ERIC Educational Resources Information Center

    Graham, Kate J.

    2014-01-01

    When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

  11. A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees

    PubMed Central

    Caragea, Doina; Silvescu, Adrian; Honavar, Vasant

    2009-01-01

    This paper motivates and precisely formulates the problem of learning from distributed data; describes a general strategy for transforming traditional machine learning algorithms into algorithms for learning from distributed data; demonstrates the application of this strategy to devise algorithms for decision tree induction from distributed data; and identifies the conditions under which the algorithms in the distributed setting are superior to their centralized counterparts in terms of time and communication complexity; The resulting algorithms are provably exact in that the decision tree constructed from distributed data is identical to that obtained in the centralized setting. Some natural extensions leading to algorithms for learning from heterogeneous distributed data and learning under privacy constraints are outlined. PMID:20351798

  12. Molecular decision trees realized by ultrafast electronic spectroscopy

    PubMed Central

    Fresch, Barbara; Hiluf, Dawit; Collini, Elisabetta; Levine, R. D.; Remacle, F.

    2013-01-01

    The outcome of a light–matter interaction depends on both the state of matter and the state of light. It is thus a natural setting for implementing bilinear classical logic. A description of the state of a time-varying system requires measuring an (ideally complete) set of time-dependent observables. Typically, this is prohibitive, but in weak-field spectroscopy we can move toward this goal because only a finite number of levels are accessible. Recent progress in nonlinear spectroscopies means that nontrivial measurements can be implemented and thereby give rise to interesting logic schemes where the outputs are functions of the observables. Lie algebra offers a natural tool for generating the outcome of the bilinear light–matter interaction. We show how to synthesize these ideas by explicitly discussing three-photon spectroscopy of a bichromophoric molecule for which there are four accessible states. Switching logic would use the on–off occupancies of these four states as outcomes. Here, we explore the use of all 16 observables that define the time-evolving state of the bichromophoric system. The bilinear laser–system interaction with the three pulses of the setup of a 2D photon echo spectroscopy experiment can be used to generate a rich parallel logic that corresponds to the implementation of a molecular decision tree. Our simulations allow relaxation by weak coupling to the environment, which adds to the complexity of the logic operations. PMID:24043793

  13. Decision trees in selection of featured determined food quality.

    PubMed

    Dębska, B; Guzowska-Świder, B

    2011-10-31

    The determination of food quality, authenticity and the detection of adulterations are problems of increasing importance in food chemistry. Recently, chemometric classification techniques and pattern recognition analysis methods for wine and other alcoholic beverages have received great attention and have been largely used. Beer is a complex mixture of components: on one hand a volatile fraction, which is responsible for its aroma, and on the other hand, a non-volatile fraction or extract consisting of a great variety of substances with distinct characteristics. The aim of this study was to consider parameters which contribute to beer differentiation according to the quality grade. Chemical (e.g. pH, acidity, dry extract, alcohol content, CO(2) content) and sensory features (e.g. bitter taste, color) were determined in 70 beer samples and used as variables in decision tree techniques. This pattern recognition techniques applied to the dataset were able to extract information useful in obtaining a satisfactory classification of beer samples according to their quality grade. Feature selection procedures indicated which features are the most discriminating for classification.

  14. Using decision trees to measure activities in people with stroke.

    PubMed

    Zhang, Ting; Fulk, George D; Tang, Wenlong; Sazonov, Edward S

    2013-01-01

    Improving community mobility is a common goal for persons with stroke. Measuring daily physical activity is helpful to determine the effectiveness of rehabilitation interventions. In our previous studies, a novel wearable shoe-based sensor system (SmartShoe) was shown to be capable of accurately classify three major postures and activities (sitting, standing, and walking) from individuals with stroke by using Artificial Neural Network (ANN). In this study, we utilized decision tree algorithms to develop individual and group activity classification models for stroke patients. The data was acquired from 12 participants with stroke. For 3-class classification, the average accuracy was 99.1% with individual models and 91.5% with group models. Further, we extended the activities into 8 classes: sitting, standing, walking, cycling, stairs-up, stairs-down, wheel-chair-push, and wheel-chair-propel. The classification accuracy for individual models was 97.9%, and for group model was 80.2%, demonstrating feasibility of multi-class activity recognition by SmartShoe in stroke patients.

  15. Discovering Patterns in Brain Signals Using Decision Trees

    PubMed Central

    2016-01-01

    Even with emerging technologies, such as Brain-Computer Interfaces (BCI) systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain's behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT) to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain's behaviour. PMID:27688746

  16. Learning from examples - Generation and evaluation of decision trees for software resource analysis

    NASA Technical Reports Server (NTRS)

    Selby, Richard W.; Porter, Adam A.

    1988-01-01

    A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

  17. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

    NASA Technical Reports Server (NTRS)

    Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

    2009-01-01

    The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

  18. Induction of Nitrate Assimilatory Enzymes in the Tree Betula pendula.

    PubMed

    Friemann, A; Lange, M; Hachtel, W; Brinkmann, K

    1992-07-01

    The coordinate appearance of the bispecific NAD(P)H-nitrate reductase (NR; EC 1.6.6.2) and nitrite reductase (NiR; EC 1.7.7.1) was investigated in leaves and roots from European white birch seedlings (Betula pendula Roth). Induction by nitrate and light of both enzymes was analyzed by in vitro assays and by measuring NR- and NiR-encoding mRNA pools with homologous cDNAs as probes. When birch seedlings were grown on a medium containing ammonium as the sole nitrogen source, low constitutive expression of NR and NiR was observed in leaves, whereas only NiR was significantly expressed in roots. Upon transfer of the seedlings to a nitrate-containing medium, mRNA pools and activities of NR and NiR dramatically increased in leaves and roots, with a more rapid induction in leaves. Peak accumulations of mRNA pools preceded the maximum activities of NR and NiR, suggesting that the appearance of both activities can be mainly attributed to an increased expression of NR and NiR genes. Expression of NR was strictly light-dependent in leaves and roots and was repressed by ammonium in roots but not in leaves. In contrast with NR, constitutive expression of NiR was not affected by light, and even a slight induction following the addition of nitrate was found in the dark in roots but not in leaves. No effect of ammonium on NiR expression was detectable in both organs. In leaves as well as in roots, NiR was induced more rapidly than NR, which appears to be a safety measure to prevent nitrite accumulation.

  19. Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

    DOEpatents

    Cantu-Paz, Erick; Kamath, Chandrika

    2006-06-13

    A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

  20. Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

    DTIC Science & Technology

    2008-04-01

    REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

  1. Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

    PubMed

    Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

    2015-11-01

    Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination.

  2. An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space.

    PubMed

    Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan

    2014-03-01

    Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset.

  3. Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data.

    PubMed

    Metting, Esther I; In 't Veen, Johannes C C M; Dekhuijzen, P N Richard; van Heijst, Ellen; Kocks, Janwillem W H; Muilwijk-Kroes, Jacqueline B; Chavannes, Niels H; van der Molen, Thys

    2016-01-01

    The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma-COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool.

  4. Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

    PubMed

    Lee, Saro; Park, Inhye

    2013-09-30

    Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events.

  5. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    PubMed

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  6. Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease.

    PubMed

    Guy, Richard T; Santago, Peter; Langefeld, Carl D

    2012-02-01

    Complex genetic disorders are a result of a combination of genetic and nongenetic factors, all potentially interacting. Machine learning methods hold the potential to identify multilocus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of single nucleotide polymorphisms (SNPs) of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges. We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Decision Trees (ADTrees). The algorithm is order nk(2), where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7,822 SNPs in 3,548 individuals). Our results suggest that BADTrees hold promise as a low computational order algorithm for detecting complex combinations of SNP and environmental factors associated with disease.

  7. Pruning a decision tree for selecting computer-related assistive devices for people with disabilities.

    PubMed

    Chi, Chia-Fen; Tseng, Li-Kai; Jang, Yuh

    2012-07-01

    Many disabled individuals lack extensive knowledge about assistive technology, which could help them use computers. In 1997, Denis Anson developed a decision tree of 49 evaluative questions designed to evaluate the functional capabilities of the disabled user and choose an appropriate combination of assistive devices, from a selection of 26, that enable the individual to use a computer. In general, occupational therapists guide the disabled users through this process. They often have to go over repetitive questions in order to find an appropriate device. A disabled user may require an alphanumeric entry device, a pointing device, an output device, a performance enhancement device, or some combination of these. Therefore, the current research eliminates redundant questions and divides Anson's decision tree into multiple independent subtrees to meet the actual demand of computer users with disabilities. The modified decision tree was tested by six disabled users to prove it can determine a complete set of assistive devices with a smaller number of evaluative questions. The means to insert new categories of computer-related assistive devices was included to ensure the decision tree can be expanded and updated. The current decision tree can help the disabled users and assistive technology practitioners to find appropriate computer-related assistive devices that meet with clients' individual needs in an efficient manner.

  8. Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

    PubMed Central

    Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

    2015-01-01

    Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

  9. [Postmastectomy pain syndrome evidence based guidelines and decision trees].

    PubMed

    Labrèze, Laurent; Dixmérias-Iskandar, Florence; Monnin, Dominique; Bussières, Emmanuel; Delahaye, Evelyne; Bernard, Dominique; Lakdja, Fabrice

    2007-03-01

    A multidisciplinary expert group had reviewed all scientific data available of post mastectomy pain syndrome. Seventy six publications were retained and thirty evidence based diagnosis, treatment and follow-up recommendations are listed. Few of theses recommendations are classed level A. Datas analysis make possible to propose a strategy based on systematic association of drugs, kinesitherapy and psychological support. Evaluation and closer follow-up are necessary. Several decisional trees are proposed.

  10. A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

    PubMed Central

    Liu, Dong-sheng; Fan, Shu-jiang

    2014-01-01

    In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

  11. Post-event human decision errors: operator action tree/time reliability correlation

    SciTech Connect

    Hall, R E; Fragola, J; Wreathall, J

    1982-11-01

    This report documents an interim framework for the quantification of the probability of errors of decision on the part of nuclear power plant operators after the initiation of an accident. The framework can easily be incorporated into an event tree/fault tree analysis. The method presented consists of a structure called the operator action tree and a time reliability correlation which assumes the time available for making a decision to be the dominating factor in situations requiring cognitive human response. This limited approach decreases the magnitude and complexity of the decision modeling task. Specifically, in the past, some human performance models have attempted prediction by trying to emulate sequences of human actions, or by identifying and modeling the information processing approach applicable to the task. The model developed here is directed at describing the statistical performance of a representative group of hypothetical individuals responding to generalized situations.

  12. A modified decision tree algorithm based on genetic algorithm for mobile user classification problem.

    PubMed

    Liu, Dong-sheng; Fan, Shu-jiang

    2014-01-01

    In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity.

  13. ROSE: decision trees, automatic learning and their applications in cardiac medicine.

    PubMed

    Zavrsnik, J; Kokol, P; Malèiae, I; Kancler, K; Mernik, M; Bigec, M

    1995-01-01

    Computerized information systems, especially decision support systems, have acquired an increasingly important role in medical applications, particularly in those where important decisions must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies, and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper, we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolapse (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and, finally, hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained expert armed with proper criteria to evaluate PMV almost 100%. But, unfortunately, there are some problems concerned with the use of echocardiography. With this in mind, we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate the PMV using conventional methods and to select potential patients from the general population. To empower doctors to perform needed activities, we have developed a computer tool called ROSE (computeRized prOlaps Syndrome dEtermination) based on algorithms of automatic learning. This tool supports the definition of new

  14. A Com-Gis Based Decision Tree Model Inagricultural Application

    NASA Astrophysics Data System (ADS)

    Cheng, Wei; Wang, Ke; Zhang, Xiuying

    The problem of agricultural soil pollution by heavy metals has been receiving an increasing attention in the last few decades. Geostatistics module in ArcGIS, could not however efficiently simulate the spatial distribution of heavy metals with satisfied accuracy when the spatial autocorrelation of the study area severely destroyed by human activities. In this study, the classificationand regression tree (CART) has been integrated into ArcGIS using ArcObjects and Visual Basic for Application (VBA) to predict the spatial distribution of soil heavy metals contents in the area severely polluted. This is a great improvement comparing with ordinary Kriging method in ArcGIS. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil heavy metals pollution.

  15. Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

    ERIC Educational Resources Information Center

    Tansy, Michael

    2009-01-01

    The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

  16. What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

    ERIC Educational Resources Information Center

    Thomas, Emily H.; Galambos, Nora

    2004-01-01

    To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

  17. Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model

    PubMed Central

    Lee, So Mi; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

    2015-01-01

    Objective To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). Materials and Methods From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Results Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Conclusion Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology. PMID:26576128

  18. Predicting metabolic syndrome using decision tree and support vector machine methods

    PubMed Central

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-01-01

    BACKGROUND Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. CONCLUSION The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in

  19. Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

    PubMed

    Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

    2015-08-01

    The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances.

  20. Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

    NASA Technical Reports Server (NTRS)

    Otterstatter, Matthew R.

    2005-01-01

    The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

  1. The bone-grafting decision tree: a systematic methodology for achieving new bone.

    PubMed

    Smiler, Dennis; Soltan, Muna

    2006-06-01

    Successful bone grafting requires that the clinician select the optimal bone grafting material and surgical technique from among a number of alternatives. This article reviews the biology of bone growth and repair, and presents a decision-making protocol in which the clinician first evaluates the bone quality at the surgical site to determine which graft material should be used. Bone quantity is then evaluated to determine the optimal surgical technique. Choices among graft stabilization techniques are also reviewed, and cases are presented to illustrate the use of this decision tree.

  2. Circum-Arctic petroleum systems identified using decision-tree chemometrics

    USGS Publications Warehouse

    Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

    2007-01-01

    Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

  3. Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach.

    PubMed

    Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

    2016-01-01

    Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members.

  4. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images.

    PubMed

    Kuo, W J; Chang, R F; Chen, D R; Lee, C C

    2001-03-01

    To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively.

  5. Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach

    PubMed Central

    Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

    2016-01-01

    Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members. PMID:27611313

  6. Office of Legacy Management Decision Tree for Solar Photovoltaic Projects - 13317

    SciTech Connect

    Elmer, John; Butherus, Michael; Barr, Deborah L.

    2013-07-01

    To support consideration of renewable energy power development as a land reuse option, the DOE Office of Legacy Management (LM) and the National Renewable Energy Laboratory (NREL) established a partnership to conduct an assessment of wind and solar renewable energy resources on LM lands. From a solar capacity perspective, the larger sites in the western United States present opportunities for constructing solar photovoltaic (PV) projects. A detailed analysis and preliminary plan was developed for three large sites in New Mexico, assessing the costs, the conceptual layout of a PV system, and the electric utility interconnection process. As a result of the study, a 1,214-hectare (3,000-acre) site near Grants, New Mexico, was chosen for further study. The state incentives, utility connection process, and transmission line capacity were key factors in assessing the feasibility of the project. LM's Durango, Colorado, Disposal Site was also chosen for consideration because the uranium mill tailings disposal cell is on a hillside facing south, transmission lines cross the property, and the community was very supportive of the project. LM worked with the regulators to demonstrate that the disposal cell's long-term performance would not be impacted by the installation of a PV solar system. A number of LM-unique issues were resolved in making the site available for a private party to lease a portion of the site for a solar PV project. A lease was awarded in September 2012. Using a solar decision tree that was developed and launched by the EPA and NREL, LM has modified and expanded the decision tree structure to address the unique aspects and challenges faced by LM on its multiple sites. The LM solar decision tree covers factors such as land ownership, usable acreage, financial viability of the project, stakeholder involvement, and transmission line capacity. As additional sites are transferred to LM in the future, the decision tree will assist in determining whether a solar

  7. An application of contingent valuation and decision tree analysis to water quality improvements.

    PubMed

    Atkins, Jonathan P; Burdon, Daryl; Allen, James H

    2007-01-01

    This paper applies contingent valuation and decision tree analysis to investigate public preferences for water quality improvements, and in particular reduced eutrophication. Such preferences are important given that the development of EU water quality legislation is imposing significant costs on European economies. Results are reported of a survey undertaken of residents of Arhus County, Denmark for water quality improvements in the Randers Fjord. Results demonstrate strong public support for reduced eutrophication and identify key determinants of such support.

  8. Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing

    NASA Astrophysics Data System (ADS)

    Sugumaran, V.; Ramachandran, K. I.

    2007-07-01

    Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has two main phases: feature extraction and feature classification. Statistical features like minimum value, standard error and kurtosis, etc. are widely used as features in fault diagnostics. These features are extracted from vibration signals. A rule set is formed from the extracted features and input to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper presents the use of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions—good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted and good features that discriminate the different fault conditions of the bearing are selected using decision tree. The rule set for fuzzy classifier is obtained once again by using the decision tree. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.

  9. Decision Trees for Continuous Data and Conditional Mutual Information as a Criterion for Splitting Instances.

    PubMed

    Drakakis, Georgios; Moledina, Saadiq; Chomenidis, Charalampos; Doganis, Philip; Sarimveis, Haralambos

    2016-01-01

    Decision trees are renowned in the computational chemistry and machine learning communities for their interpretability. Their capacity and usage are somewhat limited by the fact that they normally work on categorical data. Improvements to known decision tree algorithms are usually carried out by increasing and tweaking parameters, as well as the post-processing of the class assignment. In this work we attempted to tackle both these issues. Firstly, conditional mutual information was used as the criterion for selecting the attribute on which to split instances. The algorithm performance was compared with the results of C4.5 (WEKA's J48) using default parameters and no restrictions. Two datasets were used for this purpose, DrugBank compounds for HRH1 binding prediction and Traditional Chinese Medicine formulation predicted bioactivities for therapeutic class annotation. Secondly, an automated binning method for continuous data was evaluated, namely Scott's normal reference rule, in order to allow any decision tree to easily handle continuous data. This was applied to all approved drugs in DrugBank for predicting the RDKit SLogP property, using the remaining RDKit physicochemical attributes as input.

  10. MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

    NASA Technical Reports Server (NTRS)

    Riggs, George A.; Hall, Dorothy K.

    2010-01-01

    Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

  11. Data mining for multiagent rules, strategies, and fuzzy decision tree structure

    NASA Astrophysics Data System (ADS)

    Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

    2002-03-01

    A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

  12. Building Decision Trees for Characteristic Ellipsoid Method to Monitor Power System Transient Behaviors

    SciTech Connect

    Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.; Etingov, Pavel V.; Zhou, Ning; Dagle, Jeffery E.

    2010-12-01

    The characteristic ellipsoid is a new method to monitor the dynamics of power systems. Decision trees (DTs) play an important role in applying the characteristic ellipsoid method to system operation and analysis. This paper presents the idea and initial results of building DTs for detecting transient dynamic events using the characteristic ellipsoid method. The objective is to determine fault types, fault locations and clearance time in the system using decision trees based on ellipsoids of system transient responses. The New England 10-machine 39-bus system is used for running dynamic simulations to generate a sufficiently large number of transient events in different system configurations. Comprehensive transient simulations considering three fault types, two fault clearance times and different fault locations were conducted in the study. Bus voltage magnitudes and monitored reactive and active power flows are recorded as the phasor measurements to calculate characteristic ellipsoids whose volume, eccentricity, center and projection of the longest axis are used as indices to build decision trees. The DT performances are tested and compared by considering different sets of PMU locations. The proposed method demonstrates that the characteristic ellipsoid method is a very efficient and promising tool to monitor power system dynamic behaviors.

  13. Transporter studies in drug development: experience to date and follow-up on decision trees from the International Transporter Consortium.

    PubMed

    Tweedie, D; Polli, J W; Berglund, E Gil; Huang, S M; Zhang, L; Poirier, A; Chu, X; Feng, B

    2013-07-01

    The International Transporter Consortium (ITC) organized a second workshop in March 2012 to expand on the themes developed during the inaugural ITC workshop held in 2008. The final session of the workshop provided perspectives from regulatory and industry-based scientists, with input from academic scientists, and focused primarily on the decision trees published from the first workshop. These decision trees have become a central part of subsequent regulatory drug-drug interaction (DDI) guidances issued over the past few years.

  14. Using decision trees to manage hospital readmission risk for acute myocardial infarction, heart failure, and pneumonia.

    PubMed

    Hilbert, John P; Zasadil, Scott; Keyser, Donna J; Peele, Pamela B

    2014-12-01

    To improve healthcare quality and reduce costs, the Affordable Care Act places hospitals at financial risk for excessive readmissions associated with acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN). Although predictive analytics is increasingly looked to as a means for measuring, comparing, and managing this risk, many modeling tools require data inputs that are not readily available and/or additional resources to yield actionable information. This article demonstrates how hospitals and clinicians can use their own structured discharge data to create decision trees that produce highly transparent, clinically relevant decision rules for better managing readmission risk associated with AMI, HF, and PN. For illustrative purposes, basic decision trees are trained and tested using publically available data from the California State Inpatient Databases and an open-source statistical package. As expected, these simple models perform less well than other more sophisticated tools, with areas under the receiver operating characteristic (ROC) curve (or AUC) of 0.612, 0.583, and 0.650, respectively, but achieve a lift of at least 1.5 or greater for higher-risk patients with any of the three conditions. More importantly, they are shown to offer substantial advantages in terms of transparency and interpretability, comprehensiveness, and adaptability. By enabling hospitals and clinicians to identify important factors associated with readmissions, target subgroups of patients at both high and low risk, and design and implement interventions that are appropriate to the risk levels observed, decision trees serve as an ideal application for addressing the challenge of reducing hospital readmissions.

  15. Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

    NASA Technical Reports Server (NTRS)

    Shiffman, Smadar

    2004-01-01

    Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

  16. Decision tree and PCA-based fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  17. Supervised learning with decision tree-based methods in computational and systems biology.

    PubMed

    Geurts, Pierre; Irrthum, Alexandre; Wehenkel, Louis

    2009-12-01

    At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

  18. Evolution of Decision Rules Used for IT Portfolio Management: An Inductive Approach

    NASA Astrophysics Data System (ADS)

    Karhade, Prasanna P.; Shaw, Michael J.; Subramanyam, Ramanath

    IT portfolio management and the related planning decisions for IT-dependent initiatives are critical to organizational performance. Building on the logic of appropriateness theoretical framework, we define an important characteristic of decision rules used during IT portfolio planning; rule appropriateness with regards to the risk-taking criterion. We propose that rule appropriateness will be an important factor explaining the evolution of rules over time. Using an inductive learning methodology, we analyze a unique dataset of actual IT portfolio planning decisions spanning two consecutive years within one organization. We present systematic comparative analysis of the evolution of rules used in planning over two years to validate our research proposition. We find that rules that were inappropriate in the first year are being redefined to design appropriate rules for use in the second year. Our work provides empirical evidence demonstrating organizational learning and improvements in IT portfolio planning capabilities.

  19. Decision Optimization of Machine Sets Taking Into Consideration Logical Tree Minimization of Design Guidelines

    NASA Astrophysics Data System (ADS)

    Deptuła, A.; Partyka, M. A.

    2014-08-01

    The method of minimization of complex partial multi-valued logical functions determines the degree of importance of construction and exploitation parameters playing the role of logical decision variables. Logical functions are taken into consideration in the issues of modelling machine sets. In multi-valued logical functions with weighting products, it is possible to use a modified Quine - McCluskey algorithm of multi-valued functions minimization. Taking into account weighting coefficients in the logical tree minimization reflects a physical model of the object being analysed much better

  20. Improvement and analysis of ID3 algorithm in decision-making tree

    NASA Astrophysics Data System (ADS)

    Xie, Xiao-Lan; Long, Zhen; Liao, Wen-Qi

    2015-12-01

    For the cooperative system under development, it needs to use the spatial analysis and relative technology concerning data mining in order to carry out the detection of the subject conflict and redundancy, while the ID3 algorithm is an important data mining. Due to the traditional ID3 algorithm in the decision-making tree towards the log part is rather complicated, this paper obtained a new computational formula of information gain through the optimization of algorithm of the log part. During the experiment contrast and theoretical analysis, it is found that IID3 (Improved ID3 Algorithm) algorithm owns higher calculation efficiency and accuracy and thus worth popularizing.

  1. Decision Tree Classifier for Classification of Plant and Animal Micro RNA's

    NASA Astrophysics Data System (ADS)

    Pant, Bhasker; Pant, Kumud; Pardasani, K. R.

    Gene expression is regulated by miRNAs or micro RNAs which can be 21-23 nucleotide in length. They are non coding RNAs which control gene expression either by translation repression or mRNA degradation. Plants and animals both contain miRNAs which have been classified by wet lab techniques. These techniques are highly expensive, labour intensive and time consuming. Hence faster and economical computational approaches are needed. In view of above a machine learning model has been developed for classification of plant and animal miRNAs using decision tree classifier. The model has been tested on available data and it gives results with 91% accuracy.

  2. Spatial distribution of block falls using volumetric GIS-decision-tree models

    NASA Astrophysics Data System (ADS)

    Abdallah, C.

    2010-10-01

    Block falls are considered a significant aspect of surficial instability contributing to losses in land and socio-economic aspects through their damaging effects to natural and human environments. This paper predicts and maps the geographic distribution and volumes of block falls in central Lebanon using remote sensing, geographic information systems (GIS) and decision-tree modeling (un-pruned and pruned trees). Eleven terrain parameters (lithology, proximity to fault line, karst type, soil type, distance to drainage line, elevation, slope gradient, slope aspect, slope curvature, land cover/use, and proximity to roads) were generated to statistically explain the occurrence of block falls. The latter were discriminated using SPOT4 satellite imageries, and their dimensions were determined during field surveys. The un-pruned tree model based on all considered parameters explained 86% of the variability in field block fall measurements. Once pruned, it classifies 50% in block falls' volumes by selecting just four parameters (lithology, slope gradient, soil type, and land cover/use). Both tree models (un-pruned and pruned) were converted to quantitative 1:50,000 block falls' maps with different classes; starting from Nil (no block falls) to more than 4000 m 3. These maps are fairly matching with coincidence value equal to 45%; however, both can be used to prioritize the choice of specific zones for further measurement and modeling, as well as for land-use management. The proposed tree models are relatively simple, and may also be applied to other areas (i.e. the choice of un-pruned or pruned model is related to the availability of terrain parameters in a given area).

  3. Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

    PubMed

    Poulos, H M; Camp, A E

    2010-02-01

    Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

  4. Integrating Decision Tree and Hidden Markov Model (HMM) for Subtype Prediction of Human Influenza A Virus

    NASA Astrophysics Data System (ADS)

    Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing

    Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.

  5. Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

    NASA Astrophysics Data System (ADS)

    Zuraw, Sarah; LIGO Collaboration

    2015-04-01

    The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

  6. Diagnostic Features of Common Oral Ulcerative Lesions: An Updated Decision Tree

    PubMed Central

    Safi, Yaser

    2016-01-01

    Diagnosis of oral ulcerative lesions might be quite challenging. This narrative review article aims to introduce an updated decision tree for diagnosing oral ulcerative lesions on the basis of their diagnostic features. Various general search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of MeSH keywords such as “oral ulcer,” “stomatitis,” and “mouth diseases.” Thereafter, English-language articles published since 1983 to 2015 in both medical and dental journals including reviews, meta-analyses, original papers, and case reports were appraised. Upon compilation of the relevant data, oral ulcerative lesions were categorized into three major groups: acute, chronic, and recurrent ulcers and into five subgroups: solitary acute, multiple acute, solitary chronic, multiple chronic, and solitary/multiple recurrent, based on the number and duration of lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by stepwise progression. PMID:27781066

  7. Recognition of Protozoa and Metazoa using image analysis tools, discriminant analysis, neural networks and decision trees.

    PubMed

    Ginoris, Y P; Amaral, A L; Nicolau, A; Coelho, M A Z; Ferreira, E C

    2007-07-09

    Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. This work presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.

  8. Using decision-tree classifier systems to extract knowledge from databases

    NASA Technical Reports Server (NTRS)

    St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

    1990-01-01

    One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

  9. Development of decision tree models for substrates, inhibitors, and inducers of p-glycoprotein.

    PubMed

    Hammann, Felix; Gutmann, Heike; Jecklin, Ursula; Maunz, Andreas; Helma, Christoph; Drewe, Juergen

    2009-05-01

    In silico classification of new compounds for certain properties is a useful tool to guide further experiments or compound selection. Interaction of new compounds with the efflux pump P-glycoprotein (P-gp) is an important drug property determining tissue distribution and the potential for drug-drug interactions. We present three datasets on substrate, inhibitor, and inducer activities for P-gp (n = 471) obtained from a literature search which we compared to an existing evaluation of the Prestwick Chemical Library with the calcein-AM assay (retrieved from PubMed). Additionally, we present decision tree models of these activities with predictive accuracies of 77.7 % (substrates), 86.9 % (inhibitors), and 90.3 % (inducers) using three algorithms (CHAID, CART, and C4.5). We also present decision tree models of the calcein-AM assay (79.9 %). Apart from a comprehensive dataset of P-gp interacting compounds, our study provides evidence of the efficacy of logD descriptors and of two algorithms not commonly used in pharmacological QSAR studies (CART and CHAID).

  10. Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

    PubMed Central

    Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

    2015-01-01

    In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

  11. Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees.

    PubMed

    Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

    2015-09-18

    In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods.

  12. Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.

    PubMed

    Newby, Danielle; Freitas, Alex A; Ghafourian, Taravat

    2015-01-27

    Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology.

  13. Decision tree analysis of factors influencing rainfall-related building damage

    NASA Astrophysics Data System (ADS)

    Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

    2014-04-01

    Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a

  14. Decision-tree analysis of factors influencing rainfall-related building structure and content damage

    NASA Astrophysics Data System (ADS)

    Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

    2014-09-01

    Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998-2011. The databases include claims of water-related damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the

  15. Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia

    NASA Astrophysics Data System (ADS)

    Gessesse, B.; Bewket, W.; Bräuning, A.

    2015-11-01

    Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.

  16. Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

    NASA Astrophysics Data System (ADS)

    Gessesse, Berhan; Bewket, Woldeamlak; Bräuning, Achim

    2016-04-01

    Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.

  17. Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

    NASA Astrophysics Data System (ADS)

    Höhle, J.

    2014-09-01

    A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

  18. CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

    PubMed Central

    Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

    2015-01-01

    Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options

  19. Trees

    ERIC Educational Resources Information Center

    Al-Khaja, Nawal

    2007-01-01

    This is a thematic lesson plan for young learners about palm trees and the importance of taking care of them. The two part lesson teaches listening, reading and speaking skills. The lesson includes parts of a tree; the modal auxiliary, can; dialogues and a role play activity.

  20. Comparative Analysis of Decision Trees with Logistic Regression in Predicting Fault-Prone Classes

    NASA Astrophysics Data System (ADS)

    Singh, Yogesh; Takkar, Arvinder Kaur; Malhotra, Ruchika

    There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing activities. This may be possible due to proper allocation of resources on fault prone parts of the design and code of the software. Hence, importance and usefulness of such metrics is understandable, but empirical validation of these metrics is always a great challenge. Decision Tree (DT) methods have been successfully applied for solving classification problems in many applications. This paper evaluates the capability of three DT methods and compares its performance with statistical method in predicting fault prone software classes using publicly available NASA data set. The results indicate that the prediction performance of DT is generally better than statistical model. However, similar types of studies are required to be carried out in order to establish the acceptability of the DT models.

  1. Multi-output decision trees for lesion segmentation in multiple sclerosis

    NASA Astrophysics Data System (ADS)

    Jog, Amod; Carass, Aaron; Pham, Dzung L.; Prince, Jerry L.

    2015-03-01

    Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

  2. The Reliability of Classification of Terminal Nodes in GUIDE Decision Tree to Predict the Nonalcoholic Fatty Liver Disease.

    PubMed

    Birjandi, Mehdi; Ayatollahi, Seyyed Mohammad Taghi; Pourahmad, Saeedeh

    2016-01-01

    Tree structured modeling is a data mining technique used to recursively partition a dataset into relatively homogeneous subgroups in order to make more accurate predictions on generated classes. One of the classification tree induction algorithms, GUIDE, is a nonparametric method with suitable accuracy and low bias selection, which is used for predicting binary classes based on many predictors. In this tree, evaluating the accuracy of predicted classes (terminal nodes) is clinically of special importance. For this purpose, we used GUIDE classification tree in two statuses of equal and unequal misclassification cost in order to predict nonalcoholic fatty liver disease (NAFLD), considering 30 predictors. Then, to evaluate the accuracy of predicted classes by using bootstrap method, first the classification reliability in which individuals are assigned to a unique class and next the prediction probability reliability as support for that are considered.

  3. The Reliability of Classification of Terminal Nodes in GUIDE Decision Tree to Predict the Nonalcoholic Fatty Liver Disease

    PubMed Central

    Pourahmad, Saeedeh

    2016-01-01

    Tree structured modeling is a data mining technique used to recursively partition a dataset into relatively homogeneous subgroups in order to make more accurate predictions on generated classes. One of the classification tree induction algorithms, GUIDE, is a nonparametric method with suitable accuracy and low bias selection, which is used for predicting binary classes based on many predictors. In this tree, evaluating the accuracy of predicted classes (terminal nodes) is clinically of special importance. For this purpose, we used GUIDE classification tree in two statuses of equal and unequal misclassification cost in order to predict nonalcoholic fatty liver disease (NAFLD), considering 30 predictors. Then, to evaluate the accuracy of predicted classes by using bootstrap method, first the classification reliability in which individuals are assigned to a unique class and next the prediction probability reliability as support for that are considered. PMID:28053651

  4. Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree

    PubMed Central

    Acharya, Tri Dev; Lee, Dong Ha; Yang, In Tae; Lee, Jae Kang

    2016-01-01

    Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size. PMID:27420067

  5. A novel decision-tree method for structured continuous-label classification.

    PubMed

    Hu, Hsiao-Wei; Chen, Yen-Liang; Tang, Kwei

    2013-12-01

    Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a decision tree (DT) from this kind of data. This paper proposes a novel classification algorithm for learning DT classifiers from data with structured continuous labels. This approach considers the distribution of labels throughout the hierarchical structure during the construction of trees without requiring discretization in the preprocessing stage. We compared the results of the proposed method with those of the C4.5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity.

  6. A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.

    PubMed

    Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy

    2015-06-20

    The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology.

  7. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

    PubMed Central

    Mudali, D.; Teune, L. K.; Renken, R. J.; Leenders, K. L.; Roerdink, J. B. T. M.

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data. PMID:25918550

  8. A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

    ERIC Educational Resources Information Center

    Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

    2010-01-01

    A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

  9. What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

    ERIC Educational Resources Information Center

    Thomas, Emily H.; Galambos, Nora

    To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

  10. Visualization of spatial decision tree for predicting hotspot occurrence in land and forest in Rokan Hilir District Riau

    NASA Astrophysics Data System (ADS)

    Primajaya, Aji; Sukaesih Sitanggang, Imas; Syaufina, Lailan

    2017-01-01

    Visualization is an important issue in datamining to easy understand patterns extracted from dataset. This research applied the Bottom-Up Approach method to develop a visualization module for a spatial decision tree in a geographic information system. Spatial data used in this work consists of nine explanatory layers and one target layers. Explanatory layers include maximum daily temperature, daily precipitation, wind of speed, distance of nearest river, distance of nearest road, land cover, peatland type, peatland depth, income source. The target layer contains hotspot and non-hotspot points that occurred in 2008. The result is the visualization module of spatial decision tree that has three main features including mapping window, interactive window, tree node and tabular visualization for predicting hotspot occurrence.

  11. Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities

    PubMed Central

    Moon, Mikyung

    2017-01-01

    Objectives The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers (PUs) among elderly people admitted to Korean long-term care facilities. Methods The data were extracted from the 2014 National Inpatient Sample (NIS)—data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89*). Using R 3.3.1, a decision tree was generated with the finalized 15,856 cases and 830 variables. Results The decision tree displayed 15 subgroups with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, “injuries to the hip and thigh” was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the top 7. Conclusions These results support previous studies that showed length of stay, comorbidity, and total hospital cost were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning, such as a decision tree, could effectively predict PUs using big data. PMID:28261530

  12. Application Of Decision Tree Approach To Student Selection Model- A Case Study

    NASA Astrophysics Data System (ADS)

    Harwati; Sudiya, Amby

    2016-01-01

    The main purpose of the institution is to provide quality education to the students and to improve the quality of managerial decisions. One of the ways to improve the quality of students is to arrange the selection of new students with a more selective. This research takes the case in the selection of new students at Islamic University of Indonesia, Yogyakarta, Indonesia. One of the university's selection is through filtering administrative selection based on the records of prospective students at the high school without paper testing. Currently, that kind of selection does not yet has a standard model and criteria. Selection is only done by comparing candidate application file, so the subjectivity of assessment is very possible to happen because of the lack standard criteria that can differentiate the quality of students from one another. By applying data mining techniques classification, can be built a model selection for new students which includes criteria to certain standards such as the area of origin, the status of the school, the average value and so on. These criteria are determined by using rules that appear based on the classification of the academic achievement (GPA) of the students in previous years who entered the university through the same way. The decision tree method with C4.5 algorithm is used here. The results show that students are given priority for admission is that meet the following criteria: came from the island of Java, public school, majoring in science, an average value above 75, and have at least one achievement during their study in high school.

  13. Smart on-board diagnostic decision trees for quantitative aviation equipment and safety procedures validation

    NASA Astrophysics Data System (ADS)

    Ali, Ali H.; Markarian, Garik; Tarter, Alex; Kölle, Rainer

    2010-04-01

    The current trend in high-accuracy aircraft navigation systems is towards using data from one or more inertial navigation subsystem and one or more navigational reference subsystems. The enhancement in fault diagnosis and detection is achieved via computing the minimum mean square estimate of the aircraft states using, for instance, Kalman filter method. However, this enhancement might degrade if the cause of a subsystem fault has some effect on other subsystems that are calculating the same measurement. One instance of such case is the tragic incident of Air France Flight 447 in June, 2009 where message transmissions in the last moment before the crash indicated inconsistencies in measured airspeed as reported by Airbus. In this research, we propose the use of mathematical aircraft model to work out the current states of the airplane and in turn, using these states to validate the readings of the navigation equipment throughout smart diagnostic decision tree network. Various simulated equipment failures have been introduced in a controlled environment to proof the concept of operation. The results have showed successful detection of the failing equipment in all cases.

  14. A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization.

    PubMed

    Sánchez-Rodríguez, David; Hernández-Morera, Pablo; Quinteiro, José Ma; Alonso-González, Itziar

    2015-06-23

    Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

  15. Effect of training characteristics on object classification: An application using Boosted Decision Trees

    NASA Astrophysics Data System (ADS)

    Sevilla-Noarbe, I.; Etayo-Sotos, P.

    2015-06-01

    We present an application of a particular machine-learning method (Boosted Decision Trees, BDTs using AdaBoost) to separate stars and galaxies in photometric images using their catalog characteristics. BDTs are a well established machine learning technique used for classification purposes. They have been widely used specially in the field of particle and astroparticle physics, and we use them here in an optical astronomy application. This algorithm is able to improve from simple thresholding cuts on standard separation variables that may be affected by local effects such as blending, badly calculated background levels or which do not include information in other bands. The improvements are shown using the Sloan Digital Sky Survey Data Release 9, with respect to the type photometric classifier. We obtain an improvement in the impurity of the galaxy sample of a factor 2-4 for this particular dataset, adjusting for the same efficiency of the selection. Another main goal of this study is to verify the effects that different input vectors and training sets have on the classification performance, the results being of wider use to other machine learning techniques.

  16. A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

    PubMed Central

    Sánchez-Rodríguez, David; Hernández-Morera, Pablo; Quinteiro, José Ma.; Alonso-González, Itziar

    2015-01-01

    Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have. PMID:26110413

  17. Trees

    NASA Astrophysics Data System (ADS)

    Epstein, Henri

    2016-11-01

    An algebraic formalism, developed with V. Glaser and R. Stora for the study of the generalized retarded functions of quantum field theory, is used to prove a factorization theorem which provides a complete description of the generalized retarded functions associated with any tree graph. Integrating over the variables associated to internal vertices to obtain the perturbative generalized retarded functions for interacting fields arising from such graphs is shown to be possible for a large category of space-times.

  18. The Tree of Life: Universal and Cultural Features of Folkbiological Taxonomies and Inductions.

    ERIC Educational Resources Information Center

    Lopez, Alejandro; And Others

    1997-01-01

    Two parallel studies were performed with 12 American college students and 12 traditional Itzaj-Mayan adults each to investigate universal and cultural features of folkbiological taxonomies and inductions. Results show cultural consensus among both groups in taxonomies of local mammals. These taxonomies had similar relationships to scientific…

  19. A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

    PubMed

    Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

    2014-01-01

    As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

  20. A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

    PubMed Central

    Goo, Yeong-Jia James; Shen, Zone-De

    2014-01-01

    As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

  1. Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China

    PubMed Central

    Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

    2016-01-01

    Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities—for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals—were the main external sources of a large amount of Hg in the farmland soil. PMID:27834884

  2. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

    NASA Astrophysics Data System (ADS)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-01

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  3. Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

    PubMed Central

    Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

    2016-01-01

    Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

  4. Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis

    PubMed Central

    Zhang, Yushun; Yang, Chong; Gou, Shanmiao; Li, Yongfeng; Xiong, Jiongxin; Wu, Heshui; Wang, Chunyou

    2015-01-01

    Objective To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis. Methods Clinical data of 1308 patients with acute pancreatitis (AP) were included in the retrospective study. A total of 603 patients who were admitted to the hospital within 36 hours of the onset of the disease were included at last according to the inclusion criteria. The clinical data were collected within 12 hours after admission. All the patients were classified as having mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP) based on the revised Atlanta classification of acute pancreatitis. All the 603 patients were randomly divided into training group (402 cases) and test group (201 cases). Univariate and multiple regression analyses were used to identify the independent risk factors for the development of SAP in the training group. Then the prediction model was constructed using the decision tree method, and this model was applied to the test group to evaluate its validity. Results The decision tree model was developed using creatinine, lactate dehydrogenase, and oxygenation index to predict SAP. The diagnostic sensitivity and specificity of SAP in the training group were 80.9% and 90.0%, respectively, and the sensitivity and specificity in the test group were 88.6% and 90.4%, respectively. Conclusions The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP. PMID:26580397

  5. The Bump Hunting by the Decision Tree with the Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Hirose, Hideo

    In difficult classification problems of the z-dimensional points into two groups giving 0-1 responses due to the messy data structure, it is more favorable to search for the denser regions for the response 1 points than to find the boundaries to separate the two groups. For such problems which can often be seen in customer databases, we have developed a bump hunting method using probabilistic and statistical methods as shown in the previous study. By specifying a pureness rate in advance, a maximum capture rate will be obtained. In finding the maximum capture rate, we have used the decision tree method combined with the genetic algorithm. Then, a trade-off curve between the pureness rate and the capture rate can be constructed. However, such a trade-off curve could be optimistic if the training data set alone is used. Therefore, we should be careful in assessing the accuracy of the tradeoff curve. Using the accuracy evaluation procedures such as the cross validation or the bootstrapped hold-out method combined with the training and test data sets, we have shown that the actually applicable trade-off curve can be obtained. We have also shown that an attainable upper bound trade-off curve can be estimated by using the extreme-value statistics because the genetic algorithm provides many local maxima of the capture rates with different initial values. We have constructed the three kinds of trade-off curves; the first is the curve obtained by using the training data; the second is the return capture rate curve obtained by using the extreme-value statistics; the last is the curve obtained by using the test data. These three are indispensable like the Trinity to comprehend the whole figure of the trade-off curve between the pureness rate and the capture rate. This paper deals with the behavior of the trade-off curve from a statistical viewpoint.

  6. Chromosomal damage and EROD induction in tree swallows (Tachycineta bicolor) along the Upper Mississippi River, Minnesota, USA

    USGS Publications Warehouse

    Emilie Bigorgne,; Custer, Thomas W.; Dummer, Paul; Erickson, Richard A.; Karouna, Natalie; Schultz, Sandra; Custer, Christine M.; Thogmartin, Wayne E.; Cole W. Matson,

    2015-01-01

    The health of tree swallows, Tachycineta bicolor, on the Upper Mississippi River (UMR) was assessed in 2010 and 2011 using biomarkers at six sites downriver of Minneapolis/St. Paul, MN metropolitan area, a tributary into the UMR, and a nearby lake. Chromosomal damage was evaluated in nestling blood by measuring the coefficient of variation of DNA content (DNA CV) using flow cytometry. Cytochrome P450 1A activity in nestling liver was measured using the ethoxyresorufin-O-dealkylase (EROD) assay, and oxidative stress was estimated in nestling livers via determination of thiobarbituric acid reacting substances (TBARS), reduced glutathione (GSH), oxidized glutathione (GSSG), the ratio GSSG/GSH, total sulfhydryl, and protein bound sulfhydryl (PBSH). A multilevel regression model (DNA CV) and simple regressions (EROD and oxidative stress) were used to evaluate biomarker responses for each location. Chromosomal damage was significantly elevated at two sites on the UMR (Pigs Eye and Pool 2) relative to the Green Mountain Lake reference site, while the induction of EROD activity was only observed at Pigs Eye. No measures of oxidative stress differed among sites. Multivariate analysis confirmed an increased DNA CV at Pigs Eye and Pool 2, and elevated EROD activity at Pigs Eye. These results suggest that the health of tree swallows has been altered at the DNA level at Pigs Eye and Pool 2 sites, and at the physiological level at Pigs Eye site only.

  7. Chromosomal damage and EROD induction in tree swallows (Tachycineta bicolor) along the Upper Mississippi River, Minnesota, USA.

    PubMed

    Bigorgne, Emilie; Custer, Thomas W; Dummer, Paul M; Erickson, Richard A; Karouna-Renier, Natalie; Schultz, Sandra; Custer, Christine M; Thogmartin, Wayne E; Matson, Cole W

    2015-07-01

    The health of tree swallows, Tachycineta bicolor, on the Upper Mississippi River (UMR) was assessed in 2010 and 2011 using biomarkers at six sites downriver of Minneapolis/St. Paul, MN metropolitan area, a tributary into the UMR, and a nearby lake. Chromosomal damage was evaluated in nestling blood by measuring the coefficient of variation of DNA content (DNA CV) using flow cytometry. Cytochrome P450 1A activity in nestling liver was measured using the ethoxyresorufin-O-dealkylase (EROD) assay, and oxidative stress was estimated in nestling livers via determination of thiobarbituric acid reacting substances (TBARS), reduced glutathione (GSH), oxidized glutathione (GSSG), the ratio GSSG/GSH, total sulfhydryl, and protein bound sulfhydryl (PBSH). A multilevel regression model (DNA CV) and simple regressions (EROD and oxidative stress) were used to evaluate biomarker responses for each location. Chromosomal damage was significantly elevated at two sites on the UMR (Pigs Eye and Pool 2) relative to the Green Mountain Lake reference site, while the induction of EROD activity was only observed at Pigs Eye. No measures of oxidative stress differed among sites. Multivariate analysis confirmed an increased DNA CV at Pigs Eye and Pool 2, and elevated EROD activity at Pigs Eye. These results suggest that the health of tree swallows has been altered at the DNA level at Pigs Eye and Pool 2 sites, and at the physiological level at Pigs Eye site only.

  8. Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

    PubMed Central

    Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

    2016-01-01

    INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

  9. The creation of a digital soil map for Cyprus using decision-tree classification techniques

    NASA Astrophysics Data System (ADS)

    Camera, Corrado; Zomeni, Zomenia; Bruggeman, Adriana; Noller, Joy; Zissimos, Andreas

    2014-05-01

    Considering the increasing threats soil are experiencing especially in semi-arid, Mediterranean environments like Cyprus (erosion, contamination, sealing and salinisation), producing a high resolution, reliable soil map is essential for further soil conservation studies. This study aims to create a 1:50.000 soil map covering the area under the direct control of the Republic of Cyprus (5.760 km2). The study consists of two major steps. The first is the creation of a raster database of predictive variables selected according to the scorpan formula (McBratney et al., 2003). It is of particular interest the possibility of using, as soil properties, data coming from three older island-wide soil maps and the recently published geochemical atlas of Cyprus (Cohen et al., 2011). Ten highly characterizing elements were selected and used as predictors in the present study. For the other factors usual variables were used: temperature and aridity index for climate; total loss on ignition, vegetation and forestry types maps for organic matter; the DEM and related relief derivatives (slope, aspect, curvature, landscape units); bedrock, surficial geology and geomorphology (Noller, 2009) for parent material and age; and a sub-watershed map to better bound location related to parent material sources. In the second step, the digital soil map is created using the Random Forests package in R. Random Forests is a decision tree classification technique where many trees, instead of a single one, are developed and compared to increase the stability and the reliability of the prediction. The model is trained and verified on areas where a 1:25.000 published soil maps obtained from field work is available and then it is applied for predictive mapping to the other areas. Preliminary results obtained in a small area in the plain around the city of Lefkosia, where eight different soil classes are present, show very good capacities of the method. The Ramdom Forest approach leads to reproduce soil

  10. Using decision trees to predict benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea.

    PubMed

    Pesch, Roland; Pehlke, Hendrik; Jerosch, Kerstin; Schröder, Winfried; Schlüter, Michael

    2008-01-01

    In this article a concept is described in order to predict and map the occurrence of benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. The approach consists of two work steps: (1) geostatistical analysis of abiotic measurement data and (2) calculation of benthic provinces by means of Classification and Regression Trees (CART) and GIS-techniques. From bottom water measurements on salinity, temperature, silicate and nutrients as well as from punctual data on grain size ranges (0-20, 20-63, 63-2,000 mu) raster maps were calculated by use of geostatistical methods. At first the autocorrelation structure was examined and modelled with help of variogram analysis. The resulting variogram models were then used to calculate raster maps by applying ordinary kriging procedures. After intersecting these raster maps with punctual data on eight benthic communities a decision tree was derived to predict the occurrence of these communities within the study area. Since such a CART tree corresponds to a hierarchically ordered set of decision rules it was applied to the geostatistically estimated raster data to predict benthic habitats within and near the EEZ.

  11. Analysis of the impact of recreational trail usage for prioritising management decisions: a regression tree approach

    NASA Astrophysics Data System (ADS)

    Tomczyk, Aleksandra; Ewertowski, Marek; White, Piran; Kasprzak, Leszek

    2016-04-01

    The dual role of many Protected Natural Areas in providing benefits for both conservation and recreation poses challenges for management. Although recreation-based damage to ecosystems can occur very quickly, restoration can take many years. The protection of conservation interests at the same as providing for recreation requires decisions to be made about how to prioritise and direct management actions. Trails are commonly used to divert visitors from the most important areas of a site, but high visitor pressure can lead to increases in trail width and a concomitant increase in soil erosion. Here we use detailed field data on condition of recreational trails in Gorce National Park, Poland, as the basis for a regression tree analysis to determine the factors influencing trail deterioration, and link specific trail impacts with environmental, use related and managerial factors. We distinguished 12 types of trails, characterised by four levels of degradation: (1) trails with an acceptable level of degradation; (2) threatened trails; (3) damaged trails; and (4) heavily damaged trails. Damaged trails were the most vulnerable of all trails and should be prioritised for appropriate conservation and restoration. We also proposed five types of monitoring of recreational trail conditions: (1) rapid inventory of negative impacts; (2) monitoring visitor numbers and variation in type of use; (3) change-oriented monitoring focusing on sections of trail which were subjected to changes in type or level of use or subjected to extreme weather events; (4) monitoring of dynamics of trail conditions; and (5) full assessment of trail conditions, to be carried out every 10-15 years. The application of the proposed framework can enhance the ability of Park managers to prioritise their trail management activities, enhancing trail conditions and visitor safety, while minimising adverse impacts on the conservation value of the ecosystem. A.M.T. was supported by the Polish Ministry of

  12. Cost-Effectiveness of a new Rotavirus Vaccination Program in Pakistan: a Decision Tree Model

    PubMed Central

    Patel, Hiten D.; Roberts, Eric T.; Constenla, Dagna O.

    2013-01-01

    Background Rotavirus gastroenteritis places a significant health and economic burden on Pakistan. To determine the public health impact of a national rotavirus vaccination program, we performed a cost-effectiveness study from the perspective of the health care system. Methods A decision tree model was developed to assess the cost-effectiveness of a national vaccination program in Pakistan. Disease and cost burden with the program were compared to the current state. Disease parameters, vaccine-related costs, and medical treatment costs were based on published epidemiological and economic data, which were specific to Pakistan when possible. An annual birth cohort of children was followed for 5 years to model the public health impact of vaccination on health-related events and costs. The cost-effectiveness was assessed and quantified in cost (2012 US$) per disability-adjusted life-year (DALY) averted and cost per death averted. Sensitivity analyses were performed to assess the robustness of the incremental cost-effectiveness ratios (ICERs). Results The base case results showed vaccination prevented 1.2 million cases of rotavirus gastroenteritis, 93,000 outpatient visits, 43,000 hospitalizations, and 6,700 deaths by 5 years of age for an annual birth cohort scaled from 6% current coverage to DPT3 levels (85%). The medical cost savings would be US$1.4 million from hospitalizations and US$200,000 from outpatient visit costs. The vaccination program would cost US$35 million at a vaccine price of US$5.00. The ICER was US$149.50 per DALY averted or US$4,972 per death averted. Sensitivity analyses showed changes in case-fatality ratio, vaccine efficacy, and vaccine cost exerted the greatest influence on the ICER. Conclusions Across a range of sensitivity analyses, a national rotavirus vaccination program was predicted to decrease health and economic burden due to rotavirus gastroenteritis in Pakistan by ~40%. Vaccination was highly cost-effective in this context. As

  13. How Induction Programs Affect the Decision of Alternate Route Urban Teachers to Remain Teaching

    ERIC Educational Resources Information Center

    LoCascio, Steven J.; Smeaton, Patricia S.; Waters, Faith H.

    2016-01-01

    This mixed-methods study analyzes the induction programs for alternate route beginning teachers in low socioeconomic, urban schools. The researcher surveyed 53 teachers at the end of their first year and conducted six in-depth follow-up interviews. The study found that half the teachers did not receive an induction program congruent with state…

  14. Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

    PubMed Central

    2013-01-01

    Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. PMID:24188919

  15. Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

    PubMed Central

    2005-01-01

    We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy. PMID:16046822

  16. Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults

    PubMed Central

    Liu, Pei-Yang

    2014-01-01

    Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS. PMID:24817904

  17. Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults.

    PubMed

    Miller, Brian; Fridline, Mark; Liu, Pei-Yang; Marino, Deborah

    2014-01-01

    Metabolic syndrome (MetS) in young adults (age 20-39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS.

  18. Procalcitonin and C-reactive protein-based decision tree model for distinguishing PFAPA flares from acute infections.

    PubMed

    Kraszewska-Głomba, Barbara; Szymańska-Toczek, Zofia; Szenborn, Leszek

    2016-03-10

    As no specific laboratory test has been identified, PFAPA (periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis) remains a diagnosis of exclusion. We searched for a practical use of procalcitonin (PCT) and C-reactive protein (CRP) in distinguishing PFAPA attacks from acute bacterial and viral infections. Levels of PCT and CRP were measured in 38 patients with PFAPA and 81 children diagnosed with an acute bacterial (n=42) or viral (n=39) infection. Statistical analysis with the use of the C4.5 algorithm resulted in the following decision tree: viral infection if CRP≤19.1 mg/L; otherwise for cases with CRP>19.1 mg/L: bacterial infection if PCT>0.65ng/mL, PFAPA if PCT≤0.65 ng/mL. The model was tested using a 10-fold cross validation and in an independent test cohort (n=30), the rule's overall accuracy was 76.4% and 90% respectively. Although limited by a small sample size, the obtained decision tree might present a potential diagnostic tool for distinguishing PFAPA flares from acute infections when interpreted cautiously and with reference to the clinical context.

  19. Lessons Learned from Applications of a Climate Change Decision Tree toWater System Projects in Kenya and Nepal

    NASA Astrophysics Data System (ADS)

    Ray, P. A.; Bonzanigo, L.; Taner, M. U.; Wi, S.; Yang, Y. C. E.; Brown, C.

    2015-12-01

    The Decision Tree Framework developed for the World Bank's Water Partnership Program provides resource-limited project planners and program managers with a cost-effective and effort-efficient, scientifically defensible, repeatable, and clear method for demonstrating the robustness of a project to climate change. At the conclusion of this process, the project planner is empowered to confidently communicate the method by which the vulnerabilities of the project have been assessed, and how the adjustments that were made (if any were necessary) improved the project's feasibility and profitability. The framework adopts a "bottom-up" approach to risk assessment that aims at a thorough understanding of a project's vulnerabilities to climate change in the context of other nonclimate uncertainties (e.g., economic, environmental, demographic, political). It helps identify projects that perform well across a wide range of potential future climate conditions, as opposed to seeking solutions that are optimal in expected conditions but fragile to conditions deviating from the expected. Lessons learned through application of the Decision Tree to case studies in Kenya and Nepal will be presented, and aspects of the framework requiring further refinement will be described.

  20. Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model.

    PubMed

    Delphin, S; Escobedo, F J; Abd-Elrahman, A; Cropper, W

    2013-11-15

    Information on the effect of direct drivers such as hurricanes on ecosystem services is relevant to landowners and policy makers due to predicted effects from climate change. We identified forest damage risk zones due to hurricanes and estimated the potential loss of 2 key ecosystem services: aboveground carbon storage and timber volume. Using land cover, plot-level forest inventory data, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and a decision tree-based framework; we determined potential damage to subtropical forests from hurricanes in the Lower Suwannee River (LS) and Pensacola Bay (PB) watersheds in Florida, US. We used biophysical factors identified in previous studies as being influential in forest damage in our decision tree and hurricane wind risk maps. Results show that 31% and 0.5% of the total aboveground carbon storage in the LS and PB, respectively was located in high forest damage risk (HR) zones. Overall 15% and 0.7% of the total timber net volume in the LS and PB, respectively, was in HR zones. This model can also be used for identifying timber salvage areas, developing ecosystem service provision and management scenarios, and assessing the effect of other drivers on ecosystem services and goods.

  1. Procalcitonin and C-reactive protein-based decision tree model for distinguishing PFAPA flares from acute infections

    PubMed Central

    Kraszewska-Głomba, Barbara; Szymańska-Toczek, Zofia; Szenborn, Leszek

    2016-01-01

    As no specific laboratory test has been identified, PFAPA (periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis) remains a diagnosis of exclusion. We searched for a practical use of procalcitonin (PCT) and C-reactive protein (CRP) in distinguishing PFAPA attacks from acute bacterial and viral infections. Levels of PCT and CRP were measured in 38 patients with PFAPA and 81 children diagnosed with an acute bacterial (n=42) or viral (n=39) infection. Statistical analysis with the use of the C4.5 algorithm resulted in the following decision tree: viral infection if CRP≤19.1 mg/L; otherwise for cases with CRP>19.1 mg/L: bacterial infection if PCT>0.65ng/mL, PFAPA if PCT≤0.65 ng/mL. The model was tested using a 10-fold cross validation and in an independent test cohort (n=30), the rule’s overall accuracy was 76.4% and 90% respectively. Although limited by a small sample size, the obtained decision tree might present a potential diagnostic tool for distinguishing PFAPA flares from acute infections when interpreted cautiously and with reference to the clinical context. PMID:27131024

  2. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

    NASA Astrophysics Data System (ADS)

    Sakthivel, N. R.; Sugumaran, V.; Nair, Binoy. B.

    2010-08-01

    Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.

  3. Refined estimation of solar energy potential on roof areas using decision trees on CityGML-data

    NASA Astrophysics Data System (ADS)

    Baumanns, K.; Löwner, M.-O.

    2009-04-01

    We present a decision tree for a refined solar energy plant potential estimation on roof areas using the exchange format CityGML. Compared to raster datasets CityGML-data holds geometric and semantic information of buildings and roof areas in more detail. In addition to shadowing effects ownership structures and lifetime of roof areas can be incorporated into the valuation. Since the Renewable Energy Sources Act came into force in Germany in 2000, private house owners and municipals raise attention to the production of green electricity. At this the return on invest depends on the statutory price per Watt, the initial costs of the solar energy plant, its lifetime, and the real production of this installation. The latter depends on the radiation that is obtained from and the size of the solar energy plant. In this context the exposition and slope of the roof area is as important as building parts like chimneys or dormers that might shadow parts of the roof. Knowing the controlling factors a decision tree can be created to support a beneficial deployment of a solar energy plant. Also sufficient data has to be available. Airborne raster datasets can only support a coarse estimation of the solar energy potential of roof areas. While they carry no semantically information, even roof installations are hardly to identify. CityGML as an Open Geospatial Consortium standard is an interoperable exchange data format for virtual 3-dimensional Cities. Based on international standards it holds the aforementioned geometric properties as well as semantically information. In Germany many Cities are on the way to provide CityGML dataset, e. g. Berlin. Here we present a decision tree that incorporates geometrically as well as semantically demands for a refined estimation of the solar energy potential on roof areas. Based on CityGML's attribute lists we consider geometries of roofs and roof installations as well as global radiation which can be derived e. g. from the European Solar

  4. The relation of student behavior, peer status, race, and gender to decisions about school discipline using CHAID decision trees and regression modeling.

    PubMed

    Horner, Stacy B; Fireman, Gary D; Wang, Eugene W

    2010-04-01

    Peer nominations and demographic information were collected from a diverse sample of 1493 elementary school participants to examine behavior (overt and relational aggression, impulsivity, and prosociality), context (peer status), and demographic characteristics (race and gender) as predictors of teacher and administrator decisions about discipline. Exploratory results using classification tree analyses indicated students nominated as average or highly overtly aggressive were more likely to be disciplined than others. Among these students, race was the most significant predictor, with African American students more likely to be disciplined than Caucasians, Hispanics, or Others. Among the students nominated as low in overt aggression, a lack of prosocial behavior was the most significant predictor. Confirmatory analysis using hierarchical logistic regression supported the exploratory results. Similarities with other biased referral patterns, proactive classroom management strategies, and culturally sensitive recommendations are discussed.

  5. A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations

    PubMed Central

    Rood, Richard B.

    2016-01-01

    Abstract An object‐based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small‐scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales. PMID:28239437

  6. A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations.

    PubMed

    Soner Yorgun, M; Rood, Richard B

    2016-12-01

    An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

  7. Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

    PubMed Central

    Liang, Shih-Hsiung; Walther, Bruno Andreas

    2017-01-01

    Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables

  8. Decision-tree model for predicting outcomes after out-of-hospital cardiac arrest in the emergency department

    PubMed Central

    2013-01-01

    Introduction Estimation of outcomes in patients after out-of-hospital cardiac arrest (OHCA) soon after arrival at the hospital may help clinicians guide in-hospital strategies, particularly in the emergency department. This study aimed to develop a simple and generally applicable bedside model for predicting outcomes after cardiac arrest. Methods We analyzed data for 390,226 adult patients who had undergone OHCA, from a prospectively recorded nationwide Utstein-style Japanese database for 2005 through 2009. The primary end point was survival with favorable neurologic outcome (cerebral performance category (CPC) scale, categories 1 to 2 [CPC 1 to 2]) at 1 month. The secondary end point was survival at 1 month. We developed a decision-tree prediction model by using data from a 4-year period (2005 through 2008, n = 307,896), with validation by using external data from 2009 (n = 82,330). Results Recursive partitioning analysis of the development cohort for 10 predictors indicated that the best single predictor for survival and CPC 1 to 2 was shockable initial rhythm. The next predictors for patients with shockable initial rhythm were age (<70 years) followed by witnessed arrest and age (>70 years) followed by arrest witnessed by emergency medical services (EMS) personnel. For patients with unshockable initial rhythm, the next best predictor was witnessed arrest. A simple decision-tree prediction mode permitted stratification into four prediction groups: good, moderately good, poor, and absolutely poor. This model identified patient groups with a range from 1.2% to 30.2% for survival and from 0.3% to 23.2% for CPC 1 to 2 probabilities. Similar results were observed when this model was applied to the validation cohort. Conclusions On the basis of a decision-tree prediction model using four prehospital variables (shockable initial rhythm, age, witnessed arrest, and witnessed by EMS personnel), OHCA patients can be readily stratified into the four groups (good, moderately

  9. Application of decision trees to the analysis of soil radon data for earthquake prediction.

    PubMed

    Zmazek, B; Todorovski, L; Dzeroski, S; Vaupotic, J; Kobal, I

    2003-06-01

    Different regression methods have been used to predict radon concentration in soil gas on the basis of environmental data, i.e. barometric pressure, soil temperature, air temperature and rainfall. Analyses of the radon data from three stations in the Krsko basin, Slovenia, have shown that model trees outperform other regression methods. A model has been built which predicts radon concentration with a correlation of 0.8, provided it is influenced only by the environmental parameters. In periods with seismic activity this correlation is much lower. This decrease in predictive accuracy appears 1-7 days before earthquakes with local magnitude 0.8-3.3.

  10. Towards closed-loop deep brain stimulation: decision tree-based essential tremor patient's state classifier and tremor reappearance predictor.

    PubMed

    Shukla, Pitamber; Basu, Ishita; Tuninetti, Daniela

    2014-01-01

    Deep Brain Stimulation (DBS) is a surgical procedure to treat some progressive neurological movement disorders, such as Essential Tremor (ET), in an advanced stage. Current FDA-approved DBS systems operate open-loop, i.e., their parameters are unchanged over time. This work develops a Decision Tree (DT) based algorithm that, by using non-invasively measured surface EMG and accelerometer signals as inputs during DBS-OFF periods, classifies the ET patient's state and then predicts when tremor is about to reappear, at which point DBS is turned ON again for a fixed amount of time. The proposed algorithm achieves an overall accuracy of 93.3% and sensitivity of 97.4%, along with 2.9% false alarm rate. Also, the ratio between predicted tremor delay and the actual detected tremor delay is about 0.93, indicating that tremor prediction is very close to the instant where tremor actually reappeared.

  11. Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

    NASA Astrophysics Data System (ADS)

    Wang, Hongcui; Kawahara, Tatsuya

    CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

  12. Using image processing technology combined with decision tree algorithm in laryngeal video stroboscope automatic identification of common vocal fold diseases.

    PubMed

    Jeffrey Kuo, Chung-Feng; Wang, Po-Chun; Chu, Yueng-Hsiang; Wang, Hsing-Won; Lai, Chun-Yu

    2013-10-01

    This study used the actual laryngeal video stroboscope videos taken by physicians in clinical practice as the samples for experimental analysis. The samples were dynamic vocal fold videos. Image processing technology was used to automatically capture the image of the largest glottal area from the video to obtain the physiological data of the vocal folds. In this study, an automatic vocal fold disease identification system was designed, which can obtain the physiological parameters for normal vocal folds, vocal paralysis and vocal nodules from image processing according to the pathological features. The decision tree algorithm was used as the classifier of the vocal fold diseases. The identification rate was 92.6%, and the identification rate with an image recognition improvement processing procedure after classification can be improved to 98.7%. Hence, the proposed system has value in clinical practices.

  13. Improved γ/hadron separation for the detection of faint γ-ray sources using boosted decision trees

    NASA Astrophysics Data System (ADS)

    Krause, Maria; Pueschel, Elisa; Maier, Gernot

    2017-03-01

    Imaging atmospheric Cherenkov telescopes record an enormous number of cosmic-ray background events. Suppressing these background events while retaining γ-rays is key to achieving good sensitivity to faint γ-ray sources. The differentiation between signal and background events can be accomplished using machine learning algorithms, which are already used in various fields of physics. Multivariate analyses combine several variables into a single variable that indicates the degree to which an event is γ-ray-like or cosmic-ray-like. In this paper we will focus on the use of "boosted decision trees" for γ/hadron separation. We apply the method to data from the Very Energetic Radiation Imaging Telescope Array System (VERITAS), and demonstrate an improved sensitivity compared to the VERITAS standard analysis.

  14. A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

    NASA Astrophysics Data System (ADS)

    Kupriyanov, M. S.; Shukeilo, E. Y.; Shichkina, J. A.

    2015-11-01

    Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient's health condition using data from a wearable device considers in this article.

  15. A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

    SciTech Connect

    Kupriyanov, M. S. Shukeilo, E. Y. Shichkina, J. A.

    2015-11-17

    Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

  16. Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

    PubMed Central

    Li, Yang; Tang, Xu-Qing; Bai, Zhonghu; Dai, Xiaofeng

    2016-01-01

    Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree’s branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics. PMID:27786176

  17. Forest or the trees: At what scale do elephants make foraging decisions?

    NASA Astrophysics Data System (ADS)

    Shrader, Adrian M.; Bell, Caroline; Bertolli, Liandra; Ward, David

    2012-07-01

    For herbivores, food is distributed spatially in a hierarchical manner ranging from plant parts to regions. Ultimately, utilisation of food is dependent on the scale at which herbivores make foraging decisions. A key factor that influences these decisions is body size, because selection inversely relates to body size. As a result, large animals can be less selective than small herbivores. Savanna elephants (Loxodonta africana) are the largest terrestrial herbivore. Thus, they represent a potential extreme with respect to unselective feeding. However, several studies have indicated that elephants prefer specific habitats and certain woody plant species. Thus, it is unclear at which scale elephants focus their foraging decisions. To determine this, we recorded the seasonal selection of habitats and woody plant species by elephants in the Ithala Game Reserve, South Africa. We expected that during the wet season, when both food quality and availability were high, that elephants would select primarily for habitats. This, however, does not mean that they would utilise plant species within these habitats in proportion to availability, but rather would show a stronger selection for habitats compared to plants. In contrast, during the dry season when food quality and availability declined, we expected that elephants would shift and select for the remaining high quality woody species across all habitats. Consistent with our predictions, elephants selected for the larger spatial scale (i.e. habitats) during the wet season. However, elephants did not increase their selection of woody species during the dry season, but rather increased their selection of habitats relative to woody plant selection. Unlike a number of earlier studies, we found that that neither palatability (i.e. crude protein, digestibility, and energy) alone nor tannin concentrations had a significant effect for determining the elephants' selection of woody species. However, the palatability:tannin ratio was

  18. Control of fire blight (Erwinia amylovora) on apple trees with trunk-injected plant resistance inducers and antibiotics and assessment of induction of pathogenesis-related protein genes.

    PubMed

    Aćimović, Srđan G; Zeng, Quan; McGhee, Gayle C; Sundin, George W; Wise, John C

    2015-01-01

    Management of fire blight is complicated by limitations on use of antibiotics in agriculture, antibiotic resistance development, and limited efficacy of alternative control agents. Even though successful in control, preventive antibiotic sprays also affect non-target bacteria, aiding the selection for resistance which could ultimately be transferred to the pathogen Erwinia amylovora. Trunk injection is a target-precise pesticide delivery method that utilizes tree xylem to distribute injected compounds. Trunk injection could decrease antibiotic usage in the open environment and increase the effectiveness of compounds in fire blight control. In field experiments, after 1-2 apple tree injections of either streptomycin, potassium phosphites (PH), or acibenzolar-S-methyl (ASM), significant reduction of blossom and shoot blight symptoms was observed compared to water injected control trees. Overall disease suppression with streptomycin was lower than typically observed following spray applications to flowers. Trunk injection of oxytetracycline resulted in excellent control of shoot blight severity, suggesting that injection is a superior delivery method for this antibiotic. Injection of both ASM and PH resulted in the significant induction of PR-1, PR-2, and PR-8 protein genes in apple leaves indicating induction of systemic acquired resistance (SAR) under field conditions. The time separating SAR induction and fire blight symptom suppression indicated that various defensive compounds within the SAR response were synthesized and accumulated in the canopy. ASM and PH suppressed fire blight even after cessation of induced gene expression. With the development of injectable formulations and optimization of doses and injection schedules, the injection of protective compounds could serve as an effective option for fire blight control.

  19. Control of fire blight (Erwinia amylovora) on apple trees with trunk-injected plant resistance inducers and antibiotics and assessment of induction of pathogenesis-related protein genes

    PubMed Central

    Aćimović, Srđan G.; Zeng, Quan; McGhee, Gayle C.; Sundin, George W.; Wise, John C.

    2015-01-01

    Management of fire blight is complicated by limitations on use of antibiotics in agriculture, antibiotic resistance development, and limited efficacy of alternative control agents. Even though successful in control, preventive antibiotic sprays also affect non-target bacteria, aiding the selection for resistance which could ultimately be transferred to the pathogen Erwinia amylovora. Trunk injection is a target-precise pesticide delivery method that utilizes tree xylem to distribute injected compounds. Trunk injection could decrease antibiotic usage in the open environment and increase the effectiveness of compounds in fire blight control. In field experiments, after 1–2 apple tree injections of either streptomycin, potassium phosphites (PH), or acibenzolar-S-methyl (ASM), significant reduction of blossom and shoot blight symptoms was observed compared to water injected control trees. Overall disease suppression with streptomycin was lower than typically observed following spray applications to flowers. Trunk injection of oxytetracycline resulted in excellent control of shoot blight severity, suggesting that injection is a superior delivery method for this antibiotic. Injection of both ASM and PH resulted in the significant induction of PR-1, PR-2, and PR-8 protein genes in apple leaves indicating induction of systemic acquired resistance (SAR) under field conditions. The time separating SAR induction and fire blight symptom suppression indicated that various defensive compounds within the SAR response were synthesized and accumulated in the canopy. ASM and PH suppressed fire blight even after cessation of induced gene expression. With the development of injectable formulations and optimization of doses and injection schedules, the injection of protective compounds could serve as an effective option for fire blight control. PMID:25717330

  20. Potential link between biotic defense activation and recalcitrance to induction of somatic embryogenesis in shoot primordia from adult trees of white spruce (Picea glauca)

    PubMed Central

    2013-01-01

    Background Among the many commercial opportunities afforded by somatic embryogenesis (SE), it is the ability to clonally propagate individual plants with rare or elite traits that has some of the most significant implications. This is particularly true for many long-lived species, such as conifers, but whose long generation times pose substantive challenges, including increased recalcitrance for SE as plants age. Identification of a clonal line of somatic embryo-derived trees whose shoot primordia have remained responsive to SE induction for over a decade, provided a unique opportunity to examine the molecular aspects underpinning SE within shoot tissues of adult white spruce trees. Results Microarray analysis was used to conduct transcriptome-wide expression profiling of shoot explants taken from this responsive genotype following one week of SE induction, which when compared with that of a nonresponsive genotype, led to the identification of four of the most differentially expressed genes within each genotype. Using absolute qPCR to expand the analysis to three weeks of induction revealed that differential expression of all eight candidate genes was maintained to the end of the induction treatment, albeit to differing degrees. Most striking was that both the magnitude and duration of candidate gene expression within the nonresponsive genotype was indicative of an intense physiological response. Examining their putative identities further revealed that all four encoded for proteins with similarity to angiosperm proteins known to play prominent roles in biotic defense, and that their high-level induction over an extended period is consistent with activation of a biotic defense response. In contrast, the more temperate response within the responsive genotype, including induction of a conifer-specific dehydrin, is more consistent with elicitation of an adaptive stress response. Conclusions While additional evidence is required to definitively establish an association

  1. Vegetative propagation of Quercus suber L. by somatic embryogenesis. I. Factors affecting the induction in leaves from mature cork oak trees.

    PubMed

    Hernández, I; Celestino, C; Toribio, M

    2003-04-01

    Somatic embryogenesis was induced in expanding leaves from epicormic shoots forced to sprout from segments of branches collected from several hundred-year-old cork oak trees. Following a basic protocol previously defined for leaves taken from seedlings of this species, several factors were studied to improve the response. The induction frequency was significantly higher when the length of exposure to growth regulators was increased from 7 to 30 days. The combined application of NAA and BAP was essential for induction. Although both regulators had a very significant influence, their interaction was not significant, suggesting independent roles. Leaf size had a crucial effect, because beyond a certain threshold, embryogenesis could not be obtained. Embryogenic lines were maintained via repetitive embryogenesis on hormone-free medium for more than 2 years.

  2. Tree Ensembles on the Induced Discrete Space.

    PubMed

    Yildiz, Olcay Taner

    2016-05-01

    Decision trees are widely used predictive models in machine learning. Recently, K -tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and these orderings are used as the new attributes in decision tree induction. Although K -tree performs significantly better than the proper one, their exponential time complexity can prohibit their use. In this brief, we propose K -forest, an extension of random forest, where a subset of features is selected randomly from the induced discrete space. Simulation results on 17 data sets show that the novel ensemble classifier has significantly lower error rate compared with the random forest based on the original feature space.

  3. Decision-tree analysis of clinical data to aid diagnostic reasoning for equine laminitis: a cross-sectional study.

    PubMed

    Wylie, C E; Shaw, D J; Verheyen, K L P; Newton, J R

    2016-04-23

    The objective of this cross-sectional study was to compare the prevalence of selected clinical signs in laminitis cases and non-laminitic but lame controls to evaluate their capability to discriminate laminitis from other causes of lameness. Participating veterinary practitioners completed a checklist of laminitis-associated clinical signs identified by literature review. Cases were defined as horses/ponies with veterinary-diagnosed, clinically apparent laminitis; controls were horses/ponies with any lameness other than laminitis. Associations were tested by logistic regression with adjusted odds ratios (ORs) and 95% confidence intervals, with veterinary practice as an a priori fixed effect. Multivariable analysis using graphical classification tree-based statistical models linked laminitis prevalence with specific combinations of clinical signs. Data were collected for 588 cases and 201 controls. Five clinical signs had a difference in prevalence of greater than +50 per cent: 'reluctance to walk' (OR 4.4), 'short, stilted gait at walk' (OR 9.4), 'difficulty turning' (OR 16.9), 'shifting weight' (OR 17.7) and 'increased digital pulse' (OR 13.2) (all P<0.001). 'Bilateral forelimb lameness' was the best discriminator; 92 per cent of animals with this clinical sign had laminitis (OR 40.5, P<0.001). If, in addition, horses/ponies had an 'increased digital pulse', 99 per cent were identified as laminitis. 'Presence of a flat/convex sole' also significantly enhanced clinical diagnosis discrimination (OR 15.5, P<0.001). This is the first epidemiological laminitis study to use decision-tree analysis, providing the first evidence base for evaluating clinical signs to differentially diagnose laminitis from other causes of lameness. Improved evaluation of the clinical signs displayed by laminitic animals examined by first-opinion practitioners will lead to equine welfare improvements.

  4. An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier

    NASA Astrophysics Data System (ADS)

    Arellano-Neri, Olimpia

    Mapping is essential for the analysis of the land and land-cover dynamics, which influence many environmental processes and properties. When creating land-cover maps it is important to minimize error, since error will propagate into later analyses based upon these land cover maps. The reliability of land cover maps derived from remotely sensed data depends upon an accurate classification. For decades, traditional statistical methods have been applied in land-cover classification with varying degrees of accuracy. One of the most significant developments in the field of land-cover classification using remotely sensed data has been the introduction of Artificial Neural Networks (ANN) procedures. In this research, Artificial Neural Networks were applied to remotely sensed data of the southwestern Ohio region for land-cover classification. Three variants on traditional ANN-based classifiers were explored here: (1) the use of a customized architecture of the neural network in terms of the input layer for each land-cover class, (2) the use of texture analysis to combine spectral information and spatial information which is essential for urban classes, and (3) the use of decision tree (DT) classification to refine the ANN classification and ultimately to achieve a more reliable land-cover thematic map. The objective of this research was to prove that a classification based on Artificial Neural Networks (ANN) and decision tree (DT) would outperform by far the National Land Cover Data (NLCD). The NLCD is a land-cover classification produced by a cooperative effort between the United States Geological Survey (USGS) and the United States Environmental Protection Agency (USEPA). In order to achieve this objective, an accuracy assessment was conducted for both NLCD classification and ANN/DT classification. Error matrices resulting from the accuracy assessments provided overall accuracy, accuracy of each class, omission errors, and commission errors for each classification. The

  5. The WHO classification of lymphomas: cost-effective immunohistochemistry using a deductive reasoning "decision tree" approach: part II: the decision tree approach: diffuse patterns of proliferation in lymph nodes.

    PubMed

    Taylor, Clive R

    2009-12-01

    The 2008 World Health Organization Classification of Tumors of the Haematopoietic and Lymphoid Tissues defines current standards of practice for the diagnosis and classification of malignant lymphomas and related entities. More than 50 different types of lymphomas are described. Faced with such a broad range of different lymphomas, some encountered only rarely, and a rapidly growing armamentarium of 80 or more pertinent immunohistochemical (IHC) "stains," the challenge to the pathologist is to use IHC in an efficient manner to arrive at an assured and timely diagnosis. This review uses deductive reasoning following a decision tree or dendrogram model, combining basic morphologic patterns and common IHC markers to classify node-based malignancies by the World Health Organization schema. The review is divided into 2 parts, the first addressing those lymphomas that produce a follicular or nodular pattern of lymph nodal involvement appeared in the previous issue of AIMM. The second part addresses diffuse proliferations in lymph nodes. Emphasis is given to the more common lymphomas and the more commonly available IHC "stains" for a pragmatic and practical approach that is both broadly feasible and cost-effective. By this method, an assured diagnosis may be reached in the majority of nodal lymphomas, at the same time developing a sufficiency of data to recognize those rare or atypical cases that require referral to a specialized center.

  6. Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm

    NASA Astrophysics Data System (ADS)

    Ghulam, Abduwasit; Porton, Ingrid; Freeman, Karen

    2014-02-01

    In this paper, we propose a decision tree algorithm to characterize spatial extent and spectral features of invasive plant species (i.e., guava, Madagascar cardamom, and Molucca raspberry) in tropical rainforests by integrating datasets from passive and active remote sensing sensors. The decision tree algorithm is based on a number of input variables including matching score and infeasibility images from Mixture Tuned Matched Filtering (MTMF), land-cover maps, tree height information derived from high resolution stereo imagery, polarimetric feature images, Radar Forest Degradation Index (RFDI), polarimetric and InSAR coherence and phase difference images. Spatial distributions of the study organisms are mapped using pixel-based Winner-Takes-All (WTA) algorithm, object oriented feature extraction, spectral unmixing, and compared with the newly developed decision tree approach. Our results show that the InSAR phase difference and PolInSAR HH-VV coherence images of L-band PALSAR data are the most important variables following the MTMF outputs in mapping subcanopy invasive plant species in tropical rainforest. We also show that the three types of invasive plants alone occupy about 17.6% of the Betampona Nature Reserve (BNR) while mixed forest, shrubland and grassland areas are summed to 11.9% of the reserve. This work presents the first systematic attempt to evaluate forest degradation, habitat quality and invasive plant statistics in the BNR, and provides significant insights as to management strategies for the control of invasive plants and conversation in the reserve.

  7. A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

    NASA Astrophysics Data System (ADS)

    Khader, A.; Rosenberg, D.; McKee, M.

    2012-12-01

    Nitrate pollution poses a health risk for infants whose freshwater drinking source is groundwater. This risk creates a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision maker and the expected outcomes from these alternatives. The alternatives include: (i) ignore the health risk of nitrate contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, pollution transport processes, and climate (Khader and McKee, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine where methemoglobinemia is the main health problem associated with the principal pollutant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not-use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current

  8. A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

    NASA Astrophysics Data System (ADS)

    Khader, A. I.; Rosenberg, D. E.; McKee, M.

    2013-05-01

    Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs

  9. Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis

    PubMed Central

    Swift, Andrew J.; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M.

    2016-01-01

    Abstract Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  10. Expression profiling of FLOWERING LOCUS T-like gene in alternate bearing 'Hass' avocado trees suggests a role for PaFT in avocado flower induction.

    PubMed

    Ziv, Dafna; Zviran, Tali; Zezak, Oshrat; Samach, Alon; Irihimovitch, Vered

    2014-01-01

    In many perennials, heavy fruit load on a shoot decreases the ability of the plant to undergo floral induction in the following spring, resulting in a pattern of crop production known as alternate bearing. Here, we studied the effects of fruit load on floral determination in 'Hass' avocado (Persea americana). De-fruiting experiments initially confirmed the negative effects of fruit load on return to flowering. Next, we isolated a FLOWERING LOCUS T-like gene, PaFT, hypothesized to act as a phloem-mobile florigen signal and examined its expression profile in shoot tissues of on (fully loaded) and off (fruit-lacking) trees. Expression analyses revealed a strong peak in PaFT transcript levels in leaves of off trees from the end of October through November, followed by a return to starting levels. Moreover and concomitant with inflorescence development, only off buds displayed up-regulation of the floral identity transcripts PaAP1 and PaLFY, with significant variation being detected from October and November, respectively. Furthermore, a parallel microscopic study of off apical buds revealed the presence of secondary inflorescence axis structures that only appeared towards the end of November. Finally, ectopic expression of PaFT in Arabidopsis resulted in early flowering transition. Together, our data suggests a link between increased PaFT expression observed during late autumn and avocado flower induction. Furthermore, our results also imply that, as in the case of other crop trees, fruit-load might affect flowering by repressing the expression of PaFT in the leaves. Possible mechanism(s) by which fruit crop might repress PaFT expression, are discussed.

  11. Expression Profiling of FLOWERING LOCUS T-Like Gene in Alternate Bearing ‘Hass' Avocado Trees Suggests a Role for PaFT in Avocado Flower Induction

    PubMed Central

    Ziv, Dafna; Zviran, Tali; Zezak, Oshrat; Samach, Alon; Irihimovitch, Vered

    2014-01-01

    In many perennials, heavy fruit load on a shoot decreases the ability of the plant to undergo floral induction in the following spring, resulting in a pattern of crop production known as alternate bearing. Here, we studied the effects of fruit load on floral determination in ‘Hass' avocado (Persea americana). De-fruiting experiments initially confirmed the negative effects of fruit load on return to flowering. Next, we isolated a FLOWERING LOCUS T-like gene, PaFT, hypothesized to act as a phloem-mobile florigen signal and examined its expression profile in shoot tissues of on (fully loaded) and off (fruit-lacking) trees. Expression analyses revealed a strong peak in PaFT transcript levels in leaves of off trees from the end of October through November, followed by a return to starting levels. Moreover and concomitant with inflorescence development, only off buds displayed up-regulation of the floral identity transcripts PaAP1 and PaLFY, with significant variation being detected from October and November, respectively. Furthermore, a parallel microscopic study of off apical buds revealed the presence of secondary inflorescence axis structures that only appeared towards the end of November. Finally, ectopic expression of PaFT in Arabidopsis resulted in early flowering transition. Together, our data suggests a link between increased PaFT expression observed during late autumn and avocado flower induction. Furthermore, our results also imply that, as in the case of other crop trees, fruit-load might affect flowering by repressing the expression of PaFT in the leaves. Possible mechanism(s) by which fruit crop might repress PaFT expression, are discussed. PMID:25330324

  12. Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches

    NASA Astrophysics Data System (ADS)

    Khalilinezhad, Mahdieh; Minaei, Behrooz; Vernazza, Gianni; Dellepiane, Silvana

    2015-03-01

    Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

  13. A decision-tree approach to the assessment of posttraumatic stress disorder: Engineering empirically rigorous and ecologically valid assessment measures.

    PubMed

    Stewart, Regan W; Tuerk, Peter W; Metzger, Isha W; Davidson, Tatiana M; Young, John

    2016-02-01

    Structured diagnostic interviews are widely considered to be the optimal method of assessing symptoms of posttraumatic stress; however, few clinicians report using structured assessments to guide clinical practice. One commonly cited impediment to these assessment approaches is the amount of time required for test administration and interpretation. Empirically keyed methods to reduce the administration time of structured assessments may be a viable solution to increase the use of standardized and reliable diagnostic tools. Thus, the present research conducted an initial feasibility study using a sample of treatment-seeking military veterans (N = 1,517) to develop a truncated assessment protocol based on the Clinician-Administered Posttraumatic Stress Disorder (PTSD) Scale (CAPS). Decision-tree analysis was utilized to identify a subset of predictor variables among the CAPS items that were most predictive of a diagnosis of PTSD. The algorithm-driven, atheoretical sequence of questions reduced the number of items administered by more than 75% and classified the validation sample at 92% accuracy. These results demonstrated the feasibility of developing a protocol to assess PTSD in a way that imposes little assessment burden while still providing a reliable categorization.

  14. Systemic inflammation and family history in relation to the prevalence of type 2 diabetes based on an alternating decision tree

    PubMed Central

    Uemura, Hirokazu; Ghaibeh, A. Ammar; Katsuura-Kamano, Sakurako; Yamaguchi, Miwa; Bahari, Tirani; Ishizu, Masashi; Moriguchi, Hiroki; Arisawa, Kokichi

    2017-01-01

    To investigate unknown patterns associated with type 2 diabetes in the Japanese population, we first used an alternating decision tree (ADTree) algorithm, a powerful classification algorithm from data mining, for the data from 1,102 subjects aged 35–69 years. On the basis of the investigated patterns, we then evaluated the associations of serum high-sensitivity C-reactive protein (hs-CRP) as a biomarker of systemic inflammation and family history of diabetes (negative, positive or unknown) with the prevalence of type 2 diabetes because their detailed associations have been scarcely reported. Elevated serum hs-CRP levels were proportionally associated with the increased prevalence of type 2 diabetes after adjusting for probable covariates, including body mass index and family history of diabetes (P for trend = 0.016). Stratified analyses revealed that elevated serum hs-CRP levels were proportionally associated with increased prevalence of diabetes in subjects without a family history of diabetes (P for trend = 0.020) but not in those with a family history or with an unknown family history of diabetes. Our study demonstrates that systemic inflammation was proportionally associated with increased prevalence of type 2 diabetes even after adjusting for body mass index, especially in subjects without a family history of diabetes. PMID:28361994

  15. Landsat-derived cropland mask for Tanzania using 2010-2013 time series and decision tree classifier methods

    NASA Astrophysics Data System (ADS)

    Justice, C. J.

    2015-12-01

    80% of Tanzania's population is involved in the agriculture sector. Despite this national dependence, agricultural reporting is minimal and monitoring efforts are in their infancy. The cropland mask developed through this study provides the framework for agricultural monitoring through informing analysis of crop conditions, dispersion, and intensity at a national scale. Tanzania is dominated by smallholder agricultural systems with an average field size of less than one hectare (Sarris et al, 2006). At this field scale, previous classifications of agricultural land in Tanzania using MODIS course resolution data are insufficient to inform a working monitoring system. The nation-wide cropland mask in this study was developed using composited Landsat tiles from a 2010-2013 time series. Decision tree classifiers methods were used in the study with representative training areas collected for agriculture and no agriculture using appropriate indices to separate these classes (Hansen et al, 2013). Validation was done using random sample and high resolution satellite images to compare Agriculture and No agriculture samples from the study area. The techniques used in this study were successful and have the potential to be adapted for other countries, allowing targeted monitoring efforts to improve food security, market price, and inform agricultural policy.

  16. Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing

    NASA Astrophysics Data System (ADS)

    Sugumaran, V.; Muralidharan, V.; Ramachandran, K. I.

    2007-02-01

    Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing's nature of vibration reveals its condition and the features that show the nature, are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Often the problem is, finding out good features that discriminate the different fault conditions of the bearing. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper illustrates the use of a Decision Tree that identifies the best features from a given set of samples for the purpose of classification. It uses Proximal Support Vector Machine (PSVM), which has the capability to efficiently classify the faults using statistical features. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted therefrom and classified successfully using PSVM and SVM. The results of PSVM and SVM are compared.

  17. An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers.

    PubMed

    Ebtehaj, Isa; Bonakdari, Hossein; Zaji, Amir Hossein

    2016-01-01

    In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (C(V)) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R(2) = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = -0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers.

  18. Systemic inflammation and family history in relation to the prevalence of type 2 diabetes based on an alternating decision tree.

    PubMed

    Uemura, Hirokazu; Ghaibeh, A Ammar; Katsuura-Kamano, Sakurako; Yamaguchi, Miwa; Bahari, Tirani; Ishizu, Masashi; Moriguchi, Hiroki; Arisawa, Kokichi

    2017-03-31

    To investigate unknown patterns associated with type 2 diabetes in the Japanese population, we first used an alternating decision tree (ADTree) algorithm, a powerful classification algorithm from data mining, for the data from 1,102 subjects aged 35-69 years. On the basis of the investigated patterns, we then evaluated the associations of serum high-sensitivity C-reactive protein (hs-CRP) as a biomarker of systemic inflammation and family history of diabetes (negative, positive or unknown) with the prevalence of type 2 diabetes because their detailed associations have been scarcely reported. Elevated serum hs-CRP levels were proportionally associated with the increased prevalence of type 2 diabetes after adjusting for probable covariates, including body mass index and family history of diabetes (P for trend = 0.016). Stratified analyses revealed that elevated serum hs-CRP levels were proportionally associated with increased prevalence of diabetes in subjects without a family history of diabetes (P for trend = 0.020) but not in those with a family history or with an unknown family history of diabetes. Our study demonstrates that systemic inflammation was proportionally associated with increased prevalence of type 2 diabetes even after adjusting for body mass index, especially in subjects without a family history of diabetes.

  19. Assessing and monitoring the risk of desertification in Dobrogea, Romania, using Landsat data and decision tree classifier.

    PubMed

    Vorovencii, Iosif

    2015-04-01

    The risk of the desertification of a part of Romania is increasingly evident, constituting a serious problem for the environment and the society. This article attempts to assess and monitor the risk of desertification in Dobrogea using Landsat Thematic Mapper (TM) satellite images acquired in 1987, 1994, 2000, 2007 and 2011. In order to assess the risk of desertification, we used as indicators the Modified Soil Adjustment Vegetation Index 1 (MSAVI1), the Moving Standard Deviation Index (MSDI) and the albedo, indices relating to the vegetation conditions, the landscape pattern and micrometeorology. The decision tree classifier (DTC) was also used on the basis of pre-established rules, and maps displaying six grades of desertification risk were obtained: non, very low, low, medium, high and severe. Land surface temperature (LST) was also used for the analysis. The results indicate that, according to pre-established rules for the period of 1987-2011, there are two grades of desertification risk that have an ascending trend in Dobrogea, namely very low and medium desertification. An investigation into the causes of the desertification risk revealed that high temperature is the main factor, accompanied by the destruction of forest shelterbelts and of the irrigation system and, to a smaller extent, by the fragmentation of agricultural land and the deforestation in the study area.

  20. A Decision-Tree Approach to the Assessment of Posttraumatic Stress Disorder: Engineering Empirically Rigorous and Ecologically Valid Assessment Measures

    PubMed Central

    Stewart, Regan W.; Tuerk, Peter W.; Metzger, Isha W.; Davidson, Tatiana M.; Young, John

    2017-01-01

    Structured diagnostic interviews are widely considered to be the optimal method of assessing symptoms of posttraumatic stress; however, few clinicians report using structured assessments to guide clinical practice. One commonly cited impediment to these assessment approaches is the amount of time required for test administration and interpretation. Empirically keyed methods to reduce the administration time of structured assessments may be a viable solution to increase the use of standardized and reliable diagnostic tools. Thus, the present research conducted an initial feasibility study using a sample of treatment-seeking military veterans (N = 1,517) to develop a truncated assessment protocol based on the Clinician-Administered Posttraumatic Stress Disorder (PTSD) Scale (CAPS). Decision-tree analysis was utilized to identify a subset of predictor variables among the CAPS items that were most predictive of a diagnosis of PTSD. The algorithm-driven, atheoretical sequence of questions reduced the number of items administered by more than 75% and classified the validation sample at 92% accuracy. These results demonstrated the feasibility of developing a protocol to assess PTSD in a way that imposes little assessment burden while still providing a reliable categorization. PMID:26654473

  1. Rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems in the presence of trees.

    PubMed

    Scholz, Miklas; Uzomah, Vincent C

    2013-08-01

    The retrofitting of sustainable drainage systems (SuDS) such as permeable pavements is currently undertaken ad hoc using expert experience supported by minimal guidance based predominantly on hard engineering variables. There is a lack of practical decision support tools useful for a rapid assessment of the potential of ecosystem services when retrofitting permeable pavements in urban areas that either feature existing trees or should be planted with trees in the near future. Thus the aim of this paper is to develop an innovative rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems close to trees. This unique tool proposes the retrofitting of permeable pavements that obtained the highest ecosystem service score for a specific urban site enhanced by the presence of trees. This approach is based on a novel ecosystem service philosophy adapted to permeable pavements rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For an example case study area such as Greater Manchester, which was dominated by Sycamore and Common Lime, a comparison with the traditional approach of determining community and environment variables indicates that permeable pavements are generally a preferred SuDS option. Permeable pavements combined with urban trees received relatively high scores, because of their great potential impact in terms of water and air quality improvement, and flood control, respectively. The outcomes of this paper are likely to lead to more combined permeable pavement and tree systems in the urban landscape, which are beneficial for humans and the environment.

  2. The risk of disabling, surgery and reoperation in Crohn’s disease – A decision tree-based approach to prognosis

    PubMed Central

    Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula

    2017-01-01

    Introduction Crohn’s disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients’ risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. Materials and methods This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Results Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50–4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09–0.25] and 0.50 [0.24–1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. Conclusions The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation. PMID:28225800

  3. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

    NASA Astrophysics Data System (ADS)

    Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

    2013-11-01

    Decision tree (DT) machine learning algorithm was used to map the flood susceptible areas in Kelantan, Malaysia.We used an ensemble frequency ratio (FR) and logistic regression (LR) model in order to overcome weak points of the LR.Combined method of FR and LR was used to map the susceptible areas in Kelantan, Malaysia.Results of both methods were compared and their efficiency was assessed.Most influencing conditioning factors on flooding were recognized.

  4. Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets.

    PubMed

    Carbon-Mangels, Miriam; Hutter, Michael C

    2011-10-01

    Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over-determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to model the probability distribution of descriptors values used for binary classification using n-fold cross-validation. As a measure for the discriminative power of the classifiers, the symmetric form of the Kullback-Leibler divergence of their probability distributions was computed. We found that the most relevant descriptors possess a Gaussian-like distribution of their values, show the largest divergences, and therefore appear most often in the cross-validation scenario. The results were compared to those of the LASSO feature selection method applied to multiple decision trees and support vector machine approaches for data sets of substrates and nonsubstrates of three Cytochrome P450 isoenzymes, which comprise strongly unbalanced compound distributions. In contrast to decision trees and support vector machines, the performance of Bayesian estimates is less affected by unbalanced data sets. This strategy reveals those descriptors that allow a simple linear separation of the classes, whereas the superior accuracy of decision trees and support vector machines can be attributed to nonlinear separation, which are in turn more prone to overfitting.

  5. IHC and the WHO classification of lymphomas: cost effective immunohistochemistry using a deductive reasoning "decision tree" approach.

    PubMed

    Taylor, Clive R

    2009-10-01

    The 2008 World Health Organization Classification of Tumors of the Hematopoietic and Lymphoid Tissues defines current standards of practice for the diagnosis and classification of malignant lymphomas and related entities. More than 50 different types of lymphomas are described, combining fine morphologic criteria with immunohistochemical (IHC), and sometimes molecular, findings. Faced with such a broad range of different lymphomas, some encountered only rarely, and a rapidly growing, ever changing, armamentarium of approximately 80 pertinent IHC "stains", the challenge to the pathologist is to employ IHC in an efficient manner, to arrive at an assured diagnosis as rapidly as possible. This review uses deductive reasoning, after a decision tree or dendrogram model that relies upon recognition of basic morphologic patterns for efficient selection, use and interpretation of IHC markers to classify node-based malignancies by the World Health Organization schema. The review is divided into 2 parts, the first addressing those lymphomas that produce a follicular or nodular pattern of lymph nodal involvement; the second addressing diffuse proliferations in lymph nodes. It is accepted that only specialized centers are able to apply all of the technical resources and experience necessary for definitive diagnosis of unusual cases. Emphasis therefore is given to the more common lymphomas and the more commonly available IHC "stains", for a pragmatic and practical approach that is both broadly feasible and cost effective. By this method an assured diagnosis may be reached in the majority of nodal lymphomas, at the same time developing a sufficiency of data to recognize those rare or atypical cases that require referral to a specialized center.

  6. Energy spectra unfolding of fast neutron sources using the group method of data handling and decision tree algorithms

    NASA Astrophysics Data System (ADS)

    Hosseini, Seyed Abolfazl; Afrakoti, Iman Esmaili Paeen

    2017-04-01

    Accurate unfolding of the energy spectrum of a neutron source gives important information about unknown neutron sources. The obtained information is useful in many areas like nuclear safeguards, nuclear nonproliferation, and homeland security. In the present study, the energy spectrum of a poly-energetic fast neutron source is reconstructed using the developed computational codes based on the Group Method of Data Handling (GMDH) and Decision Tree (DT) algorithms. The neutron pulse height distribution (neutron response function) in the considered NE-213 liquid organic scintillator has been simulated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). The developed computational codes based on the GMDH and DT algorithms use some data for training, testing and validation steps. In order to prepare the required data, 4000 randomly generated energy spectra distributed over 52 bins are used. The randomly generated energy spectra and the simulated neutron pulse height distributions by MCNPX-ESUT for each energy spectrum are used as the output and input data. Since there is no need to solve the inverse problem with an ill-conditioned response matrix, the unfolded energy spectrum has the highest accuracy. The 241Am-9Be and 252Cf neutron sources are used in the validation step of the calculation. The unfolded energy spectra for the used fast neutron sources have an excellent agreement with the reference ones. Also, the accuracy of the unfolded energy spectra obtained using the GMDH is slightly better than those obtained from the DT. The results obtained in the present study have good accuracy in comparison with the previously published paper based on the logsig and tansig transfer functions.

  7. Assessment of the potential allergenicity of ice structuring protein type III HPLC 12 using the FAO/WHO 2001 decision tree for novel foods.

    PubMed

    Bindslev-Jensen, C; Sten, E; Earl, L K; Crevel, R W R; Bindslev-Jensen, U; Hansen, T K; Stahl Skov, P; Poulsen, L K

    2003-01-01

    The introduction of novel proteins into foods carries a risk of eliciting allergic reactions in individuals sensitive to the introduced protein. Therefore, decision trees for evaluation of the risk have been developed, the latest being proposed by WHO/FAO early in 2001. Proteins developed using modern biotechnology and derived from fish are being considered for use in food and other applications, and since allergy to fish is well established, a potential risk from such proteins to susceptible human beings exists. The overall aim of the study was to investigate the potential allergenicity of an Ice Structuring Protein (ISP) originating from an arctic fish (the ocean pout, Macrozoarces americanus) using the newly developed decision tree proposed by FAO/WHO. The methods used were those proposed by FAO/WHO including amino acid sequence analysis for sequence similarity to known allergens, methods for assessing degradability under standardised conditions, assays for detection of specific IgE against the protein (Maxisorb RAST) and histamine release from human basophils. In the present paper we describe the serum screening phase of the study and discuss the overall application of the decision tree to the assessment of the potential allergenicity of ISP Type III. In an accompanying paper [Food Chem. Toxicol. 40 (2002) 965], we detail the specific methodology used for the sequence analysis and assessment of resistance to pepsin-catalysed proteolysis of this protein. The ISP showed no sequence similarity to known allergens nor was it stable to proteolytic degradation using standardised methods. Using sera from 20 patients with a well-documented clinical history of fish allergy, positive in skin prick tests to ocean pout, eel pout and eel were used, positive IgE-binding in vitro to extracts of the same fish was confirmed. The sera also elicited histamine release in vitro in the presence of the same extracts. The ISP was negative in all cases in the same experiments. Using the

  8. Decision Tree Phytoremediation

    DTIC Science & Technology

    1999-12-01

    Volatile metals are taken up, changed in species, and transpired. Mercury and selenium 1.3.1 Applicable or Relevant and Appropriate Requirements...disposal methods must be developed and approved by regulatory agencies. 5. Transpiration of heavy metals such as mercury or organic contaminants such...heavy metals, such as mercury , or organic contaminants, such as TCE, may create a hazard to human health or the environment. The transpiration products

  9. Assessing the safety of co-exposure to food packaging migrants in food and water using the maximum cumulative ratio and an established decision tree.

    PubMed

    Price, Paul; Zaleski, Rosemary; Hollnagel, Heli; Ketelslegers, Hans; Han, Xianglu

    2014-01-01

    Food contact materials can release low levels of multiple chemicals (migrants) into foods and beverages, to which individuals can be exposed through food consumption. This paper investigates the potential for non-carcinogenic effects from exposure to multiple migrants using the Cefic Mixtures Ad hoc Team (MIAT) decision tree. The purpose of the assessment is to demonstrate how the decision tree can be applied to concurrent exposures to multiple migrants using either hazard or structural data on the specific components, i.e. based on the acceptable daily intake (ADI) or the threshold of toxicological concern. The tree was used to assess risks from co-exposure to migrants reported in a study on non-intentionally added substances (NIAS) eluting from food contact-grade plastic and two studies of water bottles: one on organic compounds and the other on ionic forms of various elements. The MIAT decision tree assigns co-exposures to different risk management groups (I, II, IIIA and IIIB) based on the hazard index, and the maximum cumulative ratio (MCR). The predicted co-exposures for all examples fell into Group II (low toxicological concern) and had MCR values of 1.3 and 2.4 (indicating that one or two components drove the majority of the mixture's toxicity). MCR values from the study of inorganic ions (126 mixtures) ranged from 1.1 to 3.8 for glass and from 1.1 to 5.0 for plastic containers. The MCR values indicated that a single compound drove toxicity in 58% of the mixtures. MCR values also declined with increases in the hazard index for the screening assessments of exposure (suggesting fewer substances contributed as risk potential increased). Overall, it can be concluded that the data on co-exposure to migrants evaluated in these case studies are of low toxicological concern and the safety assessment approach described in this paper was shown to be a helpful screening tool.

  10. Measurement of single top quark production in the tau+jets channnel using boosted decision trees at D0

    SciTech Connect

    Liu, Zhiyi

    2009-12-01

    The top quark is the heaviest known matter particle and plays an important role in the Standard Model of particle physics. At hadron colliders, it is possible to produce single top quarks via the weak interaction. This allows a direct measurement of the CKM matrix element Vtb and serves as a window to new physics. The first direct measurement of single top quark production with a tau lepton in the final state (the tau+jets channel) is presented in this thesis. The measurement uses 4.8 fb-1 of Tevatron Run II data in p$\\bar{p}$ collisions at √s = 1.96 TeV acquired by the D0 experiment. After selecting a data sample and building a background model, the data and background model are in good agreement. A multivariate technique, boosted decision trees, is employed in discriminating the small single top quark signal from a large background. The expected sensitivity of the tau+jets channel in the Standard Model is 1.8 standard deviations. Using a Bayesian statistical approach, an upper limit on the cross section of single top quark production in the tau+jets channel is measured as 7.3 pb at 95% confidence level, and the cross section is measured as 3.4-1.8+2.0 pb. The result of the single top quark production in the tau+jets channel is also combined with those in the electron+jets and muon+jets channels. The expected sensitivity of the electron, muon and tau combined analysis is 4.7 standard deviations, to be compared to 4.5 standard deviations in electron and muon alone. The measured cross section in the three combined final states is σ(p$\\bar{p}$ → tb + X,tqb + X) = 3.84-0.83+0.89 pb. A lower limit on |Vtb| is also measured in the three combined final states to be larger than 0.85 at 95% confidence level. These results are consistent with Standard Model expectations.

  11. Decision tree model for predicting long-term outcomes in children with out-of-hospital cardiac arrest: a nationwide, population-based observational study

    PubMed Central

    2014-01-01

    Introduction At hospital arrival, early prognostication for children after out-of-hospital cardiac arrest (OHCA) might help clinicians formulate strategies, particularly in the emergency department. In this study, we aimed to develop a simple and generally applicable bedside tool for predicting outcomes in children after cardiac arrest. Methods We analyzed data of 5,379 children who had undergone OHCA. The data were extracted from a prospectively recorded, nationwide, Utstein-style Japanese database. The primary endpoint was survival with favorable neurological outcome (Cerebral Performance Category (CPC) scale categories 1 and 2) at 1 month after OHCA. We developed a decision tree prediction model by using data from a 2-year period (2008 to 2009, n = 3,693), and the data were validated using external data from 2010 (n = 1,686). Results Recursive partitioning analysis for 11 predictors in the development cohort indicated that the best single predictor for CPC 1 and 2 at 1 month was the prehospital return of spontaneous circulation (ROSC). The next predictor for children with prehospital ROSC was an initial shockable rhythm. For children without prehospital ROSC, the next best predictor was a witnessed arrest. Use of a simple decision tree prediction model permitted stratification into four outcome prediction groups: good (prehospital ROSC and initial shockable rhythm), moderately good (prehospital ROSC and initial nonshockable rhythm), poor (prehospital non-ROSC and witnessed arrest) and very poor (prehospital non-ROSC and unwitnessed arrest). By using this model, we identified patient groups ranging from 0.2% to 66.2% for 1-month CPC 1 and 2 probabilities. The validated decision tree prediction model demonstrated a sensitivity of 69.7% (95% confidence interval (CI) = 58.7% to 78.9%), a specificity of 95.2% (95% CI = 94.1% to 96.2%) and an area under the receiver operating characteristic curve of 0.88 (95% CI = 0.87 to 0.90) for predicting 1-month

  12. Can Religious Beliefs be a Protective Factor for Suicidal Behavior? A Decision Tree Analysis in a Mid-Sized City in Iran, 2013.

    PubMed

    Baneshi, Mohammad Reza; Haghdoost, Ali Akbar; Zolala, Farzaneh; Nakhaee, Nouzar; Jalali, Maryam; Tabrizi, Reza; Akbari, Maryam

    2017-04-01

    This study aimed to assess using tree-based models the impact of different dimensions of religion and other risk factors on suicide attempts in the Islamic Republic of Iran. Three hundred patients who attempted suicide and 300 age- and sex-matched patient attendants with other types of disease who referred to Kerman Afzalipour Hospital were recruited for this study following a convenience sampling. Religiosity was assessed by the Duke University Religion Index. A tree-based model was constructed using the Gini Index as the homogeneity criterion. A complementary discrimination analysis was also applied. Variables contributing to the construction of the tree were stressful life events, mental disorder, family support, and religious belief. Strong religious belief was a protective factor for those with a low number of stressful life events and those with a high mental disorder score; 72 % of those who formed these two groups had not attempted suicide. Moreover, 63 % of those with a high number of stressful life events, strong family support, strong problem-solving skills, and a low mental disorder score were less likely to attempt suicide. The significance of four other variables, GHQ, problem-coping skills, friend support, and neuroticism, was revealed in the discrimination analysis. Religious beliefs seem to be an independent factor that can predict risk for suicidal behavior. Based on the decision tree, religious beliefs among people with a high number of stressful life events might not be a dissuading factor. Such subjects need more family support and problem-solving skills.

  13. Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research.

    PubMed

    Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

    2011-06-01

    The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was carried out in patients with cancer receiving an inpatient rehabilitation program to identify prototypical combinations of treatment elements. In the second study, growth mixture modeling was used to identify latent trajectory classes based on weekly symptom severity measurements during inpatient treatment of patients with mental disorders. A graphical tool, the Class Evolution Tree, was developed, and its central components were described. The Class Evolution Tree can be used in addition to statistical criteria to systematically address the issue of number of classes in explorative categorical latent variable modeling.

  14. Application of Decision Tree to Obtain Optimal Operation Rules for Reservoir Flood Control Considering Sediment Desilting-Case Study of Tseng Wen Reservoir

    NASA Astrophysics Data System (ADS)

    ShiouWei, L.

    2014-12-01

    Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan.  Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating.  Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the

  15. Segregating the Effects of Seed Traits and Common Ancestry of Hardwood Trees on Eastern Gray Squirrel Foraging Decisions

    PubMed Central

    Sundaram, Mekala; Willoughby, Janna R.; Lichti, Nathanael I.; Steele, Michael A.; Swihart, Robert K.

    2015-01-01

    The evolution of specific seed traits in scatter-hoarded tree species often has been attributed to granivore foraging behavior. However, the degree to which foraging investments and seed traits correlate with phylogenetic relationships among trees remains unexplored. We presented seeds of 23 different hardwood tree species (families Betulaceae, Fagaceae, Juglandaceae) to eastern gray squirrels (Sciurus carolinensis), and measured the time and distance travelled by squirrels that consumed or cached each seed. We estimated 11 physical and chemical seed traits for each species, and the phylogenetic relationships between the 23 hardwood trees. Variance partitioning revealed that considerable variation in foraging investment was attributable to seed traits alone (27–73%), and combined effects of seed traits and phylogeny of hardwood trees (5–55%). A phylogenetic PCA (pPCA) on seed traits and tree phylogeny resulted in 2 “global” axes of traits that were phylogenetically autocorrelated at the family and genus level and a third “local” axis in which traits were not phylogenetically autocorrelated. Collectively, these axes explained 30–76% of the variation in squirrel foraging investments. The first global pPCA axis, which produced large scores for seed species with thin shells, low lipid and high carbohydrate content, was negatively related to time to consume and cache seeds and travel distance to cache. The second global pPCA axis, which produced large scores for seeds with high protein, low tannin and low dormancy levels, was an important predictor of consumption time only. The local pPCA axis primarily reflected kernel mass. Although it explained only 12% of the variation in trait space and was not autocorrelated among phylogenetic clades, the local axis was related to all four squirrel foraging investments. Squirrel foraging behaviors are influenced by a combination of phylogenetically conserved and more evolutionarily labile seed traits that is

  16. Segregating the Effects of Seed Traits and Common Ancestry of Hardwood Trees on Eastern Gray Squirrel Foraging Decisions.

    PubMed

    Sundaram, Mekala; Willoughby, Janna R; Lichti, Nathanael I; Steele, Michael A; Swihart, Robert K

    2015-01-01

    The evolution of specific seed traits in scatter-hoarded tree species often has been attributed to granivore foraging behavior. However, the degree to which foraging investments and seed traits correlate with phylogenetic relationships among trees remains unexplored. We presented seeds of 23 different hardwood tree species (families Betulaceae, Fagaceae, Juglandaceae) to eastern gray squirrels (Sciurus carolinensis), and measured the time and distance travelled by squirrels that consumed or cached each seed. We estimated 11 physical and chemical seed traits for each species, and the phylogenetic relationships between the 23 hardwood trees. Variance partitioning revealed that considerable variation in foraging investment was attributable to seed traits alone (27-73%), and combined effects of seed traits and phylogeny of hardwood trees (5-55%). A phylogenetic PCA (pPCA) on seed traits and tree phylogeny resulted in 2 "global" axes of traits that were phylogenetically autocorrelated at the family and genus level and a third "local" axis in which traits were not phylogenetically autocorrelated. Collectively, these axes explained 30-76% of the variation in squirrel foraging investments. The first global pPCA axis, which produced large scores for seed species with thin shells, low lipid and high carbohydrate content, was negatively related to time to consume and cache seeds and travel distance to cache. The second global pPCA axis, which produced large scores for seeds with high protein, low tannin and low dormancy levels, was an important predictor of consumption time only. The local pPCA axis primarily reflected kernel mass. Although it explained only 12% of the variation in trait space and was not autocorrelated among phylogenetic clades, the local axis was related to all four squirrel foraging investments. Squirrel foraging behaviors are influenced by a combination of phylogenetically conserved and more evolutionarily labile seed traits that is consistent with a weak

  17. Improvement of the identification of four heavy metals in environmental samples by using predictive decision tree models coupled with a set of five bioluminescent bacteria.

    PubMed

    Jouanneau, Sulivan; Durand, Marie-José; Courcoux, Philippe; Blusseau, Thomas; Thouand, Gérald

    2011-04-01

    A primary statistical model based on the crossings between the different detection ranges of a set of five bioluminescent bacterial strains was developed to identify and quantify four metals which were at several concentrations in different mixtures: cadmium, arsenic III, mercury, and copper. Four specific decision trees based on the CHAID algorithm (CHi-squared Automatic Interaction Detector type) which compose this model were designed from a database of 576 experiments (192 different mixture conditions). A specific software, 'Metalsoft', helped us choose the best decision tree and a user-friendly way to identify the metal. To validate this innovative approach, 18 environmental samples containing a mixture of these metals were submitted to a bioassay and to standardized chemical methods. The results show on average a high correlation of 98.6% for the qualitative metal identification and 94.2% for the quantification. The results are particularly encouraging, and our model is able to provide semiquantitative information after only 60 min without pretreatments of samples.

  18. Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.

    PubMed

    Macmillan, Donna S; Canipa, Steven J; Chilton, Martyn L; Williams, Richard V; Barber, Christopher G

    2016-04-01

    There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays.

  19. Using Evidence-Based Decision Trees Instead of Formulas to Identify At-Risk Readers. REL 2014-036

    ERIC Educational Resources Information Center

    Koon, Sharon; Petscher, Yaacov; Foorman, Barbara R.

    2014-01-01

    This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in…

  20. The Relation of Student Behavior, Peer Status, Race, and Gender to Decisions about School Discipline Using CHAID Decision Trees and Regression Modeling

    ERIC Educational Resources Information Center

    Horner, Stacy B.; Fireman, Gary D.; Wang, Eugene W.

    2010-01-01

    Peer nominations and demographic information were collected from a diverse sample of 1493 elementary school participants to examine behavior (overt and relational aggression, impulsivity, and prosociality), context (peer status), and demographic characteristics (race and gender) as predictors of teacher and administrator decisions about…

  1. A Multi Criteria Group Decision-Making Model for Teacher Evaluation in Higher Education Based on Cloud Model and Decision Tree

    ERIC Educational Resources Information Center

    Chang, Ting-Cheng; Wang, Hui

    2016-01-01

    This paper proposes a cloud multi-criteria group decision-making model for teacher evaluation in higher education which is involving subjectivity, imprecision and fuzziness. First, selecting the appropriate evaluation index depending on the evaluation objectives, indicating a clear structural relationship between the evaluation index and…

  2. Under which conditions, additional monitoring data are worth gathering for improving decision making? Application of the VOI theory in the Bayesian Event Tree eruption forecasting framework

    NASA Astrophysics Data System (ADS)

    Loschetter, Annick; Rohmer, Jérémy

    2016-04-01

    Standard and new generation of monitoring observations provide in almost real-time important information about the evolution of the volcanic system. These observations are used to update the model and contribute to a better hazard assessment and to support decision making concerning potential evacuation. The framework BET_EF (based on Bayesian Event Tree) developed by INGV enables dealing with the integration of information from monitoring with the prospect of decision making. Using this framework, the objectives of the present work are i. to propose a method to assess the added value of information (within the Value Of Information (VOI) theory) from monitoring; ii. to perform sensitivity analysis on the different parameters that influence the VOI from monitoring. VOI consists in assessing the possible increase in expected value provided by gathering information, for instance through monitoring. Basically, the VOI is the difference between the value with information and the value without additional information in a Cost-Benefit approach. This theory is well suited to deal with situations that can be represented in the form of a decision tree such as the BET_EF tool. Reference values and ranges of variation (for sensitivity analysis) were defined for input parameters, based on data from the MESIMEX exercise (performed at Vesuvio volcano in 2006). Complementary methods for sensitivity analyses were implemented: local, global using Sobol' indices and regional using Contribution to Sample Mean and Variance plots. The results (specific to the case considered) obtained with the different techniques are in good agreement and enable answering the following questions: i. Which characteristics of monitoring are important for early warning (reliability)? ii. How do experts' opinions influence the hazard assessment and thus the decision? Concerning the characteristics of monitoring, the more influent parameters are the means rather than the variances for the case considered

  3. Identification of Some Zeolite Group Minerals by Application of Artificial Neural Network and Decision Tree Algorithm Based on SEM-EDS Data

    NASA Astrophysics Data System (ADS)

    Akkaş, Efe; Evren Çubukçu, H.; Akin, Lutfiye; Erkut, Volkan; Yurdakul, Yasin; Karayigit, Ali Ihsan

    2016-04-01

    Identification of zeolite group minerals is complicated due to their similar chemical formulas and habits. Although the morphologies of various zeolite crystals can be recognized under Scanning Electron Microscope (SEM), it is relatively more challenging and problematic process to identify zeolites using their mineral chemical data. SEMs integrated with energy dispersive X-ray spectrometers (EDS) provide fast and reliable chemical data of minerals. However, considering elemental similarities of characteristic chemical formulae of zeolite species (e.g. Clinoptilolite ((Na,K,Ca)2 -3Al3(Al,Si)2Si13O3612H2O) and Erionite ((Na2,K2,Ca)2Al4Si14O36ṡ15H2O)) EDS data alone does not seem to be sufficient for correct identification. Furthermore, the physical properties of the specimen (e.g. roughness, electrical conductivity) and the applied analytical conditions (e.g. accelerating voltage, beam current, spot size) of the SEM-EDS should be uniform in order to obtain reliable elemental results of minerals having high alkali (Na, K) and H2O (approx. %14-18) contents. This study which was funded by The Scientific and Technological Research Council of Turkey (TUBITAK Project No: 113Y439), aims to construct a database as large as possible for various zeolite minerals and to develop a general prediction model for the identification of zeolite minerals using SEM-EDS data. For this purpose, an artificial neural network and rule based decision tree algorithm were employed. Throughout the analyses, a total of 1850 chemical data were collected from four distinct zeolite species, (Clinoptilolite-Heulandite, Erionite, Analcime and Mordenite) observed in various rocks (e.g. coals, pyroclastics). In order to obtain a representative training data set for each minerals, a selection procedure for reference mineral analyses was applied. During the selection procedure, SEM based crystal morphology data, XRD spectra and re-calculated cationic distribution, obtained by EDS have been used for the

  4. Fast decision tree-based method to index large DNA-protein sequence databases using hybrid distributed-shared memory programming model.

    PubMed

    Jaber, Khalid Mohammad; Abdullah, Rosni; Rashid, Nur'Aini Abdul

    2014-01-01

    In recent times, the size of biological databases has increased significantly, with the continuous growth in the number of users and rate of queries; such that some databases have reached the terabyte size. There is therefore, the increasing need to access databases at the fastest rates possible. In this paper, the decision tree indexing model (PDTIM) was parallelised, using a hybrid of distributed and shared memory on resident database; with horizontal and vertical growth through Message Passing Interface (MPI) and POSIX Thread (PThread), to accelerate the index building time. The PDTIM was implemented using 1, 2, 4 and 5 processors on 1, 2, 3 and 4 threads respectively. The results show that the hybrid technique improved the speedup, compared to a sequential version. It could be concluded from results that the proposed PDTIM is appropriate for large data sets, in terms of index building time.

  5. A comparison of the decision tree approach and the neural-networks-based heuristic dynamic programming approach for subcircuit extraction problem

    NASA Astrophysics Data System (ADS)

    Zhang, Nian; Wunsch, Donald C., II

    2003-08-01

    The applications of non-standard logic device are increasing fast in the industry. Many of these applications require high speed, low power, functionality and flexibility, which cannot be obtained by standard logic device. These special logic cells can be constructed by the topology design strategy automatically or manually. However, the need arises for the topology design verification. The layout versus schematic (LVS) analysis is an essential part of topology design verification, and subcircuit extraction is one of the operations in the LVS testing. In this paper, we first provided an efficient decision tree approach to the graph isomorphism problem, and then effectively applied it to the subcircuit extraction problem based on the solution to the graph isomorphism problem. To evaluate its performance, we compare it with the neural networks based heuristic dynamic programming algorithm (SubHDP) which is by far one of the fastest algorithms for subcircuit extraction problem.

  6. Sediment source fingerprinting as an aid to catchment management: A review of the current state of knowledge and a methodological decision-tree for end-users.

    PubMed

    Collins, A L; Pulley, S; Foster, I D L; Gellis, A; Porto, P; Horowitz, A J

    2016-10-12

    The growing awareness of the environmental significance of fine-grained sediment fluxes through catchment systems continues to underscore the need for reliable information on the principal sources of this material. Source estimates are difficult to obtain using traditional monitoring techniques, but sediment source fingerprinting or tracing procedures, have emerged as a potentially valuable alternative. Despite the rapidly increasing numbers of studies reporting the use of sediment source fingerprinting, several key challenges and uncertainties continue to hamper consensus among the international scientific community on key components of the existing methodological procedures. Accordingly, this contribution reviews and presents recent developments for several key aspects of fingerprinting, namely: sediment source classification, catchment source and target sediment sampling, tracer selection, grain size issues, tracer conservatism, source apportionment modelling, and assessment of source predictions using artificial mixtures. Finally, a decision-tree representing the current state of knowledge is presented, to guide end-users in applying the fingerprinting approach.

  7. Using Induction to Refine Information Retrieval Strategies

    NASA Technical Reports Server (NTRS)

    Baudin, Catherine; Pell, Barney; Kedar, Smadar

    1994-01-01

    Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

  8. Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

    PubMed

    Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

    2012-09-01

    In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

  9. Using Decision Tree Analysis to Understand Foundation Science Student Performance. Insight Gained at One South African University

    NASA Astrophysics Data System (ADS)

    Kirby, Nicola Frances; Dempster, Edith Roslyn

    2014-11-01

    The Foundation Programme of the Centre for Science Access at the University of KwaZulu-Natal, South Africa provides access to tertiary science studies to educationally disadvantaged students who do not meet formal faculty entrance requirements. The low number of students proceeding from the programme into mainstream is of concern, particularly given the national imperative to increase participation and levels of performance in tertiary-level science. An attempt was made to understand foundation student performance in a campus of this university, with the view to identifying challenges and opportunities for remediation in the curriculum and processes of selection into the programme. A classification and regression tree analysis was used to identify which variables best described student performance. The explanatory variables included biographical and school-history data, performance in selection tests, and socio-economic data pertaining to their year in the programme. The results illustrate the prognostic reliability of the model used to select students, raise concerns about the inefficiency of school performance indicators as a measure of students' academic potential in the Foundation Programme, and highlight the importance of accommodation arrangements and financial support for student success in their access year.

  10. Sequential inductive learning

    SciTech Connect

    Gratch, J.

    1996-12-31

    This article advocates a new model for inductive learning. Called sequential induction, it helps bridge classical fixed-sample learning techniques (which are efficient but difficult to formally characterize), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). Learning proceeds as a sequence of decisions which are informed by training data. By analyzing induction at the level of these decisions, and by utilizing the only enough data to make each decision, sequential induction provides statistical guarantees but with substantially less data than worst-case methods require. The sequential inductive model is also useful as a method for determining a sufficient sample size for inductive learning and as such, is relevant to learning problems where the preponderance of data or the cost of gathering data precludes the use of traditional methods.

  11. Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery

    USGS Publications Warehouse

    Helmer, E.H.; Kennaway, T.A.; Pedreros, D.H.; Clark, M.L.; Marcano-Vega, H.; Tieszen, L.L.; Ruzycki, T.R.; Schill, S.R.; Carrington, C.M.S.

    2008-01-01

    Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering

  12. Trees Are Terrific!

    ERIC Educational Resources Information Center

    Braus, Judy, Ed.

    1992-01-01

    Ranger Rick's NatureScope is a creative education series dedicated to inspiring in children an understanding and appreciation of the natural world while developing the skills they will need to make responsible decisions about the environment. Contents are organized into the following sections: (1) "What Makes a Tree a Tree?," including…

  13. Structural Equation Model Trees

    ERIC Educational Resources Information Center

    Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

    2013-01-01

    In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

  14. Support vector machine-based decision tree for snow cover extraction in mountain areas using high spatial resolution remote sensing image

    NASA Astrophysics Data System (ADS)

    Zhu, Liujun; Xiao, Pengfeng; Feng, Xuezhi; Zhang, Xueliang; Wang, Zuo; Jiang, Luyuan

    2014-01-01

    Snow cover extraction in mountain areas is a complex task, especially from high spatial resolution remote sensing (HSRRS) data. The influence of mountain shadows in HSRRS is severe and normalized difference snow index-based snow cover extraction methods are inaccessible. A decision tree building method for snow cover extraction (DTSE) integrated with an efficiency feature selection algorithm is proposed. The severe influence of terrain shadows is eliminated by extracting snow in sunlight and snow in shadow separately in different nodes. In the feature selection algorithm, deviation of fuzzy grade matrix is proposed as a class-specific criterion which improves the efficiency and robustness of the selected feature set, thus making the snow cover extraction accurate. Two experiments are carried out based on ZY-3 image of two regions (regions A and B) located in Tianshan Mountains, China. The experiment on region A achieves an adequate accuracy demonstrating the robustness of the DTSE building method. The experiment on region B shows that a general DTSE model achieves an unsatisfied accuracy for snow in shadow and DTSE rebuilding evidently improves the performance, thus providing an accurate and fast way to extract snow cover in mountain areas.

  15. The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment

    PubMed Central

    Chiang, Kai-Wei; Liao, Jhen-Kai; Tsai, Guang-Je; Chang, Hsiu-Wen

    2015-01-01

    Hardware sensors embedded in a smartphone allow the device to become an excellent mobile navigator. A smartphone is ideal for this task because its great international popularity has led to increased phone power and since most of the necessary infrastructure is already in place. However, using a smartphone for indoor pedestrian navigation can be problematic due to the low accuracy of sensors, imprecise predictability of pedestrian motion, and inaccessibility of the Global Navigation Satellite System (GNSS) in some indoor environments. Pedestrian Dead Reckoning (PDR) is one of the most common technologies used for pedestrian navigation, but in its present form, various errors tend to accumulate. This study introduces a fuzzy decision tree (FDT) aided by map information to improve the accuracy and stability of PDR with less dependency on infrastructure. First, the map is quickly surveyed by the Indoor Mobile Mapping System (IMMS). Next, Bluetooth beacons are implemented to enable the initializing of any position. Finally, map-aided FDT can estimate navigation solutions in real time. The experiments were conducted in different fields using a variety of smartphones and users in order to verify stability. The contrast PDR system demonstrates low stability for each case without pre-calibration and post-processing, but the proposed low-complexity FDT algorithm shows good stability and accuracy under the same conditions. PMID:26729114

  16. Assessment of the potential enhancement of rural food security in Mexico using decision tree land use classification on medium resolution satellite imagery

    NASA Astrophysics Data System (ADS)

    Bermeo, A.; Couturier, S.

    2017-01-01

    Because of its renewed importance in international agendas, food security in sub-tropical countries has been the object of studies at different scales, although the spatial components of food security are still largely undocumented. Among other aspects, food security can be assessed using a food selfsufficiency index. We propose a spatial representation of this assessment in the densely populated rural area of the Huasteca Poblana, Mexico, where there is a known tendency towards the loss of selfsufficiency of basic grains. The main agricultural systems in this area are the traditional milpa (a multicrop practice with maize as the main basic crop) system, coffee plantations and grazing land for bovine livestock. We estimate a potential additional milpa - based maize production by smallholders identifying the presence of extensive coffee and pasture systems in the production data of the agricultural census. The surface of extensive coffee plantations and pasture land were estimated using the detailed coffee agricultural census data, and a decision tree combining unsupervised and supervised spectral classification techniques of medium scale (Landsat) satellite imagery. We find that 30% of the territory would benefit more than 50% increment in food security and 13% could theoretically become maize self-sufficient from the conversion of extensive systems to the traditional multicrop milpa system.

  17. The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment.

    PubMed

    Chiang, Kai-Wei; Liao, Jhen-Kai; Tsai, Guang-Je; Chang, Hsiu-Wen

    2015-12-28

    Hardware sensors embedded in a smartphone allow the device to become an excellent mobile navigator. A smartphone is ideal for this task because its great international popularity has led to increased phone power and since most of the necessary infrastructure is already in place. However, using a smartphone for indoor pedestrian navigation can be problematic due to the low accuracy of sensors, imprecise predictability of pedestrian motion, and inaccessibility of the Global Navigation Satellite System (GNSS) in some indoor environments. Pedestrian Dead Reckoning (PDR) is one of the most common technologies used for pedestrian navigation, but in its present form, various errors tend to accumulate. This study introduces a fuzzy decision tree (FDT) aided by map information to improve the accuracy and stability of PDR with less dependency on infrastructure. First, the map is quickly surveyed by the Indoor Mobile Mapping System (IMMS). Next, Bluetooth beacons are implemented to enable the initializing of any position. Finally, map-aided FDT can estimate navigation solutions in real time. The experiments were conducted in different fields using a variety of smartphones and users in order to verify stability. The contrast PDR system demonstrates low stability for each case without pre-calibration and post-processing, but the proposed low-complexity FDT algorithm shows good stability and accuracy under the same conditions.

  18. Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study

    PubMed Central

    Ramezankhani, Azra; Hadavandi, Esmaeil; Pournik, Omid; Shahrabi, Jamal; Azizi, Fereidoun; Hadaegh, Farzad

    2016-01-01

    Objective The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Design Prospective cohort study. Setting Tehran Lipid and Glucose Study (TLGS). Methods A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999–2001) and (2002–2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. Primary outcome measure T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication. Results During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Conclusions Our study emphasises the utility of DT for exploring interactions between

  19. Decision tree analysis as a supplementary tool to enhance histomorphological differentiation when distinguishing human from non-human cranial bone in both burnt and unburnt states: A feasibility study.

    PubMed

    Simmons, T; Goodburn, B; Singhrao, S K

    2016-01-01

    This feasibility study was undertaken to describe and record the histological characteristics of burnt and unburnt cranial bone fragments from human and non-human bones. Reference series of fully mineralized, transverse sections of cranial bone, from all variables and specimen states, were prepared by manual cutting and semi-automated grinding and polishing methods. A photomicrograph catalogue reflecting differences in burnt and unburnt bone from human and non-humans was recorded and qualitative analysis was performed using an established classification system based on primary bone characteristics. The histomorphology associated with human and non-human samples was, for the main part, preserved following burning at high temperature. Clearly, fibro-lamellar complex tissue subtypes, such as plexiform or laminar primary bone, were only present in non-human bones. A decision tree analysis based on histological features provided a definitive identification key for distinguishing human from non-human bone, with an accuracy of 100%. The decision tree for samples where burning was unknown was 96% accurate, and multi-step classification to taxon was possible with 100% accuracy. The results of this feasibility study strongly suggest that histology remains a viable alternative technique if fragments of cranial bone require forensic examination in both burnt and unburnt states. The decision tree analysis may provide an additional but vital tool to enhance data interpretation. Further studies are needed to assess variation in histomorphology taking into account other cranial bones, ontogeny, species and burning conditions.

  20. Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound.

    PubMed

    Yun, Yejin Esther; Cotton, Cecilia A; Edginton, Andrea N

    2014-02-01

    Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.

  1. Gaining efficiency by parallel quantification and identification of iTRAQ-labeled peptides using HCD and decision tree guided CID/ETD on an LTQ Orbitrap.

    PubMed

    Mischerikow, Nikolai; van Nierop, Pim; Li, Ka Wan; Bernstein, Hans-Gert; Smit, August B; Heck, Albert J R; Altelaar, A F Maarten

    2010-10-01

    Isobaric stable isotope labeling of peptides using iTRAQ is an important method for MS based quantitative proteomics. Traditionally, quantitative analysis of iTRAQ labeled peptides has been confined to beam-type instruments because of the weak detection capabilities of ion traps for low mass ions. Recent technical advances in fragmentation techniques on linear ion traps and the hybrid linear ion trap-orbitrap allow circumventing this limitation. Namely, PQD and HCD facilitate iTRAQ analysis on these instrument types. Here we report a method for iTRAQ-based relative quantification on the ETD enabled LTQ Orbitrap XL, which is based on parallel peptide quantification and peptide identification. iTRAQ reporter ion generation is performed by HCD, while CID and ETD provide peptide identification data in parallel in the LTQ ion trap. This approach circumvents problems accompanying iTRAQ reporter ion generation with ETD and allows quantitative, decision tree-based CID/ETD experiments. Furthermore, the use of HCD solely for iTRAQ reporter ion read out significantly reduces the number of ions needed to obtain informative spectra, which significantly reduces the analysis time. Finally, we show that integration of this method, both with existing CID and ETD methods as well as with existing iTRAQ data analysis workflows, is simple to realize. By applying our approach to the analysis of the synapse proteome from human brain biopsies, we demonstrate that it outperforms a latest generation MALDI TOF/TOF instrument, with improvements in both peptide and protein identification and quantification. Conclusively, our work shows how HCD, CID and ETD can be beneficially combined to enable iTRAQ-based quantification on an ETD-enabled LTQ Orbitrap XL.

  2. Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network

    PubMed Central

    AMINI, Payam; AHMADINIA, Hasan; POOROLAJAL, Jalal; MOQADDASI AMIRI, Mohammad

    2016-01-01

    Background: We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM). Methods: We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated. Results: Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. PMID:27957463

  3. The use of the decision tree technique and image cytometry to characterize aggressiveness in World Health Organization (WHO) grade II superficial transitional cell carcinomas of the bladder.

    PubMed

    Decaestecker, C; van Velthoven, R; Petein, M; Janssen, T; Salmon, I; Pasteels, J L; van Ham, P; Schulman, C; Kiss, R

    1996-03-01

    The aggressiveness of human bladder tumours can be assessed by means of various classification systems, including the one proposed by the World Health Organization (WHO). According to the WHO classification, three levels of malignancy are identified as grades I (low), II (intermediate), and III (high). This classification system operates satisfactorily for two of the three grades in forecasting clinical progression, most grade I tumours being associated with good prognoses and most grade III with bad. In contrast, the grade II group is very heterogeneous in terms of their clinical behaviour. The present study used two computer-assisted methods to investigate whether it is possible to sub-classify grade II tumours: computer-assisted microscope analysis (image cytometry) of Feulgen-stained nuclei and the Decision Tree Technique. This latter technique belongs to the Supervised Learning Algorithm and enables an objective assessment to be made of the diagnostic value associated with a given parameter. The combined use of these two methods in a series of 292 superficial transitional cell carcinomas shows that it is possible to identify one subgroup of grade II tumours which behave clinically like grade I tumours and a second subgroup which behaves clinically like grade III tumours. Of the nine ploidy-related parameters computed by means of image cytometry [the DNA index (DI), DNA histogram type (DHT), and the percentages of diploid, hyperdiploid, triploid, hypertriploid, tetraploid, hypertetraploid, and polyploid cell nuclei], it was the percentage of hyperdiploid and hypertetraploid cell nuclei which enabled identification, rather than conventional parameters such as the DI or the DHT.

  4. Top Quark Produced Through the Electroweak Force: Discovery Using the Matrix Element Analysis and Search for Heavy Gauge Bosons Using Boosted Decision Trees

    SciTech Connect

    Pangilinan, Monica

    2010-05-01

    The top quark produced through the electroweak channel provides a direct measurement of the Vtb element in the CKM matrix which can be viewed as a transition rate of a top quark to a bottom quark. This production channel of top quark is also sensitive to different theories beyond the Standard Model such as heavy charged gauged bosons termed W'. This thesis measures the cross section of the electroweak produced top quark using a technique based on using the matrix elements of the processes under consideration. The technique is applied to 2.3 fb-1 of data from the D0 detector. From a comparison of the matrix element discriminants between data and the signal and background model using Bayesian statistics, we measure the cross section of the top quark produced through the electroweak mechanism σ(p$\\bar{p}$ → tb + X, tqb + X) = 4.30-1.20+0.98 pb. The measured result corresponds to a 4.9σ Gaussian-equivalent significance. By combining this analysis with other analyses based on the Bayesian Neural Network (BNN) and Boosted Decision Tree (BDT) method, the measured cross section is 3.94 ± 0.88 pb with a significance of 5.0σ, resulting in the discovery of electroweak produced top quarks. Using this measured cross section and constraining |Vtb| < 1, the 95% confidence level (C.L.) lower limit is |Vtb| > 0.78. Additionally, a search is made for the production of W' using the same samples from the electroweak produced top quark. An analysis based on the BDT method is used to separate the signal from expected backgrounds. No significant excess is found and 95% C.L. upper limits on the production cross section are set for W' with masses within 600-950 GeV. For four general models of W{prime} boson production using decay channel W' → t$\\bar{p}$, the lower mass limits are the following: M(W'L with SM couplings) > 840 GeV; M(W'R) > 880 GeV or 890 GeV if the right-handed neutrino is

  5. Top quark produced through the electroweak force: Discovery using the matrix element analysis and search for heavy gauge bosons using boosted decision trees

    NASA Astrophysics Data System (ADS)

    Pangilinan, Monica

    The top quark produced through the electroweak channel provides a direct measurement of the Vtb element in the CKM matrix which can be viewed as a transition rate of a top quark to a bottom quark. This production channel of top quark is also sensitive to different theories beyond the Standard Model such as heavy charged gauged bosons termed W'. This thesis measures the cross section of the electroweak produced top quark using a technique based on using the matrix elements of the processes under consideration. The technique is applied to 2.3 fb--1 of data from the DO detector. From a comparison of the matrix element discriminants between data and the signal and background model using Bayesian statistics, we measure the cross section of the top quark produced through the electroweak mechanism spp¯→ tb+X,tqb+X=4.30+0.98-1.2 0pb The measured result corresponds to a 4.9sigma Gaussian-equivalent significance. By combining this analysis with other analyses based on the Bayesian Neural Network (BNN) and Boosted Decision Tree (BDT) method, the measured cross section is 3.94 +/- 0.88 pb with a significance of 5.0sigma, resulting in the discovery of electroweak produced top quarks. Using this measured cross section and constraining |Vtb| < 1, the 95% confidence level (C.L.) lower limit is |Vtb| > 0.78. Additionally, a search is made for the production of W' using the same samples from the electroweak produced top quark. An analysis based on the BDT method is used to separate the signal from expected backgrounds. No significant excess is found and 95% C.L. upper limits on the production cross section are set for W' with masses within 600--950 GeV. For four general models of W' boson production using decay channel W' → tb¯, the lower mass limits are the following: M( W'L with SM couplings) > 840 GeV; M( W'R ) > 880 GeV or 890 GeV if the right-handed neutrino is lighter or heavier than W'R ; and M( W'L+R ) > 915 GeV.

  6. Technology transfer by means of fault tree synthesis

    NASA Astrophysics Data System (ADS)

    Batzias, Dimitris F.

    2012-12-01

    Since Fault Tree Analysis (FTA) attempts to model and analyze failure processes of engineering, it forms a common technique for good industrial practice. On the contrary, fault tree synthesis (FTS) refers to the methodology of constructing complex trees either from dentritic modules built ad hoc or from fault tress already used and stored in a Knowledge Base. In both cases, technology transfer takes place in a quasi-inductive mode, from partial to holistic knowledge. In this work, an algorithmic procedure, including 9 activity steps and 3 decision nodes is developed for performing effectively this transfer when the fault under investigation occurs within one of the latter stages of an industrial procedure with several stages in series. The main parts of the algorithmic procedure are: (i) the construction of a local fault tree within the corresponding production stage, where the fault has been detected, (ii) the formation of an interface made of input faults that might occur upstream, (iii) the fuzzy (to count for uncertainty) multicriteria ranking of these faults according to their significance, and (iv) the synthesis of an extended fault tree based on the construction of part (i) and on the local fault tree of the first-ranked fault in part (iii). An implementation is presented, referring to 'uneven sealing of Al anodic film', thus proving the functionality of the developed methodology.

  7. Learning classification trees

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1991-01-01

    Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

  8. High-precision analysis on annual variations of heavy metals, lead isotopes and rare earth elements in mangrove tree rings by inductively coupled plasma mass spectrometry

    NASA Astrophysics Data System (ADS)

    Yu, Ke-Fu; Kamber, Balz S.; Lawrence, Michael G.; Greig, Alan; Zhao, Jian-Xin

    2007-02-01

    Annual variations from 1982 to 1999 of a wide range of trace elements and reconnaissance Pb isotopes ( 207Pb/ 206Pb and 208Pb/ 206Pb) were analyzed by solution ICP-MS on digested ash from mangrove Rhizophora apiculata, obtained from Leizhou Peninsula, along northern coast of South China Sea. The concentrations of the majority of elements show a weak declining trend with growth from 1982 to 1999, punctuated by several high concentration spikes. The declining trends are positively correlated with ring width and negatively correlated with inferred water-use efficiency, suggesting a physiological control over metal-uptake in this species. The episodic metal concentration-peaks cannot be interpreted with lateral movement or growth activities and appear to be related to environmental pollution events. Pb isotope ratios for most samples plot along the 'Chinese Pb line' and clearly document the importance of gasoline Pb as a source of contaminant. Shale-normalised REE + Y patterns are relatively flat and consistent across the growth period, with all patterns showing a positive Ce anomaly and elevated Y/Ho ratio. The positive Ce anomaly is observed regardless of the choice of normaliser, in contrast to previously reported REE patterns for terrestrial and marine plants. This pilot study of trace element, REE + Y and Pb isotope distribution in mangrove tree rings indicates the potential use of mangroves as monitors of historical environmental change.

  9. Light-mediated K(leaf) induction and contribution of both the PIP1s and PIP2s aquaporins in five tree species: walnut (Juglans regia) case study.

    PubMed

    Baaziz, Khaoula Ben; Lopez, David; Rabot, Amelie; Combes, Didier; Gousset, Aurelie; Bouzid, Sadok; Cochard, Herve; Sakr, Soulaiman; Venisse, Jean-Stephane

    2012-04-01

    Understanding the response of leaf hydraulic conductance (K(leaf)) to light is a challenge in elucidating plant-water relationships. Recent data have shown that the effect of light on K(leaf) is not systematically related to aquaporin regulation, leading to conflicting conclusions. Here we investigated the relationship between light, K(leaf), and aquaporin transcript levels in five tree species (Juglans regia L., Fagus sylvatica L., Quercus robur L., Salix alba L. and Populus tremula L.) grown in the same environmental conditions, but differing in their K(leaf) responses to light. Moreover, the K(leaf) was measured by two independent methods (high-pressure flow metre (HPFM) and evaporative flux method (EFM)) in the most (J. regia) and least (S. alba) responsive species and the transcript levels of aquaporins were analyzed in perfused and unperfused leaves. Here, we found that the light-induced K(leaf) value was closely related to stronger expression of both the PIP1 and PIP2 aquaporin genes in walnut (J. regia), but to stimulation of PIP1 aquaporins alone in F. sylvatica and Q. robur. In walnut, all newly identified aquaporins were found to be upregulated in the light and downregulated in the dark, further supporting the relationship between the light-mediated induction of K(leaf) and aquaporin expression in walnut. We also demonstrated that the K(leaf) response to light was quality-dependent, K(leaf) being 60% lower in the absence of blue light. This decrease in K(leaf) was correlated with strong downregulation of three PIP2 aquaporins and of all the PIP1 aquaporins tested. These data support a relationship between light-mediated K(leaf) regulation and the abundance of aquaporin transcripts in the walnut tree.

  10. Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

    NASA Astrophysics Data System (ADS)

    Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

    2016-04-01

    This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

  11. Hi-trees and their layout.

    PubMed

    Marriott, Kim; Sbarski, Peter; van Gelder, Tim; Prager, Daniel; Bulka, Andy

    2011-03-01

    We introduce hi-trees, a new visual representation for hierarchical data in which, depending on the kind of parent node, the child relationship is represented using either containment or links. We give a drawing convention for hi-trees based on the standard layered drawing convention for rooted trees, then show how to extend standard bottom-up tree layout algorithms to draw hi-trees in this convention. We also explore a number of other more compact layout styles for layout of larger hi-trees and give algorithms for computing these. Finally, we describe two applications of hi-trees: argument mapping and business decision support.

  12. Sampling Assumptions in Inductive Generalization

    ERIC Educational Resources Information Center

    Navarro, Daniel J.; Dry, Matthew J.; Lee, Michael D.

    2012-01-01

    Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key "sampling" assumption about how the available data were generated.…

  13. Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

    ERIC Educational Resources Information Center

    Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

    2011-01-01

    The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

  14. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

    PubMed

    Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

    2017-04-01

    Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for Rtraining(2) and Rtest(2), respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for Rtraining(2) and Rtest(2), respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (Rtraining(2) and Rtest(2) were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.

  15. Tree-Ring Based Climate Scenarios to Inform Decision Making in Water Resource Management: A Case Study From the Inland Empire, CA

    NASA Astrophysics Data System (ADS)

    Groves, D.; Tingstad, A.

    2009-12-01

    Water agencies in California are concerned about meeting future demand under climate conditions that are potentially drier than today. Tree-rings can be used to reconstruct past climate which features droughts that were longer and more severe than any dry period during the 20th century for which instrumental data used by water managers are available. This research developed a new technique for modifying tree-ring based precipitation and temperature sequences that can be widely used in water management applications. A pilot study using this new method was done for the Inland Empire Utilities Agency (IEUA) in Southern California. This work employed Robust Decisionmaking to identify near-term management actions that may help mitigate future water shortages over a wide range of uncertainties related to climate, planning, and costs. The results of this work suggest that the current IEUA management plan is vulnerable to extended, high-magnitude droughts in the paleoclimate record as well as situations where management goals are not met. Increasing water banking, recycling, groundwater replenishment, and efficiency in the near-term could reduce the risk of unmet demand and shortage costs in the future.

  16. Comparison of two inductive learning methods: A case study in failed fuel identification

    SciTech Connect

    Reifman, J.; Lee, J.C.

    1992-05-01

    Two inductive learning methods, the ID3 and Rg algorithms, are studied as a means for systematically and automatically constructing the knowledge base of expert systems. Both inductive learning methods are general-purpose and use information entropy as a discriminatory measure in order to group objects of a common class. ID3 constructs a knowledge base by building decision trees that discriminate objects of a data set as a function of their class. Rg constructs a knowledge base by grouping objects of the same class into patterns or clusters. The two inductive methods are applied to the construction of a knowledge base for failed fuel identification in the Experimental Breeder Reactor II. Through analysis of the knowledge bases generated, the ID3 and Rg algorithms are compared for their knowledge representation, data overfitting, feature space partition, feature selection, and search procedure.

  17. Comparison of two inductive learning methods: A case study in failed fuel identification

    SciTech Connect

    Reifman, J. ); Lee, J.C. . Dept. of Nuclear Engineering)

    1992-01-01

    Two inductive learning methods, the ID3 and Rg algorithms, are studied as a means for systematically and automatically constructing the knowledge base of expert systems. Both inductive learning methods are general-purpose and use information entropy as a discriminatory measure in order to group objects of a common class. ID3 constructs a knowledge base by building decision trees that discriminate objects of a data set as a function of their class. Rg constructs a knowledge base by grouping objects of the same class into patterns or clusters. The two inductive methods are applied to the construction of a knowledge base for failed fuel identification in the Experimental Breeder Reactor II. Through analysis of the knowledge bases generated, the ID3 and Rg algorithms are compared for their knowledge representation, data overfitting, feature space partition, feature selection, and search procedure.

  18. Data mining in psychological treatment research: a primer on classification and regression trees.

    PubMed

    King, Matthew W; Resick, Patricia A

    2014-10-01

    Data mining of treatment study results can reveal unforeseen but critical insights, such as who receives the most benefit from treatment and under what circumstances. The usefulness and legitimacy of exploratory data analysis have received relatively little recognition, however, and analytic methods well suited to the task are not widely known in psychology. With roots in computer science and statistics, statistical learning approaches offer a credible option: These methods take a more inductive approach to building a model than is done in traditional regression, allowing the data greater role in suggesting the correct relationships between variables rather than imposing them a priori. Classification and regression trees are presented as a powerful, flexible exemplar of statistical learning methods. Trees allow researchers to efficiently identify useful predictors of an outcome and discover interactions between predictors without the need to anticipate and specify these in advance, making them ideal for revealing patterns that inform hypotheses about treatment effects. Trees can also provide a predictive model for forecasting outcomes as an aid to clinical decision making. This primer describes how tree models are constructed, how the results are interpreted and evaluated, and how trees overcome some of the complexities of traditional regression. Examples are drawn from randomized clinical trial data and highlight some interpretations of particular interest to treatment researchers. The limitations of tree models are discussed, and suggestions for further reading and choices in software are offered.

  19. Talking Trees

    ERIC Educational Resources Information Center

    Tolman, Marvin

    2005-01-01

    Students love outdoor activities and will love them even more when they build confidence in their tree identification and measurement skills. Through these activities, students will learn to identify the major characteristics of trees and discover how the pace--a nonstandard measuring unit--can be used to estimate not only distances but also the…

  20. Gravitational induction

    NASA Astrophysics Data System (ADS)

    Bini, Donato; Cherubini, Christian; Chicone, Carmen; Mashhoon, Bahram

    2008-11-01

    We study the linear post-Newtonian approximation to general relativity known as gravitoelectromagnetism (GEM); in particular, we examine the similarities and differences between GEM and electrodynamics. Notwithstanding some significant differences between them, we find that a special nonstationary metric in GEM can be employed to show explicitly that it is possible to introduce gravitational induction within GEM in close analogy with Faraday's law of induction and Lenz's law in electrodynamics. Some of the physical implications of gravitational induction are briefly discussed.

  1. Inductive reasoning.

    PubMed

    Hayes, Brett K; Heit, Evan; Swendsen, Haruka

    2010-03-01

    Inductive reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empirical phenomena that involve taxonomic similarity are described. We also examine phenomena involving more complex background knowledge about premises and conclusions of inductive arguments and the properties referenced. Earlier models are shown to give a good account of similarity-based phenomena but not knowledge-based phenomena. Recent models that aim to account for both similarity-based and knowledge-based phenomena are reviewed and evaluated. Among the most important new directions in induction research are a focus on induction with uncertain premise categories, the modeling of the relationship between inductive and deductive reasoning, and examination of the neural substrates of induction. A common theme in both the well-established and emerging lines of induction research is the need to develop well-articulated and empirically testable formal models of induction. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

  2. China PEACE risk estimation tool for in-hospital death from acute myocardial infarction: an early risk classification tree for decisions about fibrinolytic therapy

    PubMed Central

    Li, Xi; Li, Jing; Masoudi, Frederick A; Spertus, John A; Lin, Zhenqiu; Krumholz, Harlan M; Jiang, Lixin

    2016-01-01

    Objectives As the predominant approach to acute reperfusion for ST segment elevation myocardial infarction (STEMI) in many countries, fibrinolytic therapy provides a relative risk reduction for death of ∼16% across the range of baseline risk. For patients with low baseline mortality risk, fibrinolytic therapy may therefore provide little benefit, which may be offset by the risk of major bleeding. We aimed to construct a tool to determine if it is possible to identify a low-risk group among fibrinolytic therapy-eligible patients. Design Cross-sectional study. Setting The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE) study includes a nationally representative retrospective sample of patients admitted with acute myocardial infarction (AMI) in 162 hospitals. Participants 3741 patients with STEMI who were fibrinolytic-eligible but did not receive reperfusion therapy. Main outcome measures In-hospital mortality, which was defined as a composite of death occurring within hospitalisation or withdrawal from treatment due to a terminal status at discharge. Results In the study cohort, the in-hospital mortality was 14.7%. In the derivation cohort and the validation cohort, the combination of systolic blood pressure (≥100 mm Hg), age (<60 years old) and gender (male) identified one-fifth of the cohort with an average mortality rate of <3.0%. Half of this low risk group—those with non-anterior AMI—had an average in-hospital death risk of 1.5%. Conclusions Nearly, one in five patients with STEMI who are eligible for fibrinolytic therapy are at a low risk for in-hospital death. Three simple factors available at the time of presentation can identify these individuals and support decision-making about the use of fibrinolytic therapy. Trial registration number NCT01624883. PMID:27798032

  3. Graphic Representations as Tools for Decision Making.

    ERIC Educational Resources Information Center

    Howard, Judith

    2001-01-01

    Focuses on the use of graphic representations to enable students to improve their decision making skills in the social studies. Explores three visual aids used in assisting students with decision making: (1) the force field; (2) the decision tree; and (3) the decision making grid. (CMK)

  4. Modeling of stage-discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi-Sugeno inference system technique: a comparative study

    NASA Astrophysics Data System (ADS)

    Al-Abadi, Alaa M.

    2016-11-01

    The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination ( R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.

  5. Induction voidmeter

    DOEpatents

    Anderson, Thomas T.; Roop, Conard J.; Schmidt, Kenneth J.; Brewer, John

    1986-01-01

    An induction voidmeter for detecting voids in a conductive fluid may comprise: a four arm bridge circuit having two adjustable circuit elements connected as opposite arms of said bridge circuit, an input branch, and an output branch; two induction coils, bifilarly wound together, connected as the remaining two opposing arms of said bridge circuit and positioned such that the conductive fluid passes through said coils; applying an AC excitation signal to said input branch; and detecting the output signal generated in response to said excitation signal across said output branch. The induction coils may be located outside or inside a non-magnetic pipe containing the conductive fluid.

  6. Induction voidmeter

    DOEpatents

    Anderson, T.T.; Roop, C.J.; Schmidt, K.J.; Brewer, J.

    1983-12-21

    An induction voidmeter for detecting voids in a conductive fluid may comprise: a four arm bridge circuit having two adjustable circuit elements connected as opposite arms of said bridge, an input branch, and an output branch; two induction coils, bifilarly wound together, connected as the remaining two opposing arms of said bridge circuit and positioned such that the conductive fluid passes through said coils; means for applying an AC excitation signal to said input branch; and means for detecting the output signal generated in response to said excitation signal across said output branch. The induction coils may be located outside or inside a non-magnetic pipe containing the conductive fluid.

  7. Structural regression trees

    SciTech Connect

    Kramer, S.

    1996-12-31

    In many real-world domains the task of machine learning algorithms is to learn a theory for predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with nondeterminate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems. SRT integrates the statistical method of regression trees into ILP. It constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.

  8. Audubon Tree Study Program.

    ERIC Educational Resources Information Center

    National Audubon Society, New York, NY.

    Included are an illustrated student reader, "The Story of Trees," a leaders' guide, and a large tree chart with 37 colored pictures. The student reader reviews several aspects of trees: a definition of a tree; where and how trees grow; flowers, pollination and seed production; how trees make their food; how to recognize trees; seasonal changes;…

  9. Machine Learning Through Signature Trees. Applications to Human Speech.

    ERIC Educational Resources Information Center

    White, George M.

    A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

  10. Tree harvesting

    SciTech Connect

    Badger, P.C.

    1995-12-31

    Short rotation intensive culture tree plantations have been a major part of biomass energy concepts since the beginning. One aspect receiving less attention than it deserves is harvesting. This article describes an method of harvesting somewhere between agricultural mowing machines and huge feller-bunchers of the pulpwood and lumber industries.

  11. Inductively coupled plasma-atomic emission spectrometer warning diagnosis procedure using blank solution data

    NASA Astrophysics Data System (ADS)

    Sartoros, Christine; Salin, Eric D.

    1998-05-01

    Lines available while running a blank solution were used to monitor the analytical performance of an inductively coupled plasma atomic emission spectrometry (ICP-AES) system in real time. Using H and Ar lines and their signal-to-background ratios (SBRs), simple rules in the form of a prediction table were developed by inspection of the data. These rules could be used for predicting changes in radio-frequency power, carrier gas flow rates, and sample introduction rate. The performance of the prediction table was good but not excellent. Another set of rules in the form of a decision tree was developed in an automated fashion using the C4.5 induction engine. The performance of the decision tree was superior to that of the prediction table. It appears that blank spectral information can be used to predict with over 90% accuracy when an ICP-AES is breaking down. However this is not as definitive at identifying the exact fault as some more exhaustive approaches involving the use of standard solutions.

  12. Using Bayesian Belief Networks and event trees for volcanic hazard assessment and decision support : reconstruction of past eruptions of La Soufrière volcano, Guadeloupe and retrospective analysis of 1975-77 unrest.

    NASA Astrophysics Data System (ADS)

    Komorowski, Jean-Christophe; Hincks, Thea; Sparks, Steve; Aspinall, Willy; Legendre, Yoann; Boudon, Georges

    2013-04-01

    the contemporary volcanological narrative, and demonstrates that a formal evidential case could have been made to support the authorities' concerns and decision to evacuate. Revisiting the circumstances of the 1976 crisis highlights many contemporary challenges of decision-making under conditions of volcanological uncertainty. We suggest the BBN concept is a suitable framework for marshalling multiple observations, model results and interpretations - and all associated uncertainties - in a methodical manner. Base-rate eruption probabilities for Guadeloupe can be updated now with a new chronology of activity suggesting that 10 major explosive phases and 9 dome-forming phases occurred in the last 9150 years, associated with ≥ 8 flank-collapses and ≥ 6-7 high-energy pyroclastic density currents (blasts). Eruptive recurrence, magnitude and intensity place quantitative constraints on La Soufrière's event tree to elaborate credible scenarios. The current unrest offers an opportunity to update the BBN model and explore the uncertainty on inferences about the system's internal state. This probabilistic formalism would provoke key questions relating to unrest evolution: 1) is the unrest hydrothermal or magmatic? 2) what controls dyke/intrusion arrest and hence failed-magmatic eruptions like 1976? 3) what conditions could lead to significant pressurization with potential for explosive activity and edifice instability, and what monitoring signs might be manifest?

  13. Technical Tree Climbing.

    ERIC Educational Resources Information Center

    Jenkins, Peter

    Tree climbing offers a safe, inexpensive adventure sport that can be performed almost anywhere. Using standard procedures practiced in tree surgery or rock climbing, almost any tree can be climbed. Tree climbing provides challenge and adventure as well as a vigorous upper-body workout. Tree Climbers International classifies trees using a system…

  14. Exotic trees.

    PubMed

    Burda, Z; Erdmann, J; Petersson, B; Wattenberg, M

    2003-02-01

    We discuss the scaling properties of free branched polymers. The scaling behavior of the model is classified by the Hausdorff dimensions for the internal geometry, d(L) and d(H), and for the external one, D(L) and D(H). The dimensions d(H) and D(H) characterize the behavior for long distances, while d(L) and D(L) for short distances. We show that the internal Hausdorff dimension is d(L)=2 for generic and scale-free trees, contrary to d(H), which is known to be equal to 2 for generic trees and to vary between 2 and infinity for scale-free trees. We show that the external Hausdorff dimension D(H) is directly related to the internal one as D(H)=alphad(H), where alpha is the stability index of the embedding weights for the nearest-vertex interactions. The index is alpha=2 for weights from the Gaussian domain of attraction and 0

  15. What Makes a Tree a Tree?

    ERIC Educational Resources Information Center

    NatureScope, 1986

    1986-01-01

    Provides: (1) background information on trees, focusing on the parts of trees and how they differ from other plants; (2) eight activities; and (3) ready-to-copy pages dealing with tree identification and tree rings. Activities include objective(s), recommended age level(s), subject area(s), list of materials needed, and procedures. (JN)

  16. Induction synchrotron

    NASA Astrophysics Data System (ADS)

    Takayama, Ken; Kishiro, Junichi

    2000-08-01

    A novel proton synchrotron employing induction cells instead of radio frequency cavities is proposed. The major feature of the barrier bucket acceleration, where acceleration and longitudinal focusing are independently achieved is theoretically discussed with the help of multi-particle simulations. It is proved that barrier bucket acceleration allows ultimate use of longitudinal phase-space and is quite effective to substantially increase the beam intensity in synchrotrons. Engineering aspects of key devices to realize the novel synchrotron, a ferri/ferro-magnetic material loaded induction cell and a modulator being rapidly switched in synchronization with beam acceleration are described in detail. The idea is applied to an existing machine (the KEK 12 GeV-PS) and high-intensity proton rings such as JHF, ESS, and SNS and their predicted improvement in machine performance is given with numerical values for each case.

  17. Induction machine

    DOEpatents

    Owen, Whitney H.

    1980-01-01

    A polyphase rotary induction machine for use as a motor or generator utilizing a single rotor assembly having two series connected sets of rotor windings, a first stator winding disposed around the first rotor winding and means for controlling the current induced in one set of the rotor windings compared to the current induced in the other set of the rotor windings. The rotor windings may be wound rotor windings or squirrel cage windings.

  18. The Tree Worker's Manual.

    ERIC Educational Resources Information Center

    Smithyman, S. J.

    This manual is designed to prepare students for entry-level positions as tree care professionals. Addressed in the individual chapters of the guide are the following topics: the tree service industry; clothing, eqiupment, and tools; tree workers; basic tree anatomy; techniques of pruning; procedures for climbing and working in the tree; aerial…

  19. Teaching the Tools of Pharmaceutical Care Decision-Analysis.

    ERIC Educational Resources Information Center

    Rittenhouse, Brian E.

    1994-01-01

    A method of decision-analysis in pharmaceutical care that integrates epidemiology and economics is presented, including an example illustrating both the deceptive nature of medical decision making and the power of decision analysis. Principles in determining both general and specific probabilities of interest and use of decision trees for…

  20. Additive Similarity Trees

    ERIC Educational Resources Information Center

    Sattath, Shmuel; Tversky, Amos

    1977-01-01

    Tree representations of similarity data are investigated. Hierarchical clustering is critically examined, and a more general procedure, called the additive tree, is presented. The additive tree representation is then compared to multidimensional scaling. (Author/JKS)

  1. Training Tree Transducers

    DTIC Science & Technology

    2004-01-01

    trees (similar to the role played by the finite- state acceptor FSA for strings). We describe the version (equivalent to TSG ( Schabes , 1990)) where...strictly contained in tree sets of tree adjoining gram- mars (Joshi and Schabes , 1997). 4 Extended-LHS Tree Transducers (xR) Section 1 informally described...changes without modifying the training procedure, as long as we stick to tree automata. 10 Related Work Tree substitution grammars or TSG ( Schabes , 1990

  2. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

  3. Theory of the decision/problem state

    NASA Technical Reports Server (NTRS)

    Dieterly, D. L.

    1980-01-01

    A theory of the decision-problem state was introduced and elaborated. Starting with the basic model of a decision-problem condition, an attempt was made to explain how a major decision-problem may consist of subsets of decision-problem conditions composing different condition sequences. In addition, the basic classical decision-tree model was modified to allow for the introduction of a series of characteristics that may be encountered in an analysis of a decision-problem state. The resulting hierarchical model reflects the unique attributes of the decision-problem state. The basic model of a decision-problem condition was used as a base to evolve a more complex model that is more representative of the decision-problem state and may be used to initiate research on decision-problem states.

  4. Decision making.

    PubMed

    Chambers, David W

    2011-01-01

    A decision is a commitment of resources under conditions of risk in expectation of the best future outcome. The smart decision is always the strategy with the best overall expected value-the best combination of facts and values. Some of the special circumstances involved in decision making are discussed, including decisions where there are multiple goals, those where more than one person is involved in making the decision, using trigger points, framing decisions correctly, commitments to lost causes, and expert decision makers. A complex example of deciding about removal of asymptomatic third molars, with and without an EBD search, is discussed.

  5. Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

    NASA Technical Reports Server (NTRS)

    Spikovska, Lilly; Iverson, David L.; Poll, Scott; Pryor, anna

    2005-01-01

    Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and

  6. Categorizing ideas about trees: a tree of trees.

    PubMed

    Fisler, Marie; Lecointre, Guillaume

    2013-01-01

    The aim of this study is to explore whether matrices and MP trees used to produce systematic categories of organisms could be useful to produce categories of ideas in history of science. We study the history of the use of trees in systematics to represent the diversity of life from 1766 to 1991. We apply to those ideas a method inspired from coding homologous parts of organisms. We discretize conceptual parts of ideas, writings and drawings about trees contained in 41 main writings; we detect shared parts among authors and code them into a 91-characters matrix and use a tree representation to show who shares what with whom. In other words, we propose a hierarchical representation of the shared ideas about trees among authors: this produces a "tree of trees." Then, we categorize schools of tree-representations. Classical schools like "cladists" and "pheneticists" are recovered but others are not: "gradists" are separated into two blocks, one of them being called here "grade theoreticians." We propose new interesting categories like the "buffonian school," the "metaphoricians," and those using "strictly genealogical classifications." We consider that networks are not useful to represent shared ideas at the present step of the study. A cladogram is made for showing who is sharing what with whom, but also heterobathmy and homoplasy of characters. The present cladogram is not modelling processes of transmission of ideas about trees, and here it is mostly used to test for proximity of ideas of the same age and for categorization.

  7. Tree Tectonics

    NASA Astrophysics Data System (ADS)

    Vogt, Peter R.

    2004-09-01

    Nature often replicates her processes at different scales of space and time in differing media. Here a tree-trunk cross section I am preparing for a dendrochronological display at the Battle Creek Cypress Swamp Nature Sanctuary (Calvert County, Maryland) dried and cracked in a way that replicates practically all the planform features found along the Mid-Oceanic Ridge (see Figure 1). The left-lateral offset of saw marks, contrasting with the right-lateral ``rift'' offset, even illustrates the distinction between transcurrent (strike-slip) and transform faults, the latter only recognized as a geologic feature, by J. Tuzo Wilson, in 1965. However, wood cracking is but one of many examples of natural processes that replicate one or several elements of lithospheric plate tectonics. Many of these examples occur in everyday venues and thus make great teaching aids, ``teachable'' from primary school to university levels. Plate tectonics, the dominant process of Earth geology, also occurs in miniature on the surface of some lava lakes, and as ``ice plate tectonics'' on our frozen seas and lakes. Ice tectonics also happens at larger spatial and temporal scales on the Jovian moons Europa and perhaps Ganymede. Tabletop plate tectonics, in which a molten-paraffin ``asthenosphere'' is surfaced by a skin of congealing wax ``plates,'' first replicated Mid-Oceanic Ridge type seafloor spreading more than three decades ago. A seismologist (J. Brune, personal communication, 2004) discovered wax plate tectonics by casually and serendipitously pulling a stick across a container of molten wax his wife and daughters had used in making candles. Brune and his student D. Oldenburg followed up and mirabile dictu published the results in Science (178, 301-304).

  8. The Needs of Trees

    ERIC Educational Resources Information Center

    Boyd, Amy E.; Cooper, Jim

    2004-01-01

    Tree rings can be used not only to look at plant growth, but also to make connections between plant growth and resource availability. In this lesson, students in 2nd-4th grades use role-play to become familiar with basic requirements of trees and how availability of those resources is related to tree ring sizes and tree growth. These concepts can…

  9. A dynamic fault tree model of a propulsion system

    NASA Technical Reports Server (NTRS)

    Xu, Hong; Dugan, Joanne Bechta; Meshkat, Leila

    2006-01-01

    We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

  10. Decision analysis: a primer and application to pain-related studies.

    PubMed

    Kim, Jaewhan; Nelson, Richard; Biskupiak, Joseph

    2008-01-01

    Decision analysis is a quantitative approach to decision making under uncertainty that explicitly states all relevant components of the decision, including statement of the problem, identification of the perspective of the decision maker, alternative courses of action and their consequences, and a model that illustrates the decision-making process. Decision trees and Markov models are used to provide a simplified version of complex clinical problems to help decision makers understand the risks and benefits of several clinical options. This article provides an introduction to decision analysis by describing the construction of decision trees and Markov models and employing examples from the recent literature.

  11. GumTree: Data reduction

    NASA Astrophysics Data System (ADS)

    Rayner, Hugh; Hathaway, Paul; Hauser, Nick; Fei, Yang; Franceschini, Ferdi; Lam, Tony

    2006-11-01

    Access to software tools for interactive data reduction, visualisation and analysis during a neutron scattering experiment enables instrument users to make informed decisions regarding the direction and success of their experiment. ANSTO aims to enhance the experiment experience of its facility's users by integrating these data reduction tools with the instrument control interface for immediate feedback. GumTree is a software framework and application designed to support an Integrated Scientific Experimental Environment, for concurrent access to instrument control, data acquisition, visualisation and analysis software. The Data Reduction and Analysis (DRA) module is a component of the GumTree framework that allows users to perform data reduction, correction and basic analysis within GumTree while an experiment is running. It is highly integrated with GumTree, able to pull experiment data and metadata directly from the instrument control and data acquisition components. The DRA itself uses components common to all instruments at the facility, providing a consistent interface. It features familiar ISAW-based 1D and 2D plotting, an OpenGL-based 3D plotter and peak fitting performed by fityk. This paper covers the benefits of integration, the flexibility of the DRA module, ease of use for the interface and audit trail generation.

  12. Tea tree oil.

    PubMed

    Hartford, Orville; Zug, Kathryn A

    2005-09-01

    Tea tree oil is a popular ingredient in many over-the-counter healthcare and cosmetic products. With the explosion of the natural and alternative medicine industry, more and more people are using products containing tea tree oil. This article reviews basic information about tea tree oil and contact allergy, including sources of tea tree oil, chemical composition, potential cross reactions, reported cases of allergic contact dermatitis, allergenic compounds in tea tree oil, practical patch testing information, and preventive measures.

  13. Photoperiodic growth control in perennial trees.

    PubMed

    Azeez, Abdul; Sane, Aniruddha P

    2015-01-01

    Plants have to cope with changing seasons and adverse environmental conditions. Being sessile, plants have developed elaborate mechanisms for their survival that allow them to sense and adapt to the environment and reproduce successfully. A major adaptive trait for the survival of trees of temperate and boreal forests is the induction of growth cessation in anticipation of winters. In the last few years enormous progress has been made to elucidate the molecular mechanisms underlying SDs induced growth cessation in model perennial tree hybrid aspen (Populus tremula × P. tremuloides). In this review we discuss the molecular mechanism underlying photoperiodic control of growth cessation and adaptive responses.

  14. Photoperiodic growth control in perennial trees

    PubMed Central

    Azeez, Abdul; Sane, Aniruddha P

    2015-01-01

    Plants have to cope with changing seasons and adverse environmental conditions. Being sessile, plants have developed elaborate mechanisms for their survival that allow them to sense and adapt to the environment and reproduce successfully. A major adaptive trait for the survival of trees of temperate and boreal forests is the induction of growth cessation in anticipation of winters. In the last few years enormous progress has been made to elucidate the molecular mechanisms underlying SDs induced growth cessation in model perennial tree hybrid aspen (Populus tremula × P. tremuloides). In this review we discuss the molecular mechanism underlying photoperiodic control of growth cessation and adaptive responses. PMID:26340077

  15. Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.

    PubMed

    Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

    2015-02-01

    Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can

  16. Decision technology.

    PubMed

    Edwards, W; Fasolo, B

    2001-01-01

    This review is about decision technology-the rules and tools that help us make wiser decisions. First, we review the three rules that are at the heart of most traditional decision technology-multi-attribute utility, Bayes' theorem, and subjective expected utility maximization. Since the inception of decision research, these rules have prescribed how we should infer values and probabilities and how we should combine them to make better decisions. We suggest how to make best use of all three rules in a comprehensive 19-step model. The remainder of the review explores recently developed tools of decision technology. It examines the characteristics and problems of decision-facilitating sites on the World Wide Web. Such sites now provide anyone who can use a personal computer with access to very sophisticated decision-aiding tools structured mainly to facilitate consumer decision making. It seems likely that the Web will be the mode by means of which decision tools will be distributed to lay users. But methods for doing such apparently simple things as winnowing 3000 options down to a more reasonable number, like 10, contain traps for unwary decision technologists. The review briefly examines Bayes nets and influence diagrams-judgment and decision-making tools that are available as computer programs. It very briefly summarizes the state of the art of eliciting probabilities from experts. It concludes that decision tools will be as important in the 21st century as spreadsheets were in the 20th.

  17. Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality

    PubMed Central

    Vergara, Pablo M.; Soto, Gerardo E.; Rodewald, Amanda D.; Meneses, Luis O.; Pérez-Hernández, Christian G.

    2016-01-01

    Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox’s proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales. PMID:27416115

  18. Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality.

    PubMed

    Vergara, Pablo M; Soto, Gerardo E; Moreira-Arce, Darío; Rodewald, Amanda D; Meneses, Luis O; Pérez-Hernández, Christian G

    2016-01-01

    Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox's proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales.

  19. GM trees with increased resistance to herbivores: trait efficiency and their potential to promote tree growth.

    PubMed

    Hjältén, Joakim; Axelsson, E Petter

    2015-01-01

    Climate change, as well as a more intensive forestry, is expected to increase the risk of damage by pests and pathogens on trees, which can already be a severe problem in tree plantations. Recent development of biotechnology theoretically allows for resistance enhancement that could help reduce these risks but we still lack a comprehensive understanding of benefits and tradeoffs with pest resistant GM (genetically modified) trees. We synthesized the current knowledge on the effectiveness of GM forest trees with increased resistance to herbivores. There is ample evidence that induction of exogenous Bacillus thuringiensis genes reduce performance of target pests whereas upregulation of endogenous resistance traits e.g., phenolics, generates variable results. Our review identified very few studies estimating the realized benefits in tree growth of GM trees in the field. This is concerning as the realized benefit with insect resistant GM plants seems to be context-dependent and likely manifested only if herbivore pressure is sufficiently high. Future studies of secondary pest species and resistance evolution in pest to GM trees should be prioritized. But most importantly we need more long-term field tests to evaluate the benefits and risks with pest resistant GM trees.

  20. GM trees with increased resistance to herbivores: trait efficiency and their potential to promote tree growth

    PubMed Central

    Hjältén, Joakim; Axelsson, E. Petter

    2015-01-01

    Climate change, as well as a more intensive forestry, is expected to increase the risk of damage by pests and pathogens on trees, which can already be a severe problem in tree plantations. Recent development of biotechnology theoretically allows for resistance enhancement that could help reduce these risks but we still lack a comprehensive understanding of benefits and tradeoffs with pest resistant GM (genetically modified) trees. We synthesized the current knowledge on the effectiveness of GM forest trees with increased resistance to herbivores. There is ample evidence that induction of exogenous Bacillus thuringiensis genes reduce performance of target pests whereas upregulation of endogenous resistance traits e.g., phenolics, generates variable results. Our review identified very few studies estimating the realized benefits in tree growth of GM trees in the field. This is concerning as the realized benefit with insect resistant GM plants seems to be context-dependent and likely manifested only if herbivore pressure is sufficiently high. Future studies of secondary pest species and resistance evolution in pest to GM trees should be prioritized. But most importantly we need more long-term field tests to evaluate the benefits and risks with pest resistant GM trees. PMID:25983736

  1. Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

    NASA Astrophysics Data System (ADS)

    Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

    A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

  2. Fault-Tree Compiler

    NASA Technical Reports Server (NTRS)

    Butler, Ricky W.; Boerschlein, David P.

    1993-01-01

    Fault-Tree Compiler (FTC) program, is software tool used to calculate probability of top event in fault tree. Gates of five different types allowed in fault tree: AND, OR, EXCLUSIVE OR, INVERT, and M OF N. High-level input language easy to understand and use. In addition, program supports hierarchical fault-tree definition feature, which simplifies tree-description process and reduces execution time. Set of programs created forming basis for reliability-analysis workstation: SURE, ASSIST, PAWS/STEM, and FTC fault-tree tool (LAR-14586). Written in PASCAL, ANSI-compliant C language, and FORTRAN 77. Other versions available upon request.

  3. Categorizing Ideas about Trees: A Tree of Trees

    PubMed Central

    Fisler, Marie; Lecointre, Guillaume

    2013-01-01

    The aim of this study is to explore whether matrices and MP trees used to produce systematic categories of organisms could be useful to produce categories of ideas in history of science. We study the history of the use of trees in systematics to represent the diversity of life from 1766 to 1991. We apply to those ideas a method inspired from coding homologous parts of organisms. We discretize conceptual parts of ideas, writings and drawings about trees contained in 41 main writings; we detect shared parts among authors and code them into a 91-characters matrix and use a tree representation to show who shares what with whom. In other words, we propose a hierarchical representation of the shared ideas about trees among authors: this produces a “tree of trees.” Then, we categorize schools of tree-representations. Classical schools like “cladists” and “pheneticists” are recovered but others are not: “gradists” are separated into two blocks, one of them being called here “grade theoreticians.” We propose new interesting categories like the “buffonian school,” the “metaphoricians,” and those using “strictly genealogical classifications.” We consider that networks are not useful to represent shared ideas at the present step of the study. A cladogram is made for showing who is sharing what with whom, but also heterobathmy and homoplasy of characters. The present cladogram is not modelling processes of transmission of ideas about trees, and here it is mostly used to test for proximity of ideas of the same age and for categorization. PMID:23950877

  4. Induction of labor: update and review.

    PubMed

    Bonsack, Carrie F; Lathrop, Anthony; Blackburn, Mary

    2014-01-01

    Rates of labor induction in the United States have more than doubled in the past 2 decades. Trends of indications and risk factors related to induction of labor are also increasing. Professional organizations such as the American College of Obstetricians and Gynecologists and The Joint Commission have taken steps to discourage elective induction of labor prior to 39 weeks' gestation and have created new definitions of early-term, full-term, late-term, and postterm gestation to guide clinicians in the timing of birth for specified indications. Induction of labor is associated with potential risks to both the woman and her fetus. The cost associated with induction of labor and the impact on the health care system is of growing concern. Education of women and the process of shared decision making when obtaining informed consent are key factors in reducing early elective births. Use of tools such as scheduling forms, hard stop methods, induction of labor indication tools, and informed consents may aid the provider in reducing overdiagnosis, overtreatment, and disease creep. This article provides a review of the trends of induction of labor, medical indications and criteria, associated risks, cost and health system impact, and initiatives to lower the incidence of induction of labor.

  5. Evolution of tree nutrition.

    PubMed

    Raven, John A; Andrews, Mitchell

    2010-09-01

    Using a broad definition of trees, the evolutionary origins of trees in a nutritional context is considered using data from the fossil record and molecular phylogeny. Trees are first known from the Late Devonian about 380 million years ago, originated polyphyletically at the pteridophyte grade of organization; the earliest gymnosperms were trees, and trees are polyphyletic in the angiosperms. Nutrient transporters, assimilatory pathways, homoiohydry (cuticle, intercellular gas spaces, stomata, endohydric water transport systems including xylem and phloem-like tissue) and arbuscular mycorrhizas preceded the origin of trees. Nutritional innovations that began uniquely in trees were the seed habit and, certainly (but not necessarily uniquely) in trees, ectomycorrhizas, cyanobacterial, actinorhizal and rhizobial (Parasponia, some legumes) diazotrophic symbioses and cluster roots.

  6. Tree Classification Software

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1993-01-01

    This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

  7. Illumination Under Trees

    SciTech Connect

    Max, N

    2002-08-19

    This paper is a survey of the author's work on illumination and shadows under trees, including the effects of sky illumination, sun penumbras, scattering in a misty atmosphere below the trees, and multiple scattering and transmission between leaves. It also describes a hierarchical image-based rendering method for trees.

  8. The Wish Tree Project

    ERIC Educational Resources Information Center

    Brooks, Sarah DeWitt

    2010-01-01

    This article describes the author's experience in implementing a Wish Tree project in her school in an effort to bring the school community together with a positive art-making experience during a potentially stressful time. The concept of a wish tree is simple: plant a tree; provide tags and pencils for writing wishes; and encourage everyone to…

  9. Diary of a Tree.

    ERIC Educational Resources Information Center

    Srulowitz, Frances

    1992-01-01

    Describes an activity to develop students' skills of observation and recordkeeping by studying the growth of a tree's leaves during the spring. Children monitor the growth of 11 tress over a 2-month period, draw pictures of the tree at different stages of growth, and write diaries of the tree's growth. (MDH)

  10. Minnesota's Forest Trees. Revised.

    ERIC Educational Resources Information Center

    Miles, William R.; Fuller, Bruce L.

    This bulletin describes 46 of the more common trees found in Minnesota's forests and windbreaks. The bulletin contains two tree keys, a summer key and a winter key, to help the reader identify these trees. Besides the two keys, the bulletin includes an introduction, instructions for key use, illustrations of leaf characteristics and twig…

  11. Winter Birch Trees

    ERIC Educational Resources Information Center

    Sweeney, Debra; Rounds, Judy

    2011-01-01

    Trees are great inspiration for artists. Many art teachers find themselves inspired and maybe somewhat obsessed with the natural beauty and elegance of the lofty tree, and how it changes through the seasons. One such tree that grows in several regions and always looks magnificent, regardless of the time of year, is the birch. In this article, the…

  12. Organizational Decisions.

    DTIC Science & Technology

    1982-03-01

    240 finance departments of county, city and state governments’ promotion decisions, Halabv (1976) obtained evidence that the analysis of the decision...oper- ations research techniques and practicing finance managers avoid complex mathematical models in favor of a few simple rules in investment decision...likely its managers to spend time with outside organizations. Similarly, organizations that depend on outside financing select more outside members

  13. Distributed Contour Trees

    SciTech Connect

    Morozov, Dmitriy; Weber, Gunther H.

    2014-03-31

    Topological techniques provide robust tools for data analysis. They are used, for example, for feature extraction, for data de-noising, and for comparison of data sets. This chapter concerns contour trees, a topological descriptor that records the connectivity of the isosurfaces of scalar functions. These trees are fundamental to analysis and visualization of physical phenomena modeled by real-valued measurements. We study the parallel analysis of contour trees. After describing a particular representation of a contour tree, called local{global representation, we illustrate how di erent problems that rely on contour trees can be solved in parallel with minimal communication.

  14. Growth of a Pine Tree

    ERIC Educational Resources Information Center

    Rollinson, Susan Wells

    2012-01-01

    The growth of a pine tree is examined by preparing "tree cookies" (cross-sectional disks) between whorls of branches. The use of Christmas trees allows the tree cookies to be obtained with inexpensive, commonly available tools. Students use the tree cookies to investigate the annual growth of the tree and how it corresponds to the number of whorls…

  15. How tree roots respond to drought

    PubMed Central

    Brunner, Ivano; Herzog, Claude; Dawes, Melissa A.; Arend, Matthias; Sperisen, Christoph

    2015-01-01

    The ongoing climate change is characterized by increased temperatures and altered precipitation patterns. In addition, there has been an increase in both the frequency and intensity of extreme climatic events such as drought. Episodes of drought induce a series of interconnected effects, all of which have the potential to alter the carbon balance of forest ecosystems profoundly at different scales of plant organization and ecosystem functioning. During recent years, considerable progress has been made in the understanding of how aboveground parts of trees respond to drought and how these responses affect carbon assimilation. In contrast, processes of belowground parts are relatively underrepresented in research on climate change. In this review, we describe current knowledge about responses of tree roots to drought. Tree roots are capable of responding to drought through a variety of strategies that enable them to avoid and tolerate stress. Responses include root biomass adjustments, anatomical alterations, and physiological acclimations. The molecular mechanisms underlying these responses are characterized to some extent, and involve stress signaling and the induction of numerous genes, leading to the activation of tolerance pathways. In addition, mycorrhizas seem to play important protective roles. The current knowledge compiled in this review supports the view that tree roots are well equipped to withstand drought situations and maintain morphological and physiological functions as long as possible. Further, the reviewed literature demonstrates the important role of tree roots in the functioning of forest ecosystems and highlights the need for more research in this emerging field. PMID:26284083

  16. Electromagnetic induction methods

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Electromagnetic induction geophysical methods are finding greater and greater use for agricultural purposes. Electromagnetic induction methods measure the electrical conductivity (or resistivity) for a bulk volume of soil directly beneath the surface. An instrument called a ground conductivity meter...

  17. Inductive Reasoning and Writing

    ERIC Educational Resources Information Center

    Rooks, Clay; Boyd, Robert

    2003-01-01

    Induction, properly understood, is not merely a game, nor is it a gimmick, nor is it an artificial way of explaining an element of reasoning. Proper understanding of inductive reasoning--and the various types of reasoning that the authors term inductive--enables the student to evaluate critically other people's writing and enhances the composition…

  18. Teaching Arrangement Inductively.

    ERIC Educational Resources Information Center

    Mendelson, Michael

    1988-01-01

    Argues that teaching arrangement inductively offers an alternative to the standard imitation of business communication text models. Asserts that the inductive method stimulates individual rather than formulaic responses to the problems of organization, and that inductively-trained writers see arrangements as a powerful element in persuasive…

  19. Forward estimation for game-tree search

    SciTech Connect

    Zhang, Weixiong

    1996-12-31

    It is known that bounds on the minimax values of nodes in a game tree can be used to reduce the computational complexity of minimax search for two-player games. We describe a very simple method to estimate bounds on the minimax values of interior nodes of a game tree, and use the bounds to improve minimax search. The new algorithm, called forward estimation, does not require additional domain knowledge other than a static node evaluation function, and has small constant overhead per node expansion. We also propose a variation of forward estimation, which provides a tradeoff between computational complexity and decision quality. Our experimental results show that forward estimation outperforms alpha-beta pruning on random game trees and the game of Othello.

  20. The effect of high temperature interruptions during inductive period on the extent of flowering and on metabolic responses in olives (Olea europaea L.)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The effect of the duration of high temperature interruption and the timing of it’s occurrence during inductive period on the extent of inhibition of inflorescence production in ‘Arbequina’ olive trees was investigated. Trees kept under inductive conditions in different growth chambers were subjected...

  1. Fragmentation of random trees

    NASA Astrophysics Data System (ADS)

    Kalay, Ziya; Ben-Naim, Eli

    2015-03-01

    We investigate the fragmentation of a random recursive tree by repeated removal of nodes, resulting in a forest of disjoint trees. The initial tree is generated by sequentially attaching new nodes to randomly chosen existing nodes until the tree contains N nodes. As nodes are removed, one at a time, the tree dissolves into an ensemble of separate trees, namely a forest. We study the statistical properties of trees and nodes in this heterogeneous forest. In the limit N --> ∞ , we find that the system is characterized by a single parameter: the fraction of remaining nodes m. We obtain analytically the size density ϕs of trees of size s, which has a power-law tail ϕs ~s-α , with exponent α = 1 + 1 / m . Therefore, the tail becomes steeper as further nodes are removed, producing an unusual scaling exponent that increases continuously with time. Furthermore, we investigate the fragment size distribution in a growing tree, where nodes are added as well as removed, and find that the distribution for this case is much narrower.

  2. arb_tree_32

    SciTech Connect

    Bavykin, Sergey; Alferov, Oleg

    2006-08-01

    The purpose of this program is to generate probes specific for the group of sequences that belong to a given phylogenetic node. For each node of the input tree, this program selects probes that are positive for all sequences that belong to this node and negative for all that doesn't. The program uses condensed tree for probe representation to save computer memory. As a result of calculation, the program prints lists for each node from the tree. Input file formats: FASTA for sequence database and ARB tree for phylogenetic organization of nodes. Output file format: text file.

  3. Pulsed IR inductive lasers

    NASA Astrophysics Data System (ADS)

    Razhev, A. M.; Churkin, D. S.; Kargapol'tsev, E. S.

    2014-07-01

    Pulsed inductive discharge is a new alternative method of pumping active gas laser media. The work presents results of experimental investigations of near, mid, and far IR inductive gas lasers (H2, HF, and CO2) operating at different transitions of atoms and molecules with different mechanisms of formation of inversion population. The excitation systems of a pulsed inductive cylindrical discharge (pulsed inductively coupled plasma) and pulsed RF inductive discharge in the gases are developed. Various gas mixtures including H2, N2, He, Ne, F2, NF3, and SF6 are used. Characteristics of near IR H2 laser radiation are investigated. Maximal pulse peak power of 7 kW is achieved. The possibility of using a pulsed inductive discharge as a new method of pumping HF laser active medium is demonstrated. The pulsed RF inductive CO2 laser is created and a total efficiency of 17% is achieved.

  4. Barriers to and facilitators of implementing shared decision making and decision support in a paediatric hospital: A descriptive study

    PubMed Central

    Boland, Laura; McIsaac, Daniel I; Lawson, Margaret L

    2016-01-01

    OBJECTIVE: To explore multiple stakeholders’ perceived barriers to and facilitators of implementing shared decision making and decision support in a tertiary paediatric hospital. METHODS: An interpretive descriptive qualitative study was conducted using focus groups and interviews to examine senior hospital administrators’, clinicians’, parents’ and youths’ perceived barriers to and facilitators of shared decision making and decision support implementation. Data were analyzed using inductive thematic analysis. RESULTS: Fifty-seven stakeholders participated. Six barrier and facilitator themes emerged. The main barrier was gaps in stakeholders’ knowledge of shared decision making and decision support. Facilitators included compatibility between shared decision making and the hospital’s culture and ideal practices, perceptions of positive patient and family outcomes associated with shared decision making, and positive attitudes regarding shared decision making and decision support. However, youth attitudes regarding the necessity and usefulness of a decision support program were a barrier. Two themes were both a barrier and a facilitator. First, stakeholder groups were uncertain which clinical situations are suitable for shared decision making (eg, new diagnoses, chronic illnesses, complex decisions or urgent decisions). Second, the clinical process may be hindered if shared decision making and decision support decrease efficiency and workflow; however, shared decision making may reduce repeat visits and save time over the long term. CONCLUSIONS: Specific knowledge translation strategies that improve shared decision making knowledge and match specific barriers identified by each stakeholder group may be required to promote successful shared decision making and decision support implementation in the authors’ paediatric hospital. PMID:27398058

  5. Knowledge, expectations, and inductive reasoning within conceptual hierarchies.

    PubMed

    Coley, John D; Hayes, Brett; Lawson, Christopher; Moloney, Michelle

    2004-01-01

    Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the "basic" level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hierarchical levels, expectations about conceptual coherence, and inductive inference. In Experiments 1 and 2, 5- and 8-year-olds and adults listed features of living kind (Experiments 1 and 2) and artifact (Experiment 2) concepts at different hierarchical levels (e.g. plant, tree, oak, desert oak), and also rated the strength of generalizations to the same concepts. For living kinds, the level that showed a relative advantage on these two tasks differed; the greatest increase in features listed tended to occur at the life-form level (e.g. tree), whereas the greatest increase in inductive strength tended to occur at the folk-generic level (e.g. oak). Knowledge and induction also showed different developmental trajectories. For artifact concepts, the levels at which the greatest gains in knowledge and induction occurred were more varied, and corresponded more closely across tasks. In Experiment 3, adults reported beliefs about within-category similarity for concepts at different levels of animal, plant and artifact hierarchies, and rated inductive strength as before. For living kind concepts, expectations about category coherence predicted patterns of inductions; knowledge did not. For artifact concepts, both knowledge and expectations predicted patterns of induction. Results suggest that beliefs about conceptual coherence play an important role in guiding inductive inference, that this role may be largely independent of specific knowledge of concepts, and that such beliefs are especially important in reasoning about living kinds.

  6. Tree nut oils

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The major tree nuts include almonds, Brazil nuts, cashew nuts, hazelnuts, macadamia nuts, pecans, pine nuts, pistachio nuts, and walnuts. Tree nut oils are appreciated in food applications because of their flavors and are generally more expensive than other gourmet oils. Research during the last de...

  7. CSI for Trees

    ERIC Educational Resources Information Center

    Rubino, Darrin L.; Hanson, Deborah

    2009-01-01

    The circles and patterns in a tree's stem tell a story, but that story can be a mystery. Interpreting the story of tree rings provides a way to heighten the natural curiosity of students and help them gain insight into the interaction of elements in the environment. It also represents a wonderful opportunity to incorporate the nature of science.…

  8. The Flame Tree

    ERIC Educational Resources Information Center

    Lewis, Richard

    2004-01-01

    Lewis's own experiences living in Indonesia are fertile ground for telling "a ripping good story," one found in "The Flame Tree." He hopes people will enjoy the tale and appreciate the differences of an unfamiliar culture. The excerpt from "The Flame Tree" will reel readers in quickly.

  9. Fragmentation of random trees

    NASA Astrophysics Data System (ADS)

    Kalay, Z.; Ben-Naim, E.

    2015-01-01

    We study fragmentation of a random recursive tree into a forest by repeated removal of nodes. The initial tree consists of N nodes and it is generated by sequential addition of nodes with each new node attaching to a randomly-selected existing node. As nodes are removed from the tree, one at a time, the tree dissolves into an ensemble of separate trees, namely, a forest. We study statistical properties of trees and nodes in this heterogeneous forest, and find that the fraction of remaining nodes m characterizes the system in the limit N\\to ∞ . We obtain analytically the size density {{φ }s} of trees of size s. The size density has power-law tail {{φ }s}˜ {{s}-α } with exponent α =1+\\frac{1}{m}. Therefore, the tail becomes steeper as further nodes are removed, and the fragmentation process is unusual in that exponent α increases continuously with time. We also extend our analysis to the case where nodes are added as well as removed, and obtain the asymptotic size density for growing trees.

  10. Trees for Mother Earth.

    ERIC Educational Resources Information Center

    Greer, Sandy

    1993-01-01

    Describes Trees for Mother Earth, a program in which secondary students raise funds to buy fruit trees to plant during visits to the Navajo Reservation. Benefits include developing feelings of self-worth among participants, promoting cultural exchange and understanding, and encouraging self-sufficiency among the Navajo. (LP)

  11. Reclamation: what about trees

    SciTech Connect

    Kolar, C.A.; Ashby, W.C.

    1982-07-01

    A five-year research programme was started in 1978 in the Botany Department of Southern Illinois University to evaluate the effect of reclamation practices on tree survival and growth. The project was initiated as a direct result of reports from Illinois and Indiana of tree-planting failures on mined lands reclaimed to current regulation standards.

  12. Trees in Our Lives.

    ERIC Educational Resources Information Center

    NatureScope, 1986

    1986-01-01

    Provides: (1) background information on how trees have influenced human history and how trees affect people today; (2) four activities dealing with these topics; and (3) a ready-to-copy page related to paper and plastics. Activities include an objective, recommended age level(s), subject area(s), list of materials needed, and procedures. (JN)

  13. Dependency Tree Annotation Software

    DTIC Science & Technology

    2015-11-01

    Features 5 Distribution List 12 iv List of Figures Fig. 1 Manually created dependency tree for the sentence, “The little cat ate the pie...with a dependency relation label. Fig. 1 Manually created dependency tree for the sentence, “The little cat ate the pie” The user can easily

  14. Project Learning Tree (Corporate Propaganda Tree).

    ERIC Educational Resources Information Center

    Mayer, Mike

    This document contains a critical analysis of Project Learning Tree (PLT). PLT was developed and distributed in the mid-1970s. It consists of 2 activity guides, one for grades K-6 with 89 activities and another for grades 7-12 with 88 activities. The program also provides free workshops for teachers and others. The analysis of PLT includes the…

  15. Fault tree analysis for maintenance needs

    NASA Astrophysics Data System (ADS)

    Halme, Jari; Aikala, Antti

    2012-05-01

    One of the key issues in maintenance is to allocate focus and resources to those components and subsystems which are the most unreliable and prone to failures. In industrial systems, fault tree analysis technique can be used to study the reliability of the complex systems and their substructures. In this paper a fault tree application for analyzing online the current reliability and failure probability for maintenance purposes is presented. The analysis is utilizing data connected to the fault tree root causes and events. An indication of an anomaly case, service action, cumulative loading, etc., or just time passed or service hour counter level can trigger a new calculation of current probabilities of the fault tree events and subsystem interactions. In proposed approach real time, dynamic information from several available data sources and different measurement are interconnected to each fault tree event and root cause. There is also formulated an active, constantly updated link between the fault tree events and maintenance databases for the maintenance decision support, and to keep the analysis up to date. Typically top event probability is evaluated based on updated root cause probabilities and lower level events. At the industrial plant level an identification of a failure in a component event defined within a constructed and operatively existing fault tree explicitly means that the event's failure probability is one. By utilizing this indication, the most probable failure branches through the fault tree sub events to root causes can be identified and printed as a valid check list for maintenance purposes to focus service actions first to those fault tree branches most probable causing the failure. Respectively, during the checks, service actions, etc., components, especially those within the critical branches, detected as healthy can be a updated as having zero failure probability. This information can be used to further update the fault tree and produce

  16. Whole-tree agarwood-inducing technique: an efficient novel technique for producing high-quality agarwood in cultivated Aquilaria sinensis trees.

    PubMed

    Liu, Yangyang; Chen, Huaiqiong; Yang, Yun; Zhang, Zheng; Wei, Jianhe; Meng, Hui; Chen, Weiping; Feng, Jindong; Gan, Bingchun; Chen, Xuyu; Gao, Zhihui; Huang, Junqin; Chen, Bo; Chen, Hongjiang

    2013-03-07

    Agarwood is the fragrant resin-infused wood derived from the wounded trees of Aquilaria species. It is a valuable non-timber forest product used in fragrances and as medicine. Reforestation for Aquilaria trees in combination with artificial agarwood-inducing methods serves as a way to supply agarwood and conserve of wild Aquilaria stock. However, the existing agarwood-inducing methods produce poor-quality agarwood at low yield. Our study evaluated a novel technique for producing agarwood in cultivated Aquilaria trees, called the whole-tree agarwood-inducing technique (Agar-Wit). Ten different agarwood inducers were used for comparison of Agar-Wit with three existing agarwood-inducing methods. For Aquilaria trees treated with these ten inducers, agarwood formed and spread throughout the entire tree from the transfusion point in the trunk to the roots and branches of the whole tree. Agarwood yield per tree reached 2,444.83 to 5,860.74 g, which is 4 to 28 times higher than that by the existing agarwood-inducing methods. Furthermore, this agarwood derived from Agar-Wit induction was found to have a higher quality compared with the existing methods, and similar to that of wild agarwood. This indicates Agar-Wit may have commercial potential. Induction of cultivated agarwood using this method could satisfy the significant demand for agarwood, while conserving and protecting the remaining wild Aquilaria trees.

  17. STBase: one million species trees for comparative biology.

    PubMed

    McMahon, Michelle M; Deepak, Akshay; Fernández-Baca, David; Boss, Darren; Sanderson, Michael J

    2015-01-01

    Comprehensively sampled phylogenetic trees provide the most compelling foundations for strong inferences in comparative evolutionary biology. Mismatches are common, however, between the taxa for which comparative data are available and the taxa sampled by published phylogenetic analyses. Moreover, many published phylogenies are gene trees, which cannot always be adapted immediately for species level comparisons because of discordance, gene duplication, and other confounding biological processes. A new database, STBase, lets comparative biologists quickly retrieve species level phylogenetic hypotheses in response to a query list of species names. The database consists of 1 million single- and multi-locus data sets, each with a confidence set of 1000 putative species trees, computed from GenBank sequence data for 413,000 eukaryotic taxa. Two bodies of theoretical work are leveraged to aid in the assembly of multi-locus concatenated data sets for species tree construction. First, multiply labeled gene trees are pruned to conflict-free singly-labeled species-level trees that can be combined between loci. Second, impacts of missing data in multi-locus data sets are ameliorated by assembling only decisive data sets. Data sets overlapping with the user's query are ranked using a scheme that depends on user-provided weights for tree quality and for taxonomic overlap of the tree with the query. Retrieval times are independent of the size of the database, typically a few seconds. Tree quality is assessed by a real-time evaluation of bootstrap support on just the overlapping subtree. Associated sequence alignments, tree files and metadata can be downloaded for subsequent analysis. STBase provides a tool for comparative biologists interested in exploiting the most relevant sequence data available for the taxa of interest. It may also serve as a prototype for future species tree oriented databases and as a resource for assembly of larger species phylogenies from precomputed

  18. Phylogenetic trees in bioinformatics

    SciTech Connect

    Burr, Tom L

    2008-01-01

    Genetic data is often used to infer evolutionary relationships among a collection of viruses, bacteria, animal or plant species, or other operational taxonomic units (OTU). A phylogenetic tree depicts such relationships and provides a visual representation of the estimated branching order of the OTUs. Tree estimation is unique for several reasons, including: the types of data used to represent each OTU; the use ofprobabilistic nucleotide substitution models; the inference goals involving both tree topology and branch length, and the huge number of possible trees for a given sample of a very modest number of OTUs, which implies that fmding the best tree(s) to describe the genetic data for each OTU is computationally demanding. Bioinformatics is too large a field to review here. We focus on that aspect of bioinformatics that includes study of similarities in genetic data from multiple OTUs. Although research questions are diverse, a common underlying challenge is to estimate the evolutionary history of the OTUs. Therefore, this paper reviews the role of phylogenetic tree estimation in bioinformatics, available methods and software, and identifies areas for additional research and development.

  19. The gravity apple tree

    NASA Astrophysics Data System (ADS)

    Espinosa Aldama, Mariana

    2015-04-01

    The gravity apple tree is a genealogical tree of the gravitation theories developed during the past century. The graphic representation is full of information such as guides in heuristic principles, names of main proponents, dates and references for original articles (See under Supplementary Data for the graphic representation). This visual presentation and its particular classification allows a quick synthetic view for a plurality of theories, many of them well validated in the Solar System domain. Its diachronic structure organizes information in a shape of a tree following similarities through a formal concept analysis. It can be used for educational purposes or as a tool for philosophical discussion.

  20. Evolutionary tree reconstruction

    NASA Technical Reports Server (NTRS)

    Cheeseman, Peter; Kanefsky, Bob

    1990-01-01

    It is described how Minimum Description Length (MDL) can be applied to the problem of DNA and protein evolutionary tree reconstruction. If there is a set of mutations that transform a common ancestor into a set of the known sequences, and this description is shorter than the information to encode the known sequences directly, then strong evidence for an evolutionary relationship has been found. A heuristic algorithm is described that searches for the simplest tree (smallest MDL) that finds close to optimal trees on the test data. Various ways of extending the MDL theory to more complex evolutionary relationships are discussed.

  1. Rule induction based on frequencies of attribute values

    NASA Astrophysics Data System (ADS)

    Borowik, Grzegorz; Kowalski, Karol

    2015-09-01

    Rule induction is one of the most significant issues in data mining. This is due to the fact that decision rules induced from the training data are used to classify new objects. The classification is based on matching the object with the decision rules. Specifically, the generated rules are used to resolve whether or not the object satisfies the conditions specified by the subset of attributes belonging to a given decision class. Most of the rule induction methods are insufficient for large databases and hence do not support today's Big Data issues. This is mainly due to the use of so-called discernibility matrices during calculations. The purpose of this paper is the idea of the implementation of a new efficient rule induction algorithm that is based on statistics of attribute values and that avoids building the discernibility matrix explicitly. Tests have shown that the implementation is much more efficient than currently available solutions for large data sets.

  2. Trees of trees: an approach to comparing multiple alternative phylogenies.

    PubMed

    Nye, Tom M W

    2008-10-01

    Phylogenetic analysis very commonly produces several alternative trees for a given fixed set of taxa. For example, different sets of orthologous genes may be analyzed, or the analysis may sample from a distribution of probable trees. This article describes an approach to comparing and visualizing multiple alternative phylogenies via the idea of a "tree of trees" or "meta-tree." A meta-tree clusters phylogenies with similar topologies together in the same way that a phylogeny clusters species with similar DNA sequences. Leaf nodes on a meta-tree correspond to the original set of phylogenies given by some analysis, whereas interior nodes correspond to certain consensus topologies. The construction of meta-trees is motivated by analogy with construction of a most parsimonious tree for DNA data, but instead of using DNA letters, in a meta-tree the characters are partitions or splits of the set of taxa. An efficient algorithm for meta-tree construction is described that makes use of a known relationship between the majority consensus and parsimony in terms of gain and loss of splits. To illustrate these ideas meta-trees are constructed for two datasets: a set of gene trees for species of yeast and trees from a bootstrap analysis of a set of gene trees in ray-finned fish. A software tool for constructing meta-trees and comparing alternative phylogenies is available online, and the source code can be obtained from the author.

  3. Tree-bank grammars

    SciTech Connect

    Charniak, E.

    1996-12-31

    By a {open_quotes}tree-bank grammar{close_quotes} we mean a context-free grammar created by reading the production rules directly from hand-parsed sentences in a tree bank. Common wisdom has it that such grammars do not perform well, though we know of no published data on the issue. The primary purpose of this paper is to show that the common wisdom is wrong. In particular, we present results on a tree-bank grammar based on the Penn Wall Street Journal tree bank. To the best of our knowledge, this grammar outperforms all other non-word-based statistical parsers/grammars on this corpus. That is, it outperforms parsers that consider the input as a string of tags and ignore the actual words of the corpus.

  4. Leonardo's Tree Theory.

    ERIC Educational Resources Information Center

    Werner, Suzanne K.

    2003-01-01

    Describes a series of activities exploring Leonardo da Vinci's tree theory that are designed to strengthen 8th grade students' data collection and problem solving skills in physical science classes. (KHR)

  5. Tea tree oil.

    PubMed

    Larson, David; Jacob, Sharon E

    2012-01-01

    Tea tree oil is an increasingly popular ingredient in a variety of household and cosmetic products, including shampoos, massage oils, skin and nail creams, and laundry detergents. Known for its potential antiseptic properties, it has been shown to be active against a variety of bacteria, fungi, viruses, and mites. The oil is extracted from the leaves of the tea tree via steam distillation. This essential oil possesses a sharp camphoraceous odor followed by a menthol-like cooling sensation. Most commonly an ingredient in topical products, it is used at a concentration of 5% to 10%. Even at this concentration, it has been reported to induce contact sensitization and allergic contact dermatitis reactions. In 1999, tea tree oil was added to the North American Contact Dermatitis Group screening panel. The latest prevalence rates suggest that 1.4% of patients referred for patch testing had a positive reaction to tea tree oil.

  6. Generalized constructive tree weights

    SciTech Connect

    Rivasseau, Vincent E-mail: adrian.tanasa@ens-lyon.org; Tanasa, Adrian E-mail: adrian.tanasa@ens-lyon.org

    2014-04-15

    The Loop Vertex Expansion (LVE) is a quantum field theory (QFT) method which explicitly computes the Borel sum of Feynman perturbation series. This LVE relies in a crucial way on symmetric tree weights which define a measure on the set of spanning trees of any connected graph. In this paper we generalize this method by defining new tree weights. They depend on the choice of a partition of a set of vertices of the graph, and when the partition is non-trivial, they are no longer symmetric under permutation of vertices. Nevertheless we prove they have the required positivity property to lead to a convergent LVE; in fact we formulate this positivity property precisely for the first time. Our generalized tree weights are inspired by the Brydges-Battle-Federbush work on cluster expansions and could be particularly suited to the computation of connected functions in QFT. Several concrete examples are explicitly given.

  7. Progress in Induction Linacs

    SciTech Connect

    Caporaso, G J

    2000-09-27

    This presentation will be a broad survey of progress in induction technology over the past four years. Much work has been done on accelerators for hydrodynamic test radiography and other applications. Solid-state pulsers have been developed which can provide unprecedented flexibility and precision in pulse format and accelerating voltage for both ion and electron induction machines. Induction linacs can now be built which can operate with MHz repetition rates. Solid-state technology has also made possible the development of fast kickers for precision control of high current beams. New insulator technology has been developed which will improve conventional induction linacs in addition to enabling a new class of high gradient induction linacs.

  8. Principles of Induction Accelerators

    NASA Astrophysics Data System (ADS)

    Briggs*, Richard J.

    The basic concepts involved in induction accelerators are introduced in this chapter. The objective is to provide a foundation for the more detailed coverage of key technology elements and specific applications in the following chapters. A wide variety of induction accelerators are discussed in the following chapters, from the high current linear electron accelerator configurations that have been the main focus of the original developments, to circular configurations like the ion synchrotrons that are the subject of more recent research. The main focus in the present chapter is on the induction module containing the magnetic core that plays the role of a transformer in coupling the pulsed power from the modulator to the charged particle beam. This is the essential common element in all these induction accelerators, and an understanding of the basic processes involved in its operation is the main objective of this chapter. (See [1] for a useful and complementary presentation of the basic principles in induction linacs.)

  9. Tree Topology Estimation

    PubMed Central

    Estrada, Rolando; Tomasi, Carlo; Schmidler, Scott C.; Farsiu, Sina

    2015-01-01

    Tree-like structures are fundamental in nature, and it is often useful to reconstruct the topology of a tree—what connects to what—from a two-dimensional image of it. However, the projected branches often cross in the image: the tree projects to a planar graph, and the inverse problem of reconstructing the topology of the tree from that of the graph is ill-posed. We regularize this problem with a generative, parametric tree-growth model. Under this model, reconstruction is possible in linear time if one knows the direction of each edge in the graph—which edge endpoint is closer to the root of the tree—but becomes NP-hard if the directions are not known. For the latter case, we present a heuristic search algorithm to estimate the most likely topology of a rooted, three-dimensional tree from a single two-dimensional image. Experimental results on retinal vessel, plant root, and synthetic tree datasets show that our methodology is both accurate and efficient. PMID:26353004

  10. Anatomy of the Pythagoras' Tree

    ERIC Educational Resources Information Center

    Teia, Luis

    2016-01-01

    The architecture of nature can be seen at play in a tree: no two are alike. The Pythagoras' tree behaves just as a "tree" in that the root plus the same movement repeated over and over again grows from a seed, to a plant, to a tree. In human life, this movement is termed cell division. With triples, this movement is a geometrical and…

  11. How Trees Can Save Energy.

    ERIC Educational Resources Information Center

    Fazio, James R., Ed.

    1991-01-01

    This document might easily have been called "How To Use Trees To Save Energy". It presents the energy saving advantages of landscaping the home and community with trees. The discussion includes: (1) landscaping advice to obtain the benefits of tree shade; (2) the heat island phenomenon in cities; (3) how and where to properly plant trees for…

  12. State Trees and Arbor Days.

    ERIC Educational Resources Information Center

    Forest Service (USDA), Washington, DC.

    Provides information on state trees for each of the 50 states and the District of Columbia. Includes for each state: (1) year in which state tree was chosen; (2) common and scientific names of the tree; (3) arbor day observance; (4) address of state forester; and (5) drawings of the tree, leaf, and fruit or cone. (JN)

  13. An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

    NASA Technical Reports Server (NTRS)

    Tian, Jianhui; Porter, Adam; Zelkowitz, Marvin V.

    1992-01-01

    Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

  14. Properties of inductive reasoning.

    PubMed

    Heit, E

    2000-12-01

    This paper reviews the main psychological phenomena of inductive reasoning, covering 25 years of experimental and model-based research, in particular addressing four questions. First, what makes a case or event generalizable to other cases? Second, what makes a set of cases generalizable? Third, what makes a property or predicate projectable? Fourth, how do psychological models of induction address these results? The key results in inductive reasoning are outlined, and several recent models, including a new Bayesian account, are evaluated with respect to these results. In addition, future directions for experimental and model-based work are proposed.

  15. Direct reading inductance meter

    NASA Technical Reports Server (NTRS)

    Kolby, R. B. (Inventor)

    1977-01-01

    A direct reading inductance meter comprised of a crystal oscillator and an LC tuned oscillator is presented. The oscillators function respectively to generate a reference frequency, f(r), and to generate an initial frequency, f(0), which when mixed produce a difference equal to zero. Upon connecting an inductor of small unknown value in the LC circuit to change its resonant frequency to f(x), a difference frequency (f(r)-f(x)) is produced that is very nearly a linear function of the inductance of the inductor. The difference frequency is measured and displayed on a linear scale in units of inductance.

  16. How induction generators work

    SciTech Connect

    Nailen, R.L.

    1980-06-01

    The operating principles of the induction generator, which is a standard squirrel cage motor in which the shaft is coupled to and driven by an engine or turbine at a rate above its synchronous speed and which, under these conditions, produces electrical power, are described. The advantages of induction generators, e.g., low cost, simplicity, frequency and voltage controlled by the utility system, no synchronizing controls needed, and the advantages of using small induction generators run by wind turbines, small gas turbines and in low head hydro plants are discussed. (LCL)

  17. Decision making under uncertain categorization

    PubMed Central

    Chen, Stephanie Y.; Ross, Brian H.; Murphy, Gregory L.

    2014-01-01

    Two experiments investigated how category information is used in decision making under uncertainty and whether the framing of category information influences how it is used. Subjects were presented with vignettes in which the categorization of a critical item was ambiguous and were asked to choose among a set of actions with the goal of attaining the desired outcome for the main character in the story. The normative decision making strategy was to base the decision on all possible categories; however, research on a related topic, category-based induction, has found that people often only consider a single category when making predictions when categorization is uncertain. These experiments found that subjects tend to consider multiple categories when making decisions, but do so both when it is and is not appropriate, suggesting that use of multiple categories is not driven by an understanding of whether categories are relevant to the decision. Similarly, although a framing manipulation increased the rate of multiple-category use, it did so in situations in which multiple-category use both was and was not appropriate. PMID:25309475

  18. Software Development for Decision Analysis

    DTIC Science & Technology

    1975-03-01

    34|"𔃻" ’’ " ’■|’■’ J - " ■»—w—"■ ■ 1 »I ■■ »I mill 1 11 1 MI independence (Category 1) or partial Independence (Categories 2 and 3) can >>e...place vandom variable 1 after decision 3 in the tree. In the nuit phase of our research, we hope to develop general algorithms for translating any...nMiu uiiim^p^M (^PLANT EFFICIENCY^ \\~r\\ |1T) % (CAPITAL COSTS Tris ^ /KW (OPERATING COSTS^) r=TTl MILLS /KWH (jmc^lTIQn-j] MILLS /KWH.*’** By

  19. Reinforcement Learning Trees.

    PubMed

    Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R

    In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings.

  20. Reinforcement Learning Trees

    PubMed Central

    Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.

    2015-01-01

    In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings. PMID:26903687

  1. Data mining of the GAW14 simulated data using rough set theory and tree-based methods.

    PubMed

    Wei, Liang-Ying; Huang, Cheng-Lung; Chen, Chien-Hsiun

    2005-12-30

    Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci.

  2. Induction heating coupler

    NASA Technical Reports Server (NTRS)

    Fox, Robert L. (Inventor); Copeland, Carl E. (Inventor); Swaim, Robert J. (Inventor); Coultrip, Robert H. (Inventor); Johnston, David F. (Inventor); Phillips, W. Morris (Inventor); Johnson, Samuel D. (Inventor); Dinkins, James R. (Inventor); Buckley, John D. (Inventor)

    1994-01-01

    An induction heating device includes a handle having a hollow interior and two opposite ends, a wrist connected to one end of the handle, a U-shaped pole piece having two spaced apart ends, a tank circuit including an induction coil wrapped around the pole piece and a capacitor connected to the induction coil, a head connected to the wrist and including a housing for receiving the U-shaped pole piece, the two spaced apart ends of the pole piece extending outwardely beyond the housing, and a power source connected to the tank circuit. When the tank circuit is energized and a susceptor is placed in juxtaposition to the ends of the U-shaped pole piece, the susceptor is heated by induction heating due to magnetic flux passing between the two ends of the pole piece.

  3. Induction melter apparatus

    DOEpatents

    Roach, Jay A [Idaho Falls, ID; Richardson, John G [Idaho Falls, ID; Raivo, Brian D [Idaho Falls, ID; Soelberg, Nicholas R [Idaho Falls, ID

    2008-06-17

    Apparatus and methods of operation are provided for a cold-crucible-induction melter for vitrifying waste wherein a single induction power supply may be used to effect a selected thermal distribution by independently energizing at least two inductors. Also, a bottom drain assembly may be heated by an inductor and may include an electrically resistive heater. The bottom drain assembly may be cooled to solidify molten material passing therethrough to prevent discharge of molten material therefrom. Configurations are provided wherein the induction flux skin depth substantially corresponds with the central longitudinal axis of the crucible. Further, the drain tube may be positioned within the induction flux skin depth in relation to material within the crucible or may be substantially aligned with a direction of flow of molten material within the crucible. An improved head design including four shells forming thermal radiation shields and at least two gas-cooled plenums is also disclosed.

  4. Thermal kinetic inductance detector

    DOEpatents

    Cecil, Thomas; Gades, Lisa; Miceli, Antonio; Quaranta, Orlando

    2016-12-20

    A microcalorimeter for radiation detection that uses superconducting kinetic inductance resonators as the thermometers. The detector is frequency-multiplexed which enables detector systems with a large number of pixels.

  5. Enhancing Induction Coil Reliability

    NASA Astrophysics Data System (ADS)

    Kreter, K.; Goldstein, R.; Yakey, C.; Nemkov, V.

    2014-12-01

    In induction hardening, thermal fatigue is one of the main copper failure modes of induction heat treating coils. There have been papers published that describe this failure mode and others that describe some good design practices. The variables previously identified as the sources of thermal fatigue include radiation from the part surface, frequency, current, concentrator losses, water pressure and coil wall thickness. However, there is very little quantitative data on the factors that influence thermal fatigue in induction coils is available in the public domain. By using finite element analysis software this study analyzes the effect of common design variables of inductor cooling, and quantifies the relative importance of these variables. A comprehensive case study for a single shot induction coil with Fluxtrol A concentrator applied is used for the analysis.

  6. [Tree species information extraction of farmland returned to forests based on improved support vector machine algorithm].

    PubMed

    Wu, Jian; Peng, Dao-Li

    2011-04-01

    The difference analysis of spectrum among tree species and the improvement of classification algorithm are the difficult points of extracting tree species information using remote sensing images, and are also the keys to improving the accuracy in the tree species information extraction in farmland returned to forests area. TM images were selected in this study, and the spectral indexes that could distinguish tree species information were filtered by analyzing tree species spectrum. Afterwards, the information of tree species was extracted using improved support vector machine algorithm. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 81.7%. The corresponding result of the traditional method is 72.5%. The method in this paper can achieve a more precise information extraction of tree species and the results can meet the demand of accurate monitoring and decision-making. This method is significant to the rapid assessment of project quality.

  7. Doubly fed induction machine

    DOEpatents

    Skeist, S. Merrill; Baker, Richard H.

    2005-10-11

    An electro-mechanical energy conversion system coupled between an energy source and an energy load including an energy converter device having a doubly fed induction machine coupled between the energy source and the energy load to convert the energy from the energy source and to transfer the converted energy to the energy load and an energy transfer multiplexer coupled to the energy converter device to control the flow of power or energy through the doubly fed induction machine.

  8. Induction launcher design considerations

    NASA Technical Reports Server (NTRS)

    Driga, M. D.; Weldon, W. F.

    1989-01-01

    New concepts in the design of induction accelerators and their power supplies for space and military applications are discussed. Particular attention is given to a piecewise-rising-frequency power supply in which each elementary generator (normal compulsator or rising frequency generator) has a different base frequency. A preliminary design of a coaxial induction accelerator for a hypersonic real gas facility is discussed to illustrate the concepts described.

  9. Induction of the Proepicardium.

    PubMed

    Maya-Ramos, Lisandro; Cleland, James; Bressan, Michael; Mikawa, Takashi

    2013-09-01

    The proepicardium is a transient extracardiac embryonic tissue that gives rise to the epicardium and a number of coronary vascular cell lineages. This important extracardiac tissue develops through multiple steps of inductive events, from specification of multiple cell lineages to morphogenesis. This article will review our current understanding of inductive events involved in patterning of the proepicardium precursor field, specification of cell types within the proepicardium, and their extension and attachment to the heart.

  10. Induction Heating Systems

    NASA Technical Reports Server (NTRS)

    1991-01-01

    Induction heating technology, a magnetic non-deforming process, was developed by Langley researchers to join plastic and composite components in space. Under NASA license, Inductron Corporation uses the process to produce induction heating systems and equipment for numerous applications. The Torobonder, a portable system, comes with a number of interchangeable heads for aircraft repair. Other developments are the E Heating Head, the Toroid Joining Gun, and the Torobrazer. These products perform bonding applications more quickly, safely and efficiently than previous methods.

  11. Linear induction accelerator

    DOEpatents

    Buttram, M.T.; Ginn, J.W.

    1988-06-21

    A linear induction accelerator includes a plurality of adder cavities arranged in a series and provided in a structure which is evacuated so that a vacuum inductance is provided between each adder cavity and the structure. An energy storage system for the adder cavities includes a pulsed current source and a respective plurality of bipolar converting networks connected thereto. The bipolar high-voltage, high-repetition-rate square pulse train sets and resets the cavities. 4 figs.

  12. Tree nut allergens.

    PubMed

    Roux, Kenneth H; Teuber, Suzanne S; Sathe, Shridhar K

    2003-08-01

    Allergic reactions to tree nuts can be serious and life threatening. Considerable research has been conducted in recent years in an attempt to characterize those allergens that are most responsible for allergy sensitization and triggering. Both native and recombinant nut allergens have been identified and characterized and, for some, the IgE-reactive epitopes described. Some allergens, such as lipid transfer proteins, profilins, and members of the Bet v 1-related family, represent minor constituents in tree nuts. These allergens are frequently cross-reactive with other food and pollen homologues, and are considered panallergens. Others, such as legumins, vicilins, and 2S albumins, represent major seed storage protein constituents of the nuts. The allergenic tree nuts discussed in this review include those most commonly responsible for allergic reactions such as hazelnut, walnut, cashew, and almond as well as those less frequently associated with allergies including pecan, chestnut, Brazil nut, pine nut, macadamia nut, pistachio, coconut, Nangai nut, and acorn.

  13. Phytotechnology Technical and Regulatory Guidance and Decision Trees, Revised

    DTIC Science & Technology

    2009-02-01

    technologies at specific sites. Although the information in all ITRC products is believed to be reliable and accurate, the product and all material set...respect to particular materials , conditions, or procedures in specific applications of any technology. Consequently, ITRC recommends also...consulting applicable standards, laws, regulations, suppliers of materials , and material safety data sheets for information concerning safety and health risks

  14. Parsimonious Downgrading and Decision Trees Applied to the Inference Problem

    DTIC Science & Technology

    1998-01-01

    also been some related work in intrusion detection). We want to change this by siphoning o valuable techniques from our sister sub- elds in computer...is the prob- lem? The problem is that the relations within this set of data might still be high. Of course, this has been noted in many papers. The...the following two functions (possibly relations ?): L : 2D T M ! where 2D is the power set of D, T is time, and U : 2D T M ! F U is acting as a

  15. Decision tree rating scales for workload estimation: Theme and variations

    NASA Technical Reports Server (NTRS)

    Wierwille, W. W.; Skipper, J. H.; Rieger, C. A.

    1984-01-01

    The Modified Cooper-Harper (MCH) scale which is a sensitive indicator of workload in several different types of aircrew tasks was examined. The study determined if variations of the scale might provide greater sensitivity and the reasons for the sensitivity of the scale. The MCH scale and five newly devised scales were examined in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. It is indicated that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale exhibits consistent senstivity and remains the scale recommended for general use. The MCH scale results are consistent with earlier experiments. The rating scale experiments are reported and the questionnaire results which were directed to obtain a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described.

  16. Decision Tree Rating Scales for Workload Estimation: Theme and Variations

    NASA Technical Reports Server (NTRS)

    Wietwille, W. W.; Skipper, J. H.; Rieger, C. A.

    1984-01-01

    The modified Cooper-Harper (MCH) scale has been shown to be a sensitive indicator of workload in several different types of aircrew tasks. The MCH scale was examined to determine if certain variations of the scale might provide even greater sensitivity and to determine the reasons for the sensitivity of the scale. The MCH scale and five newly devised scales were studied in two different aircraft simulator experiments in which pilot loading was treated as an independent variable. Results indicate that while one of the new scales may be more sensitive in a given experiment, task dependency is a problem. The MCH scale exhibits consistent sensitivity and remains the scale recommended for general use. The results of the rating scale experiments are presented and the questionnaire results which were directed at obtaining a better understanding of the reasons for the relative sensitivity of the MCH scale and its variations are described.

  17. Decision trees and integrated features for computer aided mammographic screening

    SciTech Connect

    Kegelmeyer, W.P. Jr.; Groshong, B.; Allmen, M.; Woods, K.

    1997-02-01

    Breast cancer is a serious problem, which in the United States causes 43,000 deaths a year, eventually striking 1 in 9 women. Early detection is the only effective countermeasure, and mass mammography screening is the only reliable means for early detection. Mass screening has many shortcomings which could be addressed by a computer-aided mammographic screening system. Accordingly, we have applied the pattern recognition methods developed in earlier investigations of speculated lesions in mammograms to the detection of microcalcifications and circumscribed masses, generating new, more rigorous and uniform methods for the detection of both those signs. We have also improved the pattern recognition methods themselves, through the development of a new approach to combinations of multiple classifiers.

  18. Language Adaptive LVCSR Through Polyphone Decision Tree Specialization

    DTIC Science & Technology

    2000-08-01

    15.0 fectiveness of porting multilingual recognition systems to new tar- French (Bref) FR 7143 74 123K 13.9 get languages with very limited adaptation...22.9 Korean KO 6335 80 301K 16.4 recognition results are presented based on mono- and multilingual Spanish SP 5419 82 138K 17.6 recognizers developed in...limitation in developing LVCSR systems in new lan- lingual phonemic inventories and multilingual phoneme mod- guages is the need of large training data

  19. The Representation of Discrete Functions by Decision Trees.

    DTIC Science & Technology

    1982-02-28

    sum-of-products (SOP), which can be minimized (with respect to the number of conjunctions) by the well-known Quine - McCluskey algorithm. A function of n...number of rules leading to the same set of actions by the Quine - McCluskey algorithm, thereby yielding a minimal equivalent table (which, however, is...still not unique). The author overlooked the fact that the Quine - McCluskey algo-ithm requires exrponential time [in fact, the minimization of a limited

  20. Rough Set Based Splitting Criterion for Binary Decision Tree Classifiers

    DTIC Science & Technology

    2006-09-26

    k -folds... Mean of Mean Accuracy (MMA) for the Fold Mean 153 Table A.2 Mean of Mean Error Rate (MMER) for the Fold Mean 154 Table A.3 Mean of Mean ...Precision (MMP) for the Fold Mean 155 Table A.4 Mean of Mean Recall (MMR) for the Fold Mean 156 Table A.5 Mean of Mean F-Measure (MMFM) for the

  1. Effective Rating Scale Development for Speaking Tests: Performance Decision Trees

    ERIC Educational Resources Information Center

    Fulcher, Glenn; Davidson, Fred; Kemp, Jenny

    2011-01-01

    Rating scale design and development for testing speaking is generally conducted using one of two approaches: the measurement-driven approach or the performance data-driven approach. The measurement-driven approach prioritizes the ordering of descriptors onto a single scale. Meaning is derived from the scaling methodology and the agreement of…

  2. Graphical representation of life paths to better convey results of decision models to patients.

    PubMed

    Rubrichi, Stefania; Rognoni, Carla; Sacchi, Lucia; Parimbelli, Enea; Napolitano, Carlo; Mazzanti, Andrea; Quaglini, Silvana

    2015-04-01

    The inclusion of patients' perspectives in clinical practice has become an important matter for health professionals, in view of the increasing attention to patient-centered care. In this regard, this report illustrates a method for developing a visual aid that supports the physician in the process of informing patients about a critical decisional problem. In particular, we focused on interpretation of the results of decision trees embedding Markov models implemented with the commercial tool TreeAge Pro. Starting from patient-level simulations and exploiting some advanced functionalities of TreeAge Pro, we combined results to produce a novel graphical output that represents the distributions of outcomes over the lifetime for the different decision options, thus becoming a more informative decision support in a context of shared decision making. The training example used to illustrate the method is a decision tree for thromboembolism risk prevention in patients with nonvalvular atrial fibrillation.

  3. The gene tree delusion.

    PubMed

    Springer, Mark S; Gatesy, John

    2016-01-01

    Higher-level relationships among placental mammals are mostly resolved, but several polytomies remain contentious. Song et al. (2012) claimed to have resolved three of these using shortcut coalescence methods (MP-EST, STAR) and further concluded that these methods, which assume no within-locus recombination, are required to unravel deep-level phylogenetic problems that have stymied concatenation. Here, we reanalyze Song et al.'s (2012) data and leverage these re-analyses to explore key issues in systematics including the recombination ratchet, gene tree stoichiometry, the proportion of gene tree incongruence that results from deep coalescence versus other factors, and simulations that compare the performance of coalescence and concatenation methods in species tree estimation. Song et al. (2012) reported an average locus length of 3.1 kb for the 447 protein-coding genes in their phylogenomic dataset, but the true mean length of these loci (start codon to stop codon) is 139.6 kb. Empirical estimates of recombination breakpoints in primates, coupled with consideration of the recombination ratchet, suggest that individual coalescence genes (c-genes) approach ∼12 bp or less for Song et al.'s (2012) dataset, three to four orders of magnitude shorter than the c-genes reported by these authors. This result has general implications for the application of coalescence methods in species tree estimation. We contend that it is illogical to apply coalescence methods to complete protein-coding sequences. Such analyses amalgamate c-genes with different evolutionary histories (i.e., exons separated by >100,000 bp), distort true gene tree stoichiometry that is required for accurate species tree inference, and contradict the central rationale for applying coalescence methods to difficult phylogenetic problems. In addition, Song et al.'s (2012) dataset of 447 genes includes 21 loci with switched taxonomic names, eight duplicated loci, 26 loci with non-homologous sequences that are

  4. A celestial Christmas tree

    NASA Astrophysics Data System (ADS)

    Moore, S.

    2006-12-01

    Having finished decorating your terrestrial Christmas tree this year, you may care to step outside and view a celestial one. Well placed in the December night sky in the often overlooked but very rewarding constellation of Monoceros, NGC 2264, called the Christmas Tree by the American astronomer and writer Leland S. Copeland, lies due south around 1 a.m. in mid-December at an altitude of 50°. The cluster lies amid a vast area of nebulosity, well captured in the image by Gordon Rogers on the cover of this Journal.

  5. The Inference of Gene Trees with Species Trees

    PubMed Central

    Szöllősi, Gergely J.; Tannier, Eric; Daubin, Vincent; Boussau, Bastien

    2015-01-01

    This article reviews the various models that have been used to describe the relationships between gene trees and species trees. Molecular phylogeny has focused mainly on improving models for the reconstruction of gene trees based on sequence alignments. Yet, most phylogeneticists seek to reveal the history of species. Although the histories of genes and species are tightly linked, they are seldom identical, because genes duplicate, are lost or horizontally transferred, and because alleles can coexist in populations for periods that may span several speciation events. Building models describing the relationship between gene and species trees can thus improve the reconstruction of gene trees when a species tree is known, and vice versa. Several approaches have been proposed to solve the problem in one direction or the other, but in general neither gene trees nor species trees are known. Only a few studies have attempted to jointly infer gene trees and species trees. These models account for gene duplication and loss, transfer or incomplete lineage sorting. Some of them consider several types of events together, but none exists currently that considers the full repertoire of processes that generate gene trees along the species tree. Simulations as well as empirical studies on genomic data show that combining gene tree–species tree models with models of sequence evolution improves gene tree reconstruction. In turn, these better gene trees provide a more reliable basis for studying genome evolution or reconstructing ancestral chromosomes and ancestral gene sequences. We predict that gene tree–species tree methods that can deal with genomic data sets will be instrumental to advancing our understanding of genomic evolution. PMID:25070970

  6. Group performance and decision making.

    PubMed

    Kerr, Norbert L; Tindale, R Scott

    2004-01-01

    Theory and research on small group performance and decision making is reviewed. Recent trends in group performance research have found that process gains as well as losses are possible, and both are frequently explained by situational and procedural contexts that differentially affect motivation and resource coordination. Research has continued on classic topics (e.g., brainstorming, group goal setting, stress, and group performance) and relatively new areas (e.g., collective induction). Group decision making research has focused on preference combination for continuous response distributions and group information processing. New approaches (e.g., group-level signal detection) and traditional topics (e.g., groupthink) are discussed. New directions, such as nonlinear dynamic systems, evolutionary adaptation, and technological advances, should keep small group research vigorous well into the future.

  7. Cork Oak Vulnerability to Fire: The Role of Bark Harvesting, Tree Characteristics and Abiotic Factors

    PubMed Central

    Catry, Filipe X.; Moreira, Francisco; Pausas, Juli G.; Fernandes, Paulo M.; Rego, Francisco; Cardillo, Enrique; Curt, Thomas

    2012-01-01

    Forest ecosystems where periodical tree bark harvesting is a major economic activity may be particularly vulnerable to disturbances such as fire, since debarking usually reduces tree vigour and protection against external agents. In this paper we asked how cork oak Quercus suber trees respond after wildfires and, in particular, how bark harvesting affects post-fire tree survival and resprouting. We gathered data from 22 wildfires (4585 trees) that occurred in three southern European countries (Portugal, Spain and France), covering a wide range of conditions characteristic of Q. suber ecosystems. Post-fire tree responses (tree mortality, stem mortality and crown resprouting) were examined in relation to management and ecological factors using generalized linear mixed-effects models. Results showed that bark thickness and bark harvesting are major factors affecting resistance of Q. suber to fire. Fire vulnerability was higher for trees with thin bark (young or recently debarked individuals) and decreased with increasing bark thickness until cork was 3–4 cm thick. This bark thickness corresponds to the moment when exploited trees are debarked again, meaning that exploited trees are vulnerable to fire during a longer period. Exploited trees were also more likely to be top-killed than unexploited trees, even for the same bark thickness. Additionally, vulnerability to fire increased with burn severity and with tree diameter, and was higher in trees burned in early summer or located in drier south-facing aspects. We provided tree response models useful to help estimating the impact of fire and to support management decisions. The results suggested that an appropriate management of surface fuels and changes in the bark harvesting regime (e.g. debarking coexisting trees in different years or increasing the harvesting cycle) would decrease vulnerability to fire and contribute to the conservation of cork oak ecosystems. PMID:22787521

  8. Cork oak vulnerability to fire: the role of bark harvesting, tree characteristics and abiotic factors.

    PubMed

    Catry, Filipe X; Moreira, Francisco; Pausas, Juli G; Fernandes, Paulo M; Rego, Francisco; Cardillo, Enrique; Curt, Thomas

    2012-01-01

    Forest ecosystems where periodical tree bark harvesting is a major economic activity may be particularly vulnerable to disturbances such as fire, since debarking usually reduces tree vigour and protection against external agents. In this paper we asked how cork oak Quercus suber trees respond after wildfires and, in particular, how bark harvesting affects post-fire tree survival and resprouting. We gathered data from 22 wildfires (4585 trees) that occurred in three southern European countries (Portugal, Spain and France), covering a wide range of conditions characteristic of Q. suber ecosystems. Post-fire tree responses (tree mortality, stem mortality and crown resprouting) were examined in relation to management and ecological factors using generalized linear mixed-effects models. Results showed that bark thickness and bark harvesting are major factors affecting resistance of Q. suber to fire. Fire vulnerability was higher for trees with thin bark (young or recently debarked individuals) and decreased with increasing bark thickness until cork was 3-4 cm thick. This bark thickness corresponds to the moment when exploited trees are debarked again, meaning that exploited trees are vulnerable to fire during a longer period. Exploited trees were also more likely to be top-killed than unexploited trees, even for the same bark thickness. Additionally, vulnerability to fire increased with burn severity and with tree diameter, and was higher in trees burned in early summer or located in drier south-facing aspects. We provided tree response models useful to help estimating the impact of fire and to support management decisions. The results suggested that an appropriate management of surface fuels and changes in the bark harvesting regime (e.g. debarking coexisting trees in different years or increasing the harvesting cycle) would decrease vulnerability to fire and contribute to the conservation of cork oak ecosystems.

  9. A Universal Phylogenetic Tree.

    ERIC Educational Resources Information Center

    Offner, Susan

    2001-01-01

    Presents a universal phylogenetic tree suitable for use in high school and college-level biology classrooms. Illustrates the antiquity of life and that all life is related, even if it dates back 3.5 billion years. Reflects important evolutionary relationships and provides an exciting way to learn about the history of life. (SAH)

  10. Tree-Ties.

    ERIC Educational Resources Information Center

    Gresczyk, Rick

    Created to help students understand how plants were used for food, for medicine, and for arts and crafts among the Ojibwe (Chippewa) Indians, the game Tree-Ties combines earth and social sciences within a specific culture. The game requires mutual respect, understanding, and agreement to succeed. Sounding like the word "treaties", the…

  11. Christmas Tree Category Manual.

    ERIC Educational Resources Information Center

    Bowman, James S.; Turmel, Jon P.

    This manual provides information needed to meet the standards for pesticide applicator certification. Pests and diseases of christmas tree plantations are identified and discussed. Section one deals with weeds and woody plants and the application, formulation and effects of herbicides in controlling them. Section two discusses specific diseases…

  12. Digging Deeper with Trees.

    ERIC Educational Resources Information Center

    Growing Ideas, 2001

    2001-01-01

    Describes hands-on science areas that focus on trees. A project on leaf pigmentation involves putting crushed leaves in a test tube with solvent acetone to dissolve pigment. In another project, students learn taxonomy by sorting and classifying leaves based on observable characteristics. Includes a language arts connection. (PVD)

  13. Phylogenics & Tree-Thinking

    ERIC Educational Resources Information Center

    Baum, David A.; Offner, Susan

    2008-01-01

    Phylogenetic trees, which are depictions of the inferred evolutionary relationships among a set of species, now permeate almost all branches of biology and are appearing in increasing numbers in biology textbooks. While few state standards explicitly require knowledge of phylogenetics, most require some knowledge of evolutionary biology, and many…

  14. The Sacred Tree.

    ERIC Educational Resources Information Center

    Lethbridge Univ. (Alberta).

    Designed as a text for high school students and adults, this illustrated book presents ethical concepts and teachings of Native societies throughout North America concerning the nature and possibilities of human existence. The final component of a course in self-discovery and development, the book begins with the legend of the "Sacred Tree"…

  15. Wrapper Induction Software

    SciTech Connect

    2011-08-18

    Wrapper Induction is a software package that allows for unsupervised, semi-supervised, and manual extraction of social media data independent of language or site architecture. A large range of blog formats is available to individuals as means of publishing data to the internet. Blogs are a source of rich information for analysts. With a growing volume of information and blog engines, there is an increased need for automatic or semi-automatic extraction of that data for processing to help deliver results to analysts. Wrapper Induction is designed to automatically or semi-automatically create a template that can be used to harvest blog data from websites. Blogs are in a variety of formats and languages. Wrapper Induction creates a template and extracts blog data in a way that is independent of a specified blog format or language.

  16. New Life From Dead Trees

    ERIC Educational Resources Information Center

    DeGraaf, Richard M.

    1978-01-01

    There are numerous bird species that will nest only in dead or dying trees. Current forestry practices include clearing forests of these snags, or dead trees. This practice is driving many species out of the forests. An illustrated example of bird succession in and on a tree is given. (MA)

  17. The Hopi Fruit Tree Book.

    ERIC Educational Resources Information Center

    Nyhuis, Jane

    Referring as often as possible to traditional Hopi practices and to materials readily available on the reservation, the illustrated booklet provides information on the care and maintenance of young fruit trees. An introduction to fruit trees explains the special characteristics of new trees, e.g., grafting, planting pits, and watering. The…

  18. Building up rhetorical structure trees

    SciTech Connect

    Marcu, D.

    1996-12-31

    I use the distinction between the nuclei and the satellites that pertain to discourse relations to introduce a compositionality criterion for discourse trees. I provide a first-order formalization of rhetorical structure trees and, on its basis, I derive an algorithm that constructs all the valid rhetorical trees that can be associated with a given discourse.

  19. The Tree Worker's Manual. [Revised.

    ERIC Educational Resources Information Center

    Lilly, S. J.

    This manual acquaints readers with the general operations of the tree care industry. The manual covers subjects important to a tree worker and serves as a training aid for workers at the entry level as tree care professionals. Each chapter begins with a set of objectives and may include figures, tables, and photographs. Ten chapters are included:…

  20. Our Air: Unfit for Trees.

    ERIC Educational Resources Information Center

    Dochinger, Leon S.

    To help urban, suburban, and rural tree owners know about air pollution's effects on trees and their tolerance and intolerance to pollutants, the USDA Forest Service has prepared this booklet. It answers the following questions about atmospheric pollution: Where does it come from? What can it do to trees? and What can we do about it? In addition,…

  1. Baldcypress tree ring elemental concentrations at Reelfoot Lake, Tennessee from AD 1795 to AD 1820

    SciTech Connect

    Van Arsdale, R.; Hall, G.

    1995-11-01

    Many two hundred year old baldcypress trees in Reelfoot Lake, Tennessee, lived through the great New Madrid earthquakes of 1811--1812. This study was undertaken to determine if the elemental composition of baldcypress tree rings showed any systematic variation through the earthquake period of AD 1795 through AD 1820. Multiple cores were collected from two Reelfoot Lake baldcypress trees and analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Individual yearly rings and five-year ring segments were analyzed to determine their elemental compositions. The cores were analyzed for Li through U but only Ba, Ce, Cs, Cu, I, La, Mg, Mn, Nd, Rb, Sm, Sr, and Zn were found to be in appropriate concentrations for this study. Of these elements only Ce, I, La, Nd, Rb, and Sm showed any systematic changes within individual cores. Comparison of three cores taken from one tree reveal that tree-ring elemental concentrations and changes in tree-ring elemental concentration through time are very different among the cores. When comparing the elemental concentrations of tree rings for the same years in the two different trees neither elemental concentrations nor changes in elemental concentration through time were similar. We conclude that the elemental concentrations in the tree rings of the two baldcypress trees analyzed in this study show no systematic change through the earthquake period of AD 1795 through AD 1820.

  2. HIGH GRADIENT INDUCTION ACCELERATOR

    SciTech Connect

    Caporaso, G J; Sampayan, S; Chen, Y; Blackfield, D; Harris, J; Hawkins, S; Holmes, C; Krogh, M; Nelson, S; Nunnally, W; Paul, A; Poole, B; Rhodes, M; Sanders, D; Selenes, K; Sullivan, J; Wang, L; Watson, J

    2007-06-21

    A new type of compact induction accelerator is under development at the Lawrence Livermore National Laboratory that promises to increase the average accelerating gradient by at least an order of magnitude over that of existing induction machines. The machine is based on the use of high gradient vacuum insulators, advanced dielectric materials and switches and is stimulated by the desire for compact flash x-ray radiography sources. Research describing an extreme variant of this technology aimed at proton therapy for cancer will be described. Progress in applying this technology to several applications will be reviewed.

  3. Inductive dielectric analyzer

    NASA Astrophysics Data System (ADS)

    Agranovich, Daniel; Polygalov, Eugene; Popov, Ivan; Ben Ishai, Paul; Feldman, Yuri

    2017-03-01

    One of the approaches to bypass the problem of electrode polarization in dielectric measurements is the free electrode method. The advantage of this technique is that, the probing electric field in the material is not supplied by contact electrodes, but rather by electromagnetic induction. We have designed an inductive dielectric analyzer based on a sensor comprising two concentric toroidal coils. In this work, we present an analytic derivation of the relationship between the impedance measured by the sensor and the complex dielectric permittivity of the sample. The obtained relationship was successfully employed to measure the dielectric permittivity and conductivity of various alcohols and aqueous salt solutions.

  4. Induction powered biological radiosonde

    NASA Technical Reports Server (NTRS)

    Fryer, T. B. (Inventor)

    1980-01-01

    An induction powered implanted monitor for epidurally measuring intracranial pressure and telemetering the pressure information to a remote readout is disclosed. The monitor utilizes an inductance-capacitance (L-C) oscillator in which the C comprises a variable capacitance transducer, one electrode of which is a small stiff pressure responsive diaphragm. The oscillator is isolated from a transmitting tank circuit by a buffer circuit and all electric components in the implanted unit except an input and an output coil are shielded by a metal housing.

  5. Geometric tree kernels: classification of COPD from airway tree geometry.

    PubMed

    Feragen, Aasa; Petersen, Jens; Grimm, Dominik; Dirksen, Asger; Pedersen, Jesper Holst; Borgwardt, Karsten; de Bruijne, Marleen

    2013-01-01

    Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree. While state-of-the-art graph and tree kernels use combinatorial tree/graph structure with discrete node and edge labels, the kernels presented in this paper can include geometric information such as branch shape, branch radius or other vector valued properties. In addition to being flexible in their ability to model different types of attributes, the presented kernels are computationally efficient and some of them can easily be computed for large datasets (N - 10.000) of trees with 30 - 600 branches. Combining the kernels with standard machine learning tools enables us to analyze the relation between disease and anatomical tree structure and geometry. Experimental results: The kernels are used to compare airway trees segmented from low-dose CT, endowed with branch shape descriptors and airway wall area percentage measurements made along the tree. Using kernelized hypothesis testing we show that the geometric airway trees are significantly differently distributed in patients with Chronic Obstructive Pulmonary Disease (COPD) than in healthy individuals. The geometric tree kernels also give a significant increase in the classification accuracy of COPD from geometric tree structure endowed with airway wall thickness measurements in comparison with state-of-the-art methods, giving further insight into the relationship between airway wall thickness and COPD. Software: Software for computing kernels and statistical tests is available at http://image.diku.dk/aasa/software.php.

  6. Mesoderm induction: from caps to chips.

    PubMed

    Kimelman, David

    2006-05-01

    Vertebrate mesoderm induction is one of the classical problems in developmental biology. Various developmental biology approaches, particularly in Xenopus and zebrafish, have identified many of the key factors that are involved in this process and have provided major insights into how these factors interact as part of a signalling and transcription-factor network. These data are beginning to be refined by high-throughput approaches such as microarray assays. Future challenges include understanding how the prospective mesodermal cells integrate the various signals they receive and how they resolve this information to regulate their morphogenetic behaviours and cell-fate decisions.

  7. Tree Colors: Color Schemes for Tree-Structured Data.

    PubMed

    Tennekes, Martijn; de Jonge, Edwin

    2014-12-01

    We present a method to map tree structures to colors from the Hue-Chroma-Luminance color model, which is known for its well balanced perceptual properties. The Tree Colors method can be tuned with several parameters, whose effect on the resulting color schemes is discussed in detail. We provide a free and open source implementation with sensible parameter defaults. Categorical data are very common in statistical graphics, and often these categories form a classification tree. We evaluate applying Tree Colors to tree structured data with a survey on a large group of users from a national statistical institute. Our user study suggests that Tree Colors are useful, not only for improving node-link diagrams, but also for unveiling tree structure in non-hierarchical visualizations.

  8. Rate of tree carbon accumulation increases continuously with tree size.

    PubMed

    Stephenson, N L; Das, A J; Condit, R; Russo, S E; Baker, P J; Beckman, N G; Coomes, D A; Lines, E R; Morris, W K; Rüger, N; Alvarez, E; Blundo, C; Bunyavejchewin, S; Chuyong, G; Davies, S J; Duque, A; Ewango, C N; Flores, O; Franklin, J F; Grau, H R; Hao, Z; Harmon, M E; Hubbell, S P; Kenfack, D; Lin, Y; Makana, J-R; Malizia, A; Malizia, L R; Pabst, R J; Pongpattananurak, N; Su, S-H; Sun, I-F; Tan, S; Thomas, D; van Mantgem, P J; Wang, X; Wiser, S K; Zavala, M A

    2014-03-06

    Forests are major components of the global carbon cycle, providing substantial feedback to atmospheric greenhouse gas concentrations. Our ability to understand and predict changes in the forest carbon cycle--particularly net primary productivity and carbon storage--increasingly relies on models that represent biological processes across several scales of biological organization, from tree leaves to forest stands. Yet, despite advances in our understanding of productivity at the scales of leaves and stands, no consensus exists about the nature of productivity at the scale of the individual tree, in part because we lack a broad empirical assessment of whether rates of absolute tree mass growth (and thus carbon accumulation) decrease, remain constant, or increase as trees increase in size and age. Here we present a global analysis of 403 tropical and temperate tree species, showing that for most species mass growth rate increases continuously with tree size. Thus, large, old trees do not act simply as senescent carbon reservoirs but actively fix large amounts of carbon compared to smaller trees; at the extreme, a single big tree can add the same amount of carbon to the forest within a year as is contained in an entire mid-sized tree. The apparent paradoxes of individual tree growth increasing with tree size despite declining leaf-level and stand-level productivity can be explained, respectively, by increases in a tree's total leaf area that outpace declines in productivity per unit of leaf area and, among other factors, age-related reductions in population density. Our results resolve conflicting assumptions about the nature of tree growth, inform efforts to undertand and model forest carbon dynamics, and have additional implications for theories of resource allocation and plant senescence.

  9. Density Estimation Trees as fast non-parametric modelling tools

    NASA Astrophysics Data System (ADS)

    Anderlini, Lucio

    2016-10-01

    A Density Estimation Tree (DET) is a decision trees trained on a multivariate dataset to estimate the underlying probability density function. While not competitive with kernel techniques in terms of accuracy, DETs are incredibly fast, embarrassingly parallel and relatively small when stored to disk. These properties make DETs appealing in the resource- expensive horizon of the LHC data analysis. Possible applications may include selection optimization, fast simulation and fast detector calibration. In this contribution I describe the algorithm and its implementation made available to the HEP community as a RooFit object. A set of applications under discussion within the LHCb Collaboration are also briefly illustrated.

  10. Fault-Tree Compiler Program

    NASA Technical Reports Server (NTRS)

    Butler, Ricky W.; Martensen, Anna L.

    1992-01-01

    FTC, Fault-Tree Compiler program, is reliability-analysis software tool used to calculate probability of top event of fault tree. Five different types of gates allowed in fault tree: AND, OR, EXCLUSIVE OR, INVERT, and M OF N. High-level input language of FTC easy to understand and use. Program supports hierarchical fault-tree-definition feature simplifying process of description of tree and reduces execution time. Solution technique implemented in FORTRAN, and user interface in Pascal. Written to run on DEC VAX computer operating under VMS operating system.

  11. Global Value Trees

    PubMed Central

    Zhu, Zhen; Puliga, Michelangelo; Cerina, Federica; Chessa, Alessandro; Riccaboni, Massimo

    2015-01-01

    The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term “global value chains” (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the evolution of the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs. PMID:25978067

  12. Clinical decision support foundations.

    PubMed

    Pradhan, Malcolm; Liaw, Siaw Teng

    2010-01-01

    This chapter gives an educational overview of: * The elements of a clinical decision; * The elements of decision making: prior probability, evidence (likelihood), posterior probability, actions, utility (value); * A framework for decision making, and support, encompassing validity, utility, importance and certainty; and * The required elements of a clinical decision support system. * The role of knowledge management in the construction and maintenance of clinical decision support.

  13. Iteration, Not Induction

    ERIC Educational Resources Information Center

    Dobbs, David E.

    2009-01-01

    The main purpose of this note is to present and justify proof via iteration as an intuitive, creative and empowering method that is often available and preferable as an alternative to proofs via either mathematical induction or the well-ordering principle. The method of iteration depends only on the fact that any strictly decreasing sequence of…

  14. Using Continuity Induction

    ERIC Educational Resources Information Center

    Hathaway, Dan

    2011-01-01

    Here is a technique for proving the fundamental theorems of analysis that provides a unified way to pass from local properties to global properties on the real line, just as ordinary induction passes from local implication (if true for "k", the theorem is true for "k" + 1) to a global conclusion in the natural numbers.

  15. Learning and Inductive Inference

    DTIC Science & Technology

    1982-07-01

    is also a paradigm example of induction as discussed by philosophers ( Carnap , 1950). Data compression is of practical value for storage and...IBelser, A. 0. Hlolzman, and A. Kent (Eds.., Eneyclopedia of computer science and technology (Vol. II). New York: Marcel Dekker, 24 51. " Carnap , R. 1950

  16. Educational Inductive Gravimeter

    ERIC Educational Resources Information Center

    Nunn, John

    2014-01-01

    A simple inductive gravimeter constructed from a rigid plastic pipe and insulated copper wire is described. When a magnet is dropped through the vertically mounted pipe it induces small alternating voltages. These small signals are fed to the microphone input of a typical computer and sampled at a typical rate of 44.1 kHz using a custom computer…

  17. Training in Inductive Reasoning.

    ERIC Educational Resources Information Center

    Tomic, Welko

    This study investigated the effects of K. J. Klauer's (1989) inductive reasoning training program of teaching children. Effects of training and the range of transfer of the training were assessed. The subjects were 34 third-grade Dutch children of average ability, matched on age, sex, and IQ. Children from the training condition (N=17) received…

  18. Tree Rings: Timekeepers of the Past.

    ERIC Educational Resources Information Center

    Phipps, R. L.; McGowan, J.

    One of a series of general interest publications on science issues, this booklet describes the uses of tree rings in historical and biological recordkeeping. Separate sections cover the following topics: dating of tree rings, dating with tree rings, tree ring formation, tree ring identification, sample collections, tree ring cross dating, tree…

  19. How To Write a Municipal Tree Ordinance.

    ERIC Educational Resources Information Center

    Fazio, James R., Ed.

    1990-01-01

    At the heart of the Tree City USA program are four basic requirements: The community must have the following: (1) a tree board or department; (2) an annual community forestry program with financial provisions for trees and tree care; (3) an annual Arbor Day proclamation and observance; and (4) a tree ordinance. Sections of a model tree ordinance…

  20. Insert tree completion system

    SciTech Connect

    Brands, K.W.; Ball, I.G.; Cegielski, E.J.; Gresham, J.S.; Saunders, D.N.

    1982-09-01

    This paper outlines the overall project for development and installation of a low-profile, caisson-installed subsea Christmas tree. After various design studies and laboratory and field tests of key components, a system for installation inside a 30-in. conductor was ordered in July 1978 from Cameron Iron Works Inc. The system is designed to have all critical-pressure-containing components below the mudline and, with the reduced profile (height) above seabed, provides for improved safety of satellite underwater wells from damage by anchors, trawl boards, and even icebergs. In addition to the innovative nature of the tree design, the completion includes improved 3 1/2-in. through flowline (TFL) pumpdown completion equipment with deep set safety valves and a dual detachable packer head for simplified workover capability. The all-hydraulic control system incorporates a new design of sequencing valve for both Christmas tree control and remote flowline connection. A semisubmersible drilling rig was used to initiate the first end flowline connection at the wellhead for subsequent tie-in to the prelaid, surface-towed, all-welded subsea pipeline bundle.

  1. Active flows on trees

    NASA Astrophysics Data System (ADS)

    Forrow, Aden; Woodhouse, Francis G.; Dunkel, Jörn

    2016-11-01

    Coherent, large scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. We introduce and explore a generic model for compressible active flows on tree networks. In contrast to thermally-driven systems, active friction selects discrete states with only a small number of oscillation modes activated at distinct fixed amplitudes. This state selection can interact with graph topology to produce different localized dynamical time scales in separate regions of large networks. Using perturbation theory, we systematically predict the stationary states of noisy networks. Our analytical predictions agree well with a Bayesian state estimation based on a hidden Markov model applied to simulated time series data on binary trees. While the number of stable states per tree scales exponentially with the number of edges, the mean number of activated modes in each state averages 1 / 4 the number of edges. More broadly, these results suggest that the macroscopic response of active networks, from actin-myosin networks in cells to flow networks in Physarum polycephalum, can be dominated by a few select modes.

  2. Decision making under uncertainty: An investigation into the application of formal decision-making methods to safety issue decisions

    SciTech Connect

    Bohn, M P

    1992-12-01

    As part of the NRC-sponsored program to study the implications of Generic Issue 57, Effects of Fire Protection System Actuation on Safety-Related Equipment,'' a subtask was performed to evaluate the applicability of formal decision analysis methods to generic issues cost/benefit-type decisions and to apply these methods to the GI-57 results. In this report, the numerical results obtained from the analysis of three plants (two PWRs and one BWR) as developed in the technical resolution program for GI-57 were studied. For each plant, these results included a calculation of the person-REM averted due to various accident scenarios and various proposed modifications to mitigate the accident scenarios identified. These results were recomputed to break out the benefit in terms of contributions due to random event scenarios, fire event scenarios, and seismic event scenarios. Furthermore, the benefits associated with risk (in terms of person-REM) averted from earthquakes at three different seismic ground motion levels were separately considered. Given this data, formal decision methodologies involving decision trees, value functions, and utility functions were applied to this basic data. It is shown that the formal decision methodology can be applied at several different levels. Examples are given in which the decision between several retrofits is changed from that resulting from a simple cost/benefit-ratio criterion by virtue of the decision-makinger's expressed (and assumed) preferences.

  3. Linguistic decision making for robot route learning.

    PubMed

    He, Hongmei; McGinnity, Thomas Martin; Coleman, Sonya; Gardiner, Bryan

    2014-01-01

    Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behavior, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a linguistic decision tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelization of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a model's robustness regarding the quality of training data. Compared with the nonlinear system identification nonlinear auto-regressive moving average with eXogeneous inputs model structure with offline parameter estimation, the linguistic decision tree model with online linguistic ID3 learning achieves much better performance, robustness, and reliability.

  4. Tree-to-tree variation in seed size and its consequences for seed dispersal versus predation by rodents.

    PubMed

    Wang, Bo; Ives, Anthony R

    2017-03-01

    Individual variation in seed size and seed production is high in many plant species. How does this variation affect seed-dispersing animals and, in turn, the fitness of individual plants? In this study, we first surveyed intraspecific variation in seed mass and production in a population of a Chinese white pine, Pinus armandii. For 134 target trees investigated in 2012, there was very high variation in seed size, with mean seed mass varying among trees almost tenfold, from 0.038 to 0.361 g. Furthermore, 30 of the 134 trees produced seeds 2 years later, and for these individuals there was a correlation in seed mass of 0.59 between years, implying consistent differences among individuals. For a subset of 67 trees, we monitored the foraging preferences of scatter-hoarding rodents on a total of 15,301 seeds: 8380 were ignored, 3184 were eaten in situ, 2651 were eaten after being cached, and 395 were successfully dispersed (cached and left intact). At the scale of individual seeds, seed mass affected almost every decision that rodents made to eat, remove, and cache individual seeds. At the level of individual trees, larger seeds had increased probabilities of both predation and successful dispersal: the effects of mean seed size on costs (predation) and benefits (caching) balanced out. Thus, despite seed size affecting rodent decisions, variation among trees in dispersal success associated with mean seed size was small once seeds were harvested. This might explain, at least in part, the maintenance of high variation in mean seed mass among tree individuals.

  5. Exact solutions for species tree inference from discordant gene trees.

    PubMed

    Chang, Wen-Chieh; Górecki, Paweł; Eulenstein, Oliver

    2013-10-01

    Phylogenetic analysis has to overcome the grant challenge of inferring accurate species trees from evolutionary histories of gene families (gene trees) that are discordant with the species tree along whose branches they have evolved. Two well studied approaches to cope with this challenge are to solve either biologically informed gene tree parsimony (GTP) problems under gene duplication, gene loss, and deep coalescence, or the classic RF supertree problem that does not rely on any biological model. Despite the potential of these problems to infer credible species trees, they are NP-hard. Therefore, these problems are addressed by heuristics that typically lack any provable accuracy and precision. We describe fast dynamic programming algorithms that solve the GTP problems and the RF supertree problem exactly, and demonstrate that our algorithms can solve instances with data sets consisting of as many as 22 taxa. Extensions of our algorithms can also report the number of all optimal species trees, as well as the trees themselves. To better asses the quality of the resulting species trees that best fit the given gene trees, we also compute the worst case species trees, their numbers, and optimization score for each of the computational problems. Finally, we demonstrate the performance of our exact algorithms using empirical and simulated data sets, and analyze the quality of heuristic solutions for the studied problems by contrasting them with our exact solutions.

  6. Rate of tree carbon accumulation increases continuously with tree size

    USGS Publications Warehouse

    Stephenson, N.L.; Das, A.J.; Condit, R.; Russo, S.E.; Baker, P.J.; Beckman, N.G.; Coomes, D.A.; Lines, E.R.; Morris, W.K.; Rüger, N.; Álvarez, E.; Blundo, C.; Bunyavejchewin, S.; Chuyong, G.; Davies, S.J.; Duque, Á.; Ewango, C.N.; Flores, O.; Franklin, J.F.; Grau, H.R.; Hao, Z.; Harmon, M.E.; Hubbell, S.P.; Kenfack, D.; Lin, Y.; Makana, J.-R.; Malizia, A.; Malizia, L.R.; Pabst, R.J.; Pongpattananurak, N.; Su, S.-H.; Sun, I-F.; Tan, S.; Thomas, D.; van Mantgem, P.J.; Wang, X.; Wiser, S.K.; Zavala, M.A.

    2014-01-01

    Forests are major components of the global carbon cycle, providing substantial feedback to atmospheric greenhouse gas concentrations. Our ability to understand and predict changes in the forest carbon cycle—particularly net primary productivity and carbon storage - increasingly relies on models that represent biological processes across several scales of biological organization, from tree leaves to forest stands. Yet, despite advances in our understanding of productivity at the scales of leaves and stands, no consensus exists about the nature of productivity at the scale of the individual tree, in part because we lack a broad empirical assessment of whether rates of absolute tree mass growth (and thus carbon accumulation) decrease, remain constant, or increase as trees increase in size and age. Here we present a global analysis of 403 tropical and temperate tree species, showing that for most species mass growth rate increases continuously with tree size. Thus, large, old trees do not act simply as senescent carbon reservoirs but actively fix large amounts of carbon compared to smaller trees; at the extreme, a single big tree can add the same amount of carbon to the forest within a year as is contained in an entire mid-sized tree. The apparent paradoxes of individual tree growth increasing with tree size despite declining leaf-level and stand-level productivity can be explained, respectively, by increases in a tree’s total leaf area that outpace declines in productivity per unit of leaf area and, among other factors, age-related reductions in population density. Our results resolve conflicting assumptions about the nature of tree growth, inform efforts to understand and model forest carbon dynamics, and have additional implications for theories of resource allocation and plant senescence.

  7. Understanding Boswellia papyrifera tree secondary metabolites through bark spectral analysis

    NASA Astrophysics Data System (ADS)

    Girma, Atkilt; Skidmore, Andrew K.; de Bie, C. A. J. M.; Bongers, Frans

    2015-07-01

    Decision makers are concerned whether to tap or rest Boswellia Papyrifera trees. Tapping for the production of frankincense is known to deplete carbon reserves from the tree leading to production of less viable seeds, tree carbon starvation and ultimately tree mortality. Decision makers use traditional experience without considering the amount of metabolites stored or depleted from the stem-bark of the tree. This research was designed to come up with a non-destructive B. papyrifera tree metabolite estimation technique relevant for management using spectroscopy. The concentration of biochemicals (metabolites) found in the tree bark was estimated through spectral analysis. Initially, a random sample of 33 trees was selected, the spectra of bark measured with an Analytical Spectral Device (ASD) spectrometer. Bark samples were air dried and ground. Then, 10 g of sample was soaked in Petroleum ether to extract crude metabolites. Further chemical analysis was conducted to quantify and isolate pure metabolite compounds such as incensole acetate and boswellic acid. The crude metabolites, which relate to frankincense produce, were compared to plant properties (such as diameter and crown area) and reflectance spectra of the bark. Moreover, the extract was compared to the ASD spectra using partial least square regression technique (PLSR) and continuum removed spectral analysis. The continuum removed spectral analysis were performed, on two wavelength regions (1275-1663 and 1836-2217) identified through PLSR, using absorption features such as band depth, area, position, asymmetry and the width to characterize and find relationship with the bark extracts. The results show that tree properties such as diameter at breast height (DBH) and the crown area of untapped and healthy trees were strongly correlated to the amount of stored crude metabolites. In addition, the PLSR technique applied to the first derivative transformation of the reflectance spectrum was found to estimate the

  8. MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine

    PubMed Central

    Valdes, Gilmer; Luna, José Marcio; Eaton, Eric; Simone, Charles B.; Ungar, Lyle H.; Solberg, Timothy D.

    2016-01-01

    Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods. We present MediBoost, a novel framework for constructing decision trees that retain interpretability while having accuracy similar to ensemble methods, and compare MediBoost’s performance to that of conventional decision trees and ensemble methods on 13 medical classification problems. MediBoost significantly outperformed current decision tree algorithms in 11 out of 13 problems, giving accuracy comparable to ensemble methods. The resulting trees are of the same type as decision trees used throughout clinical practice but have the advantage of improved accuracy. Our algorithm thus gives the best of both worlds: it grows a single, highly interpretable tree that has the high accuracy of ensemble methods. PMID:27901055

  9. MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine

    NASA Astrophysics Data System (ADS)

    Valdes, Gilmer; Luna, José Marcio; Eaton, Eric; Simone, Charles B.; Ungar, Lyle H.; Solberg, Timothy D.

    2016-11-01

    Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods. We present MediBoost, a novel framework for constructing decision trees that retain interpretability while having accuracy similar to ensemble methods, and compare MediBoost’s performance to that of conventional decision trees and ensemble methods on 13 medical classification problems. MediBoost significantly outperformed current decision tree algorithms in 11 out of 13 problems, giving accuracy comparable to ensemble methods. The resulting trees are of the same type as decision trees used throughout clinical practice but have the advantage of improved accuracy. Our algorithm thus gives the best of both worlds: it grows a single, highly interpretable tree that has the high accuracy of ensemble methods.

  10. Tree detection in orchards from VHR satellite images using scale-space theory

    NASA Astrophysics Data System (ADS)

    Mahour, Milad; Tolpekin, Valentyn; Stein, Alfred

    2016-10-01

    This study focused on extracting reliable and detailed information from very High Resolution (VHR) satellite images for the detection of individual trees in orchards. The images contain detailed information on spectral and geometrical properties of trees. Their scale level, however, is insufficient for spectral properties of individual trees, because adjacent tree canopies interlock. We modeled trees using a bell shaped spectral profile. Identifying the brightest peak was challenging due to sun illumination effects caused 1 by differences in positions of the sun and the satellite sensor. Crown boundary detection was solved by using the NDVI from the same image. We used Gaussian scale-space methods that search for extrema in the scale-space domain. The procedures were tested on two orchards with different tree types, tree sizes and tree observation patterns in Iran. Validation was done using reference data derived from an UltraCam digital aerial photo. Local extrema of the determinant of the Hessian corresponded well to the geographical coordinates and the size of individual trees. False detections arising from a slight asymmetry of trees were distinguished from multiple detections of the same tree with different extents. Uncertainty assessment was carried out on the presence and spatial extents of individual trees. The study demonstrated how the suggested approach can be used for image segmentation for orchards with different types of trees. We concluded that Gaussian scale-space theory can be applied to extract information from VHR satellite images for individual tree detection. This may lead to improved decision making for irrigation and crop water requirement purposes in future studies.

  11. Inductive Reasoning: A Training Approach

    ERIC Educational Resources Information Center

    Klauer, Karl Josef; Phye, Gary D.

    2008-01-01

    Researchers have examined inductive reasoning to identify different cognitive processes when participants deal with inductive problems. This article presents a prescriptive theory of inductive reasoning that identifies cognitive processing using a procedural strategy for making comparisons. It is hypothesized that training in the use of the…

  12. Inductive Position Sensor

    NASA Technical Reports Server (NTRS)

    Youngquist, Robert C. (Inventor); Simmons, Stephen M. (Inventor)

    2015-01-01

    An inductive position sensor uses three independent inductors inductively coupled by a common medium such as air. First and second inductors are separated by a fixed distance with the first inductor's axial core and second inductor's axial core maintained parallel to one another. A third inductor is disposed between the first and second inductors with the third inductor's axial core being maintained parallel to those of the first and second inductors. The combination of the first and second inductors are configured for relative movement with the third inductor's axial core remaining parallel to those of the first and second inductors as distance changes from the third inductor to each of the first inductor and second inductor. An oscillating current can be supplied to at least one of the three inductors, while voltage induced in at least one of the three inductors not supplied with the oscillating current is measured.

  13. Induction motor control

    NASA Astrophysics Data System (ADS)

    Hansen, Irving G.

    Electromechanical actuators developed to date have commonly ultilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilized induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high-frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high-frequency power distribution and management techniques developed by NASA for Space Station Freedom.

  14. Induction motor control

    NASA Technical Reports Server (NTRS)

    Hansen, Irving G.

    1990-01-01

    Electromechanical actuators developed to date have commonly utilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilizes induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high frequency power distribution and management techniques developed by NASA for Space Station Freedom.

  15. Induction motor control

    NASA Technical Reports Server (NTRS)

    Hansen, Irving G.

    1990-01-01

    Electromechanical actuators developed to date have commonly ultilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilized induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high-frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high-frequency power distribution and management techniques developed by NASA for Space Station Freedom.

  16. Induction plasma tube

    DOEpatents

    Hull, D.E.

    1982-07-02

    An induction plasma tube having a segmented, fluid-cooled internal radiation shield is disclosed. The individual segments are thick in cross-section such that the shield occupies a substantial fraction of the internal volume of the plasma enclosure, resulting in improved performance and higher sustainable plasma temperatures. The individual segments of the shield are preferably cooled by means of a counterflow fluid cooling system wherein each segment includes a central bore and a fluid supply tube extending into the bore. The counterflow cooling system results in improved cooling of the individual segments and also permits use of relatively larger shield segments which permit improved electromagnetic coupling between the induction coil and a plasma located inside the shield. Four embodiments of the invention, each having particular advantages, are disclosed.

  17. Pulsed inductive HF laser

    NASA Astrophysics Data System (ADS)

    Razhev, A. M.; Churkin, D. S.; Kargapol'tsev, E. S.; Demchuk, S. V.

    2016-03-01

    We report the results of experimentally investigated dependences of temporal, spectral and spatial characteristics of an inductive HF-laser generation on the pump conditions. Gas mixtures H2 - F2(NF3 or SF66) and He(Ne) - H2 - F2(NF3 or SF6) were used as active media. The FWHM pulse duration reached 0.42 μs. This value corresponded to a pulsed power of 45 kW. For the first time, the emission spectrum of an inductive HF laser was investigated, which consisted of seven groups of bands with centres around the wavelengths of 2732, 2736, 2739, 2835, 2837, 2893 and 2913 nm. The cross section profile of the laser beam was a ring with a diameter of about 20 mm and width of about 5 mm. Parameters of laser operation in the repetitively pulsed regime were sufficiently stable. The amplitude instability of light pulses was no greater than 5% - 6%.

  18. Induction plasma tube

    DOEpatents

    Hull, Donald E.

    1984-01-01

    An induction plasma tube having a segmented, fluid-cooled internal radiation shield is disclosed. The individual segments are thick in cross-section such that the shield occupies a substantial fraction of the internal volume of the plasma enclosure, resulting in improved performance and higher sustainable plasma temperatures. The individual segments of the shield are preferably cooled by means of a counterflow fluid cooling system wherein each segment includes a central bore and a fluid supply tube extending into the bore. The counterflow cooling system results in improved cooling of the individual segments and also permits use of relatively larger shield segments which permit improved electromagnetic coupling between the induction coil and a plasma located inside the shield. Four embodiments of the invention, each having particular advantages, are disclosed.

  19. Inductively commutated coilguns

    SciTech Connect

    Mongeau, P.P. )

    1991-01-01

    In this paper the concept and relevance of power factor is presented in regards to high performance launchers. As the scale of launchers grows and as efforts to improve efficiency continue power factor considerations will become crucial in engineering design and ultimate launcher performance limits. The use of motion induced commutation to improve the power factor are discussed. Various approaches to inductive commutation are presented, including: the brush-commutated 9 MJ Coilgun, the solid state-switched coilgun and the quenchgun.

  20. High Gradient Induction Cell

    SciTech Connect

    Caporaso, G J

    2004-11-29

    A concept being developed for high current electron beams may have application to HEDP and is described here. It involves the use of planar Blumlein stacks placed inside an induction cell. The output end of the Blumlein stack is applied across a high gradient insulator (HGI). These insulators have been used successfully in the presence of kilo Ampere-level electron beam currents for tens of nanoseconds at gradients of 20 MV/meter.

  1. GODDESS: A Goal-Directed Decision Structuring System.

    DTIC Science & Technology

    1980-06-01

    in artificial intelligence as ’problem reduction’ or ’neans-enus ar.31.sVs (Nilsson, 1971). Each node in this structure represents a subproblem or a...recommendations and communi- cating the structure to others (Leal and Pearl, 1977). The program’s main techniques were borrowed from both artificial ... intelligence (AI) and decision analysis (DA). DA provided a formal structure of knowledge representation in the form of a decision tree quantified with

  2. Clustering with shallow trees

    NASA Astrophysics Data System (ADS)

    Bailly-Bechet, M.; Bradde, S.; Braunstein, A.; Flaxman, A.; Foini, L.; Zecchina, R.

    2009-12-01

    We propose a new method for obtaining hierarchical clustering based on the optimization of a cost function over trees of limited depth, and we derive a message-passing method that allows one to use it efficiently. The method and the associated algorithm can be interpreted as a natural interpolation between two well-known approaches, namely that of single linkage and the recently presented affinity propagation. We analyse using this general scheme three biological/medical structured data sets (human population based on genetic information, proteins based on sequences and verbal autopsies) and show that the interpolation technique provides new insight.

  3. Save a Tree

    NASA Astrophysics Data System (ADS)

    Williams, Kathryn R.

    1999-10-01

    Starting in September 1925, JCE reproduced pictures of famous chemists or chemistry-related works of art as frontispieces. Often, the Journal included a biography or other article about the picture. The August 1945 frontispiece featured the largest cork oak in the United States. An accompanying article described the goals of the Cork Project to plant cork trees in suitable locations in the U.S., to compensate for uncertain European and African sources during World War II. The final frontispiece appeared in December 1956. To view supplementary material, please refer to JCE Online's supplementary links.

  4. Palm tree peroxidases.

    PubMed

    Sakharov, I Yu

    2004-08-01

    Over the years novel plant peroxidases have been isolated from palm trees leaves. Some molecular and catalytic properties of palm peroxidases have been studied. The substrate specificity of palm peroxidases is distinct from the specificity of other plant peroxidases. Palm peroxidases show extremely high stability under acidic and alkaline conditions and high thermal stability. Moreover, these enzymes are more stable with respect to hydrogen peroxide treatment than other peroxidases. Due to their extremely high stability, palm peroxidases have been used successfully in the development of new bioanalytical tests, the construction of improved biosensors, and in polymer synthesis.

  5. A multi-neighbor-joining approach for phylogenetic tree reconstruction and visualization.

    PubMed

    Silva, Ana Estela A da; Villanueva, Wilfredo J P; Knidel, Helder; Bonato, Viniacute Cius; Reis, Sérgio F dos; Von Zuben, Fernando J

    2005-09-30

    The computationally challenging problem of reconstructing the phylogeny of a set of contemporary data, such as DNA sequences or morphological attributes, was treated by an extended version of the neighbor-joining (NJ) algorithm. The original NJ algorithm provides a single-tree topology, after a cascade of greedy pairing decisions that tries to simultaneously optimize the minimum evolution and the least squares criteria. Given that some sub-trees are more stable than others, and that the minimum evolution tree may not be achieved by the original NJ algorithm, we propose a multi-neighbor-joining (MNJ) algorithm capable of performing multiple pairing decisions at each level of the tree reconstruction, keeping various partial solutions along the recursive execution of the NJ algorithm. The main advantages of the new reconstruction procedure are: 1) as is the case for the original NJ algorithm, the MNJ algorithm is still a low-cost reconstruction method; 2) a further investigation of the alternative topologies may reveal stable and unstable sub-trees; 3) the chance of achieving the minimum evolution tree is greater; 4) tree topologies with very similar performances will be simultaneously presented at the output. When there are multiple unrooted tree topologies to be compared, a visualization tool is also proposed, using a radial layout to uniformly distribute the branches with the help of well-known metaheuristics used in computer science.

  6. An empirical evaluation of Bayesian networks derived from fault trees

    NASA Astrophysics Data System (ADS)

    Strasser, Shane; Sheppard, John

    Fault Isolation Manuals (FIMs) are derived from a type of decision tree and play an important role in maintenance troubleshooting of large systems. However, there are some drawbacks to using decision trees for maintenance, such as requiring a static order of tests to reach a conclusion. One method to overcome these limitations is by converting FIMs to Bayesian networks. However, it has been shown that Bayesian networks derived from FIMs will not contain the entire set of fault and alarm relationships present in the system from which the FIM was developed. In this paper we analyze Bayesian networks that have been derived from FIMs and report on several measurements, such as accuracy, relative probability of target diagnoses, diagnosis rank, and KL-divergence. Based on our results, we found that even with incomplete information, the Bayesian networks derived from the FIMs were still able to perform reasonably well.

  7. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

    SciTech Connect

    Liu, TIng; Jurrus, Elizabeth R.; Seyedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2012-11-11

    Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints in the sense of optimization to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.

  8. Approximate Splitting for Ensembles of Trees using Histograms

    SciTech Connect

    Kamath, C; Cantu-Paz, E; Littau, D

    2001-09-28

    Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an ensemble of classifiers and having them vote for the most popular class. Implicit in many of these techniques is the concept of randomization that generates different classifiers. In this paper, they focus on ensembles of decision trees that are created using a randomized procedure based on histograms. Techniques, such as histograms, that discretize continuous variables, have long been used in classification to convert the data into a form suitable for processing and to reduce the compute time. The approach combines the ideas behind discretization through histograms and randomization in ensembles to create decision trees by randomly selecting a split point in an interval around the best bin boundary in the histogram. The experimental results with public domain data show that ensembles generated using this approach are competitive in accuracy and superior in computational cost to other ensembles techniques such as boosting and bagging.

  9. Diagnostics for induction accelerators

    SciTech Connect

    Fessenden, T.J.

    1996-04-01

    The induction accelerator was conceived by N. C. Christofilos and first realized as the Astron accelerator that operated at LLNL from the early 1960`s to the end of 1975. This accelerator generated electron beams at energies near 6 MeV with typical currents of 600 Amperes in 400 ns pulses. The Advanced Test Accelerator (ATA) built at Livermore`s Site 300 produced 10,000 Ampere beams with pulse widths of 70 ns at energies approaching 50 MeV. Several other electron and ion induction accelerators have been fabricated at LLNL and LBNL. This paper reviews the principal diagnostics developed through efforts by scientists at both laboratories for measuring the current, position, energy, and emittance of beams generated by these high current, short pulse accelerators. Many of these diagnostics are closely related to those developed for other accelerators. However, the very fast and intense current pulses often require special diagnostic techniques and considerations. The physics and design of the more unique diagnostics developed for electron induction accelerators are presented and discussed in detail.

  10. The fault-tree compiler

    NASA Technical Reports Server (NTRS)

    Martensen, Anna L.; Butler, Ricky W.

    1987-01-01

    The Fault Tree Compiler Program is a new reliability tool used to predict the top event probability for a fault tree. Five different gate types are allowed in the fault tree: AND, OR, EXCLUSIVE OR, INVERT, and M OF N gates. The high level input language is easy to understand and use when describing the system tree. In addition, the use of the hierarchical fault tree capability can simplify the tree description and decrease program execution time. The current solution technique provides an answer precise (within the limits of double precision floating point arithmetic) to the five digits in the answer. The user may vary one failure rate or failure probability over a range of values and plot the results for sensitivity analyses. The solution technique is implemented in FORTRAN; the remaining program code is implemented in Pascal. The program is written to run on a Digital Corporation VAX with the VMS operation system.

  11. Inference of reversible tree languages.

    PubMed

    López, Damián; Sempere, José M; García, Pedro

    2004-08-01

    In this paper, we study the notion of k-reversibility and k-testability when regular tree languages are involved. We present an inference algorithm for learning a k-testable tree language that runs in polynomial time with respect to the size of the sample used. We also study the tree language classes in relation to other well known ones, and some properties of these languages are proven.

  12. Large Deviations for Random Trees

    PubMed Central

    Heitsch, Christine

    2010-01-01

    We consider large random trees under Gibbs distributions and prove a Large Deviation Principle (LDP) for the distribution of degrees of vertices of the tree. The LDP rate function is given explicitly. An immediate consequence is a Law of Large Numbers for the distribution of vertex degrees in a large random tree. Our motivation for this study comes from the analysis of RNA secondary structures. PMID:20216937

  13. Phylogenetic trees and Euclidean embeddings.

    PubMed

    Layer, Mark; Rhodes, John A

    2017-01-01

    It was recently observed by de Vienne et al. (Syst Biol 60(6):826-832, 2011) that a simple square root transformation of distances between taxa on a phylogenetic tree allowed for an embedding of the taxa into Euclidean space. While the justification for this was based on a diffusion model of continuous character evolution along the tree, here we give a direct and elementary explanation for it that provides substantial additional insight. We use this embedding to reinterpret the differences between the NJ and BIONJ tree building algorithms, providing one illustration of how this embedding reflects tree structures in data.

  14. Genealogy and gene trees.

    PubMed

    Rasmuson, Marianne

    2008-02-01

    Heredity can be followed in persons or in genes. Persons can be identified only a few generations back, but simplified models indicate that universal ancestors to all now living persons have occurred in the past. Genetic variability can be characterized as variants of DNA sequences. Data are available only from living persons, but from the pattern of variation gene trees can be inferred by means of coalescence models. The merging of lines backwards in time leads to a MRCA (most recent common ancestor). The time and place of living for this inferred person can give insights in human evolutionary history. Demographic processes are incorporated in the model, but since culture and customs are known to influence demography the models used ought to be tested against available genealogy. The Icelandic data base offers a possibility to do so and points to some discrepancies. Mitochondrial DNA and Y chromosome patterns give a rather consistent view of human evolutionary history during the latest 100 000 years but the earlier epochs of human evolution demand gene trees with longer branches. The results of such studies reveal as yet unsolved problems about the sources of our genome.

  15. Distributed Merge Trees

    SciTech Connect

    Morozov, Dmitriy; Weber, Gunther

    2013-01-08

    Improved simulations and sensors are producing datasets whose increasing complexity exhausts our ability to visualize and comprehend them directly. To cope with this problem, we can detect and extract significant features in the data and use them as the basis for subsequent analysis. Topological methods are valuable in this context because they provide robust and general feature definitions. As the growth of serial computational power has stalled, data analysis is becoming increasingly dependent on massively parallel machines. To satisfy the computational demand created by complex datasets, algorithms need to effectively utilize these computer architectures. The main strength of topological methods, their emphasis on global information, turns into an obstacle during parallelization. We present two approaches to alleviate this problem. We develop a distributed representation of the merge tree that avoids computing the global tree on a single processor and lets us parallelize subsequent queries. To account for the increasing number of cores per processor, we develop a new data structure that lets us take advantage of multiple shared-memory cores to parallelize the work on a single node. Finally, we present experiments that illustrate the strengths of our approach as well as help identify future challenges.

  16. A taxonomy of inductive problems.

    PubMed

    Kemp, Charles; Jern, Alan

    2014-02-01

    Inductive inferences about objects, features, categories, and relations have been studied for many years, but there are few attempts to chart the range of inductive problems that humans are able to solve. We present a taxonomy of inductive problems that helps to clarify the relationships between familiar inductive problems such as generalization, categorization, and identification, and that introduces new inductive problems for psychological investigation. Our taxonomy is founded on the idea that semantic knowledge is organized into systems of objects, features, categories, and relations, and we attempt to characterize all of the inductive problems that can arise when these systems are partially observed. Recent studies have begun to address some of the new problems in our taxonomy, and future work should aim to develop unified theories of inductive reasoning that explain how people solve all of the problems in the taxonomy.

  17. i-Tree: Tools to assess and manage structure, function, and value of community forests

    NASA Astrophysics Data System (ADS)

    Hirabayashi, S.; Nowak, D.; Endreny, T. A.; Kroll, C.; Maco, S.

    2011-12-01

    Trees in urban communities can mitigate many adverse effects associated with anthropogenic activities and climate change (e.g. urban heat island, greenhouse gas, air pollution, and floods). To protect environmental and human health, managers need to make informed decisions regarding urban forest management practices. Here we present the i-Tree suite of software tools (www.itreetools.org) developed by the USDA Forest Service and their cooperators. This software suite can help urban forest managers assess and manage the structure, function, and value of urban tree populations regardless of community size or technical capacity. i-Tree is a state-of-the-art, peer-reviewed Windows GUI- or Web-based software that is freely available, supported, and continuously refined by the USDA Forest Service and their cooperators. Two major features of i-Tree are 1) to analyze current canopy structures and identify potential planting spots, and 2) to estimate the environmental benefits provided by the trees, such as carbon storage and sequestration, energy conservation, air pollution removal, and storm water reduction. To cover diverse forest topologies, various tools were developed within the i-Tree suite: i-Tree Design for points (individual trees), i-Tree Streets for lines (street trees), and i-Tree Eco, Vue, and Canopy (in the order of complexity) for areas (community trees). Once the forest structure is identified with these tools, ecosystem services provided by trees can be estimated with common models and protocols, and reports in the form of texts, charts, and figures are then created for users. Since i-Tree was developed with a client/server architecture, nationwide data in the US such as location-related parameters, weather, streamflow, and air pollution data are stored in the server and retrieved to a user's computer at run-time. Freely available remote-sensed images (e.g. NLCD and Google maps) are also employed to estimate tree canopy characteristics. As the demand for i-Tree

  18. DIF Trees: Using Classification Trees to Detect Differential Item Functioning

    ERIC Educational Resources Information Center

    Vaughn, Brandon K.; Wang, Qiu

    2010-01-01

    A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…

  19. Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

    PubMed

    Ypma, Rolf J F; van Ballegooijen, W Marijn; Wallinga, Jacco

    2013-11-01

    Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

  20. How similar are recognition memory and inductive reasoning?

    PubMed

    Hayes, Brett K; Heit, Evan

    2013-07-01

    Conventionally, memory and reasoning are seen as different types of cognitive activities driven by different processes. In two experiments, we challenged this view by examining the relationship between recognition memory and inductive reasoning involving multiple forms of similarity. A common study set (members of a conjunctive category) was followed by a test set containing old and new category members, as well as items that matched the study set on only one dimension. The study and test sets were presented under recognition or induction instructions. In Experiments 1 and 2, the inductive property being generalized was varied in order to direct attention to different dimensions of similarity. When there was no time pressure on decisions, patterns of positive responding were strongly affected by property type, indicating that different types of similarity were driving recognition and induction. By comparison, speeded judgments showed weaker property effects and could be explained by generalization based on overall similarity. An exemplar model, GEN-EX (GENeralization from EXamples), could account for both the induction and recognition data. These findings show that induction and recognition share core component processes, even when the tasks involve flexible forms of similarity.

  1. Low inductance gas switching.

    SciTech Connect

    Chavez, Ray; Harjes, Henry Charles III; Wallace, Zachariah; Elizondo, Juan E.

    2007-10-01

    The laser trigger switch (LTS) is a key component in ZR-type pulsed power systems. In ZR, the pulse rise time through the LTS is > 200 ns and additional stages of pulse compression are required to achieve the desired <100 ns rise time. The inductance of the LTS ({approx}500nH) in large part determines the energy transfer time through the switch and there is much to be gained in improving system performance and reducing system costs by reducing this inductance. The current path through the cascade section of the ZR LTS is at a diameter of {approx} 6-inches which is certainly not optimal from an inductance point of view. The LTS connects components of much greater diameter (typically 4-5 feet). In this LDRD the viability of switch concepts in which the diameter of cascade section is greatly increased have been investigated. The key technical question to be answered was, will the desired multi-channel behavior be maintained in a cascade section of larger diameter. This LDRD proceeded in 2 distinct phases. The original plan for the LDRD was to develop a promising switch concept and then design, build, and test a moderate scale switch which would demonstrate the key features of the concept. In phase I, a switch concept which meet all electrical design criteria and had a calculated inductance of 150 nH was developed. A 1.5 MV test switch was designed and fabrication was initiated. The LDRD was then redirected due to budgetary concerns. The fabrication of the switch was halted and the focus of the LDRD was shifted to small scale experiments designed to answer the key technical question concerning multi-channel behavior. In phase II, the Multi-channel switch test bed (MCST) was designed and constructed. The purpose of MCST was to provide a versatile, fast turn around facility for the study the multi-channel electrical breakdown behavior of a ZR type cascade switch gap in a parameter space near that of a ZR LTS. Parameter scans on source impedance, gap tilt, gap spacing and

  2. Recognizing human gestures using a novel SVM tree

    NASA Astrophysics Data System (ADS)

    Jain, Hitesh; Chatterjee, Abhik; Kumar, Sanjeev; Raman, Balasubramanian

    2012-01-01

    In this paper, a novel support vector machine (SVM) tree is proposed for gesture recognition from the silhouette images. A skeleton based strategy is adopted to extract the features from a video sequence representing any human gesture. In our binary tree implementation of SVM, the number of binary classifiers required is reduced since, instead of grouping different classes together in order to train a global classifier, we select two classes for training at every node of the tree and use probability theory to classify the remaining points based on their similarities and differences to the two classes used for training. This process is carried on, randomly selecting two classes for training at a node, thus creating two child nodes and subsequently assigning the classes to the nodes derived. In the classification phase, we start out at the root node. At each node of the tree, a binary decision is made regarding the assignment of the input data point to either of the group represented by the left and right sub-tree of the node which may contain multiple classes. This is repeated recursively downward until we reach a leaf node that represents the class to which the input data point belonging. Finally, the proposed framework is tested on various data sets to check its efficiency. Encouraging results are achieved in terms of classification accuracy.

  3. Rubbery Polya Tree

    PubMed Central

    NIETO-BARAJAS, LUIS E.; MÜLLER, PETER

    2013-01-01

    Polya trees (PT) are random probability measures which can assign probability 1 to the set of continuous distributions for certain specifications of the hyperparameters. This feature distinguishes the PT from the popular Dirichlet process (DP) model which assigns probability 1 to the set of discrete distributions. However, the PT is not nearly as widely used as the DP prior. Probably the main reason is an awkward dependence of posterior inference on the choice of the partitioning subsets in the definition of the PT. We propose a generalization of the PT prior that mitigates this undesirable dependence on the partition structure, by allowing the branching probabilities to be dependent within the same level. The proposed new process is not a PT anymore. However, it is still a tail-free process and many of the prior properties remain the same as those for the PT. PMID:24368872

  4. Subgroup finding via Bayesian additive regression trees.

    PubMed

    Sivaganesan, Siva; Müller, Peter; Huang, Bin

    2017-03-09

    We provide a Bayesian decision theoretic approach to finding subgroups that have elevated treatment effects. Our approach separates the modeling of the response variable from the task of subgroup finding and allows a flexible modeling of the response variable irrespective of potential subgroups of interest. We use Bayesian additive regression trees to model the response variable and use a utility function defined in terms of a candidate subgroup and the predicted response for that subgroup. Subgroups are identified by maximizing the expected utility where the expectation is taken with respect to the posterior predictive distribution of the response, and the maximization is carried out over an a priori specified set of candidate subgroups. Our approach allows subgroups based on both quantitative and categorical covariates. We illustrate the approach using simulated data set study and a real data set. Copyright © 2017 John Wiley & Sons, Ltd.

  5. The Group Tree of Experience.

    ERIC Educational Resources Information Center

    Ping, Ki

    1994-01-01

    Describes a group activity that uses a tree as a metaphor to reflect both group and personal growth during adventure activities. The tree's roots represent the group's formation, the branches and leaves represent the group's diversity and capabilities, and the seeds represent the personal learning and growth that took place within the group.…

  6. Fractions, trees and unfinished business

    NASA Astrophysics Data System (ADS)

    Shraiman, Boris

    In this talk, mourning the loss of a teacher and a dear friend, I would like to share some unfinished thoughts loosely connecting - via Farey fraction trees - Kadanoff's study of universality of quasi-periodic route to chaos with the effort to understand universal features of genealogical trees.

  7. The limits to tree height.

    PubMed

    Koch, George W; Sillett, Stephen C; Jennings, Gregory M; Davis, Stephen D

    2004-04-22

    Trees grow tall where resources are abundant, stresses are minor, and competition for light places a premium on height growth. The height to which trees can grow and the biophysical determinants of maximum height are poorly understood. Some models predict heights of up to 120 m in the absence of mechanical damage, but there are historical accounts of taller trees. Current hypotheses of height limitation focus on increasing water transport constraints in taller trees and the resulting reductions in leaf photosynthesis. We studied redwoods (Sequoia sempervirens), including the tallest known tree on Earth (112.7 m), in wet temperate forests of northern California. Our regression analyses of height gradients in leaf functional characteristics estimate a maximum tree height of 122-130 m barring mechanical damage, similar to the tallest recorded trees of the past. As trees grow taller, increasing leaf water stress due to gravity and path length resistance may ultimately limit leaf expansion and photosynthesis for further height growth, even with ample soil moisture.

  8. Studying Evergreen Trees in December.

    ERIC Educational Resources Information Center

    Platt, Dorothy K.

    1991-01-01

    This lesson plan uses evergreen trees on sale in cities and villages during the Christmas season to teach identification techniques. Background information, activities, and recommended references guides deal with historical, symbolic and current uses of evergreen trees, physical characteristics, selection, care, and suggestions for post-Christmas…

  9. Tree Hydraulics: How Sap Rises

    ERIC Educational Resources Information Center

    Denny, Mark

    2012-01-01

    Trees transport water from roots to crown--a height that can exceed 100 m. The physics of tree hydraulics can be conveyed with simple fluid dynamics based upon the Hagen-Poiseuille equation and Murray's law. Here the conduit structure is modelled as conical pipes and as branching pipes. The force required to lift sap is generated mostly by…

  10. Assessing visual green effects of individual urban trees using airborne Lidar data.

    PubMed

    Chen, Ziyue; Xu, Bing; Gao, Bingbo

    2015-12-01

    Urban trees benefit people's daily life in terms of air quality, local climate, recreation and aesthetics. Among these functions, a growing number of studies have been conducted to understand the relationship between residents' preference towards local environments and visual green effects of urban greenery. However, except for on-site photography, there are few quantitative methods to calculate green visibility, especially tree green visibility, from viewers' perspectives. To fill this research gap, a case study was conducted in the city of Cambridge, which has a diversity of tree species, sizes and shapes. Firstly, a photograph-based survey was conducted to approximate the actual value of visual green effects of individual urban trees. In addition, small footprint airborne Lidar (Light detection and ranging) data was employed to measure the size and shape of individual trees. Next, correlations between visual tree green effects and tree structural parameters were examined. Through experiments and gradual refinement, a regression model with satisfactory R2 and limited large errors is proposed. Considering the diversity of sample trees and the result of cross-validation, this model has the potential to be applied to other study sites. This research provides urban planners and decision makers with an innovative method to analyse and evaluate landscape patterns in terms of tree greenness.

  11. 36 CFR 223.4 - Exchange of trees or portions of trees.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Exchange of trees or portions of trees. 223.4 Section 223.4 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF... PRODUCTS General Provisions § 223.4 Exchange of trees or portions of trees. Trees or portions of trees...

  12. 36 CFR 223.4 - Exchange of trees or portions of trees.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Exchange of trees or portions of trees. 223.4 Section 223.4 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF... PRODUCTS General Provisions § 223.4 Exchange of trees or portions of trees. Trees or portions of trees...

  13. 36 CFR 223.4 - Exchange of trees or portions of trees.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Exchange of trees or portions of trees. 223.4 Section 223.4 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF... PRODUCTS General Provisions § 223.4 Exchange of trees or portions of trees. Trees or portions of trees...

  14. 36 CFR 223.4 - Exchange of trees or portions of trees.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Exchange of trees or portions of trees. 223.4 Section 223.4 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF... PRODUCTS General Provisions § 223.4 Exchange of trees or portions of trees. Trees or portions of trees...

  15. Induction linear accelerators

    NASA Astrophysics Data System (ADS)

    Birx, Daniel

    1992-03-01

    Among the family of particle accelerators, the Induction Linear Accelerator is the best suited for the acceleration of high current electron beams. Because the electromagnetic radiation used to accelerate the electron beam is not stored in the cavities but is supplied by transmission lines during the beam pulse it is possible to utilize very low Q (typically<10) structures and very large beam pipes. This combination increases the beam breakup limited maximum currents to of order kiloamperes. The micropulse lengths of these machines are measured in 10's of nanoseconds and duty factors as high as 10-4 have been achieved. Until recently the major problem with these machines has been associated with the pulse power drive. Beam currents of kiloamperes and accelerating potentials of megavolts require peak power drives of gigawatts since no energy is stored in the structure. The marriage of liner accelerator technology and nonlinear magnetic compressors has produced some unique capabilities. It now appears possible to produce electron beams with average currents measured in amperes, peak currents in kiloamperes and gradients exceeding 1 MeV/meter, with power efficiencies approaching 50%. The nonlinear magnetic compression technology has replaced the spark gap drivers used on earlier accelerators with state-of-the-art all-solid-state SCR commutated compression chains. The reliability of these machines is now approaching 1010 shot MTBF. In the following paper we will briefly review the historical development of induction linear accelerators and then discuss the design considerations.

  16. Pulsed inductive HF laser

    SciTech Connect

    Razhev, A M; Kargapol'tsev, E S; Churkin, D S; Demchuk, S V

    2016-03-31

    We report the results of experimentally investigated dependences of temporal, spectral and spatial characteristics of an inductive HF-laser generation on the pump conditions. Gas mixtures H{sub 2} – F{sub 2}(NF{sub 3} or SF6{sub 6}) and He(Ne) – H{sub 2} – F{sub 2}(NF{sub 3} or SF{sub 6}) were used as active media. The FWHM pulse duration reached 0.42 μs. This value corresponded to a pulsed power of 45 kW. For the first time, the emission spectrum of an inductive HF laser was investigated, which consisted of seven groups of bands with centres around the wavelengths of 2732, 2736, 2739, 2835, 2837, 2893 and 2913 nm. The cross section profile of the laser beam was a ring with a diameter of about 20 mm and width of about 5 mm. Parameters of laser operation in the repetitively pulsed regime were sufficiently stable. The amplitude instability of light pulses was no greater than 5% – 6%. (lasers)

  17. "Menstrual induction" with Sulproston.

    PubMed

    Csapo, A I; Peskin, E G; Pulkkinen, M; Laajoki, V; Kivikoski, A; Lampe, L; Godeny, S; Szeverenyi, M; Herczeg, J; Resch, B; Bacos, L

    1982-11-01

    The PGE2-analogue Sulproston (16-phenoxy-omega-17,18,19,20-tetranor-PGE2-mythylsulfonylamide) was administered to 200 medically and gynecologically normal women who were 17 +/- 0.4 days beyond their expected menstrual period and who had a positive pregnancy test. The intramuscular impact dose (500 micrograms repeated after 4 hours) caused an immediate tonic uterine contraction which compromised the estradiol 17 beta, progesterone and chorionic gonadotropin production within the fetoplacental unit, and thereby allowed the evolution of cyclic uterine activity, cervical dilatation and tissue expulsion. Pregnancy termination was complete in 92% of women, 5.5% required surgical curettage and 2.5% were given a second Sulproston treatment 2-3 weeks after the first to remove retained tissue from the uterus. The medical induction of menstruation was preferred by 83% of the women who had previously experienced surgical termination of pregnancy. Normal menstruation resumed in all women after 36 +/- 0.9 days. The majority of 42 women questioned found Sulproston a satisfactory, safe, simple and effective drug regimen for "menstrual induction".

  18. Tree reconstruction from partial orders

    SciTech Connect

    Kannan, S.K. ); Warnow, T.J. )

    1993-01-01

    The problem of constructing trees given a matrix of interleaf distances is motivated by applications in computational evolutionary biology and linguistics. The general problem is to find an edge-weighted tree which most closely approximates the distance matrix. Although the construction problem is easy when the tree exactly fits the distance matrix, optimization problems under all popular criteria are either known or conjectured to be NP-complete. In this paper we consider the related problem where we are given a partial order on the pairwise distances, and wish to construct (if possible) an edge-weighted tree realizing the partial order. In particular we are interested in partial orders which arise from experiments on triples of species, which determine either a linear ordering of the three pairwise distances (called Total Order Model or TOM experiments) or only the pair(s) of minimum distance apart (called Partial Order Model or POM experiments). The POM and TOM experimental model is inspired by the model proposed by Kannan, Lawler, and Warnow for constructing trees from experiments which determine the rooted topology for any triple of species. We examine issues of construction of trees and consistency of TOM and POM experiments, where the trees may either be weighted or unweighted. Using these experiments to construct unweighted trees without nodes of degree two is motivated by a similar problem studied by Winkler, called the Discrete Metric Realization problem, which he showed to be strongly NP-hard. We have the following results: Determining consistency of a set of TOM or POM experiments is NP-Complete whether the tree is weighted or constrained to be unweighted and without degree two nodes. We can construct unweighted trees without degree two nodes from TOM experiments in optimal O(n[sup 3]) time and from POM experiments in O(n[sup 4]) time.

  19. Tree reconstruction from partial orders

    SciTech Connect

    Kannan, S.K.; Warnow, T.J.

    1993-03-01

    The problem of constructing trees given a matrix of interleaf distances is motivated by applications in computational evolutionary biology and linguistics. The general problem is to find an edge-weighted tree which most closely approximates the distance matrix. Although the construction problem is easy when the tree exactly fits the distance matrix, optimization problems under all popular criteria are either known or conjectured to be NP-complete. In this paper we consider the related problem where we are given a partial order on the pairwise distances, and wish to construct (if possible) an edge-weighted tree realizing the partial order. In particular we are interested in partial orders which arise from experiments on triples of species, which determine either a linear ordering of the three pairwise distances (called Total Order Model or TOM experiments) or only the pair(s) of minimum distance apart (called Partial Order Model or POM experiments). The POM and TOM experimental model is inspired by the model proposed by Kannan, Lawler, and Warnow for constructing trees from experiments which determine the rooted topology for any triple of species. We examine issues of construction of trees and consistency of TOM and POM experiments, where the trees may either be weighted or unweighted. Using these experiments to construct unweighted trees without nodes of degree two is motivated by a similar problem studied by Winkler, called the Discrete Metric Realization problem, which he showed to be strongly NP-hard. We have the following results: Determining consistency of a set of TOM or POM experiments is NP-Complete whether the tree is weighted or constrained to be unweighted and without degree two nodes. We can construct unweighted trees without degree two nodes from TOM experiments in optimal O(n{sup 3}) time and from POM experiments in O(n{sup 4}) time.

  20. Automation: Decision Aid or Decision Maker?

    NASA Technical Reports Server (NTRS)

    Skitka, Linda J.

    1998-01-01

    This study clarified that automation bias is something unique to automated decision making contexts, and is not the result of a general tendency toward complacency. By comparing performance on exactly the same events on the same tasks with and without an automated decision aid, we were able to determine that at least the omission error part of automation bias is due to the unique context created by having an automated decision aid, and is not a phenomena that would occur even if people were not in an automated context. However, this study also revealed that having an automated decision aid did lead to modestly improved performance across all non-error events. Participants in the non- automated condition responded with 83.68% accuracy, whereas participants in the automated condition responded with 88.67% accuracy, across all events. Automated decision aids clearly led to better overall performance when they were accurate. People performed almost exactly at the level of reliability as the automation (which across events was 88% reliable). However, also clear, is that the presence of less than 100% accurate automated decision aids creates a context in which new kinds of errors in decision making can occur. Participants in the non-automated condition responded with 97% accuracy on the six "error" events, whereas participants in the automated condition had only a 65% accuracy rate when confronted with those same six events. In short, the presence of an AMA can lead to vigilance decrements that can lead to errors in decision making.