DEFF Research Database (Denmark)
ElSalamouny, Ehab; Nielsen, Mogens; Sassone, Vladimiro
2010-01-01
Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either assume fixed behaviour for the principals or incorporate the notion of ‘decay' as an ad hoc approach to cope...... with their dynamic behaviour. Using Hidden Markov Models (HMMs) for both modelling and approximating the behaviours of principals, we introduce the HMM-based trust model as a new approach to evaluating trust in systems exhibiting dynamic behaviour. This model avoids the fixed behaviour assumption which is considered...... the major limitation of existing Beta trust model. We show the consistency of the HMM-based trust model and contrast it against the well known Beta trust model with the decay principle in terms of the estimation precision....
HMM_Model-Checker pour la vérification probabiliste HMM_Model ...
African Journals Online (AJOL)
ASSIA
probabiliste –Télescope Hubble. Abstract. Probabilistic verification for embedded systems continues to attract more and more followers in the research community. Given a probabilistic model, a formula of temporal logic, describing a property of a system and an exploration algorithm to check whether the property is satisfied ...
Study on solitary word based on HMM model and Baum-Welch algorithm
Directory of Open Access Journals (Sweden)
Junxia CHEN
Full Text Available This paper introduces the principle of Hidden Markov Model, which is used to describe the Markov process with unknown parameters, is a probability model to describe the statistical properties of the random process. On this basis, designed a solitary word detection experiment based on HMM model, by optimizing the experimental model, Using Baum-Welch algorithm for training the problem of solving the HMM model, HMM model to estimate the parameters of the λ value is found, in this view of mathematics equivalent to other linear prediction coefficient. This experiment in reducing unnecessary HMM training at the same time, reduced the algorithm complexity. In order to test the effectiveness of the Baum-Welch algorithm, The simulation of experimental data, the results show that the algorithm is effective.
Speech-To-Text Conversion STT System Using Hidden Markov Model HMM
Directory of Open Access Journals (Sweden)
Su Myat Mon
2015-06-01
Full Text Available Abstract Speech is an easiest way to communicate with each other. Speech processing is widely used in many applications like security devices household appliances cellular phones ATM machines and computers. The human computer interface has been developed to communicate or interact conveniently for one who is suffering from some kind of disabilities. Speech-to-Text Conversion STT systems have a lot of benefits for the deaf or dumb people and find their applications in our daily lives. In the same way the aim of the system is to convert the input speech signals into the text output for the deaf or dumb students in the educational fields. This paper presents an approach to extract features by using Mel Frequency Cepstral Coefficients MFCC from the speech signals of isolated spoken words. And Hidden Markov Model HMM method is applied to train and test the audio files to get the recognized spoken word. The speech database is created by using MATLAB.Then the original speech signals are preprocessed and these speech samples are extracted to the feature vectors which are used as the observation sequences of the Hidden Markov Model HMM recognizer. The feature vectors are analyzed in the HMM depending on the number of states.
Accelerated Profile HMM Searches.
Directory of Open Access Journals (Sweden)
Sean R Eddy
2011-10-01
Full Text Available Profile hidden Markov models (profile HMMs and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
Cluster-Based Adaptation Using Density Forest for HMM Phone Recognition
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll
2014-01-01
The dissimilarity between the training and test data in speech recognition systems is known to have a considerable effect on the recognition accuracy. To solve this problem, we use density forest to cluster the data and use maximum a posteriori (MAP) method to build a cluster-based adapted Gaussian...... mixture models (GMMs) in HMM speech recognition. Specifically, a set of bagged versions of the training data for each state in the HMM is generated, and each of these versions is used to generate one GMM and one tree in the density forest. Thereafter, an acoustic model forest is built by replacing...... the data of each leaf (cluster) in each tree with the corresponding GMM adapted by the leaf data using the MAP method. The results show that the proposed approach achieves 3:8% (absolute) lower phone error rate compared with the standard HMM/GMM and 0:8% (absolute) lower PER compared with bagged HMM/GMM....
HMM Adaptation for child speech synthesis
CSIR Research Space (South Africa)
Govender, Avashna
2015-09-01
Full Text Available Hidden Markov Model (HMM)-based synthesis in combination with speaker adaptation has proven to be an approach that is well-suited for child speech synthesis. This paper describes the development and evaluation of different HMM-based child speech...
E. Gregory McPherson; Paula J. Peper
2012-01-01
This paper describes three long-term tree growth studies conducted to evaluate tree performance because repeated measurements of the same trees produce critical data for growth model calibration and validation. Several empirical and process-based approaches to modeling tree growth are reviewed. Modeling is more advanced in the fields of forestry and...
Directory of Open Access Journals (Sweden)
Nandi Soumyadeep
2007-03-01
Full Text Available Abstract Background Profile Hidden Markov Models (HMM are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives. Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. Results The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors
A stochastic HMM-based forecasting model for fuzzy time series.
Li, Sheng-Tun; Cheng, Yi-Chung
2010-10-01
Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.
HMM filtering and parameter estimation of an electricity spot price model
International Nuclear Information System (INIS)
Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar
2010-01-01
In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)
HMM Logos for visualization of protein families
Directory of Open Access Journals (Sweden)
Schultz Jörg
2004-01-01
Full Text Available Abstract Background Profile Hidden Markov Models (pHMMs are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way. Results We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability and the expected number of letters the state emits during a pass through the model (the state's expected contribution. A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics http://logos.molgen.mpg.de. Conclusions We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras.
Structural Equation Model Trees
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…
Hidden Neural Networks: A Framework for HMM/NN Hybrids
DEFF Research Database (Denmark)
Riis, Søren Kamaric; Krogh, Anders Stærmose
1997-01-01
This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....
An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition
Directory of Open Access Journals (Sweden)
Ing-Jr Ding
2014-01-01
Full Text Available In the past, the kernel of automatic speech recognition (ASR is dynamic time warping (DTW, which is feature-based template matching and belongs to the category technique of dynamic programming (DP. Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM- like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation. A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.
Directory of Open Access Journals (Sweden)
Ririn Kusumawati
2016-05-01
In the classification, using Hidden Markov Model, voice signal is analyzed and searched the maximum possible value that can be recognized. The modeling results obtained parameters are used to compare with the sound of Arabic speakers. From the test results' Classification, Hidden Markov Models with Linear Predictive Coding extraction average accuracy of 78.6% for test data sampling frequency of 8,000 Hz, 80.2% for test data sampling frequency of 22050 Hz, 79% for frequencies sampling test data at 44100 Hz.
AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.
Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo
2017-09-21
Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.
Effect of HMM Glutenin Subunits on Wheat Quality Attributes
Directory of Open Access Journals (Sweden)
Daniela Horvat
2009-01-01
Full Text Available Glutenin is a group of polymeric gluten proteins. Glutenin molecules consist of glutenin subunits linked together with disulphide bonds and having higher (HMM-GS and lower (LMM-GS molecular mass. The main objective of this study is the evaluation of the influence of HMM-GS on flour processing properties. Seven bread wheat genotypes with contrasting quality attributes and different HMM-GS composition were analyzed during three years. The composition and quantity of HMM-GS were determined by SDS-PAGE and RP-HPLC, respectively. The quality diversity among genotypes was estimated by the analysis of wheat grain, and flour and bread quality parameters. The presence of HMM glutenin subunits 1 and 2* at Glu-A1 and the subunits 5+10 at Glu-D1 loci, as well as a higher proportion of total HMM-GS, had a positive effect on wheat quality. Cluster analysis of the three groups of data (genotype and HMM-GS, flour and bread quality, and dough rheology yielded the same hierarchical structure for the first top three levels, and similarity of the corresponding dendrograms was proved by the principal eigenvalues of the corresponding Euclidian distance matrices. The obtained similarity in classification based on essentially different types of measurements reflects strong natural association between genetic data, product quality and physical properties. Principal component analysis (PCA was applied to effectively reduce large data set into lower dimensions of latent variables amenable for the analysis. PCA analysis of the total set of data (15 variables revealed a very strong interrelationship between the variables. The first three PCA components accounted for 96 % of the total variance, which was significant to the level of 0.05 and was considered as the level of experimental error. These data imply that the quality of wheat cultivars can be contributed to HMM-GS data and should be taken into account in breeding programs assisted by computer models with the aim to
Important factors in HMM-based phonetic segmentation
CSIR Research Space (South Africa)
Van Niekerk, DR
2007-11-01
Full Text Available , window and step sizes. Taking into account that the segmentation system trains and applies the HMM models on a single speaker only, our first con- cern was the applicability of the window and step sizes that are commonly used for speech recognition...
Appropriate baseline values for HMM-based speech recognition
CSIR Research Space (South Africa)
Barnard, E
2004-11-01
Full Text Available A number of issues realted to the development of speech-recognition systems with Hidden Markov Models (HMM) are discussed. A set of systematic experiments using the HTK toolkit and the TMIT database are used to elucidate matters such as the number...
Modelling tree biomasses in Finland
Energy Technology Data Exchange (ETDEWEB)
Repola, J.
2013-06-01
Biomass equations for above- and below-ground tree components of Scots pine (Pinus sylvestris L), Norway spruce (Picea abies [L.] Karst) and birch (Betula pendula Roth and Betula pubescens Ehrh.) were compiled using empirical material from a total of 102 stands. These stands (44 Scots pine, 34 Norway spruce and 24 birch stands) were located mainly on mineral soil sites representing a large part of Finland. The biomass models were based on data measured from 1648 sample trees, comprising 908 pine, 613 spruce and 127 birch trees. Biomass equations were derived for the total above-ground biomass and for the individual tree components: stem wood, stem bark, living and dead branches, needles, stump, and roots, as dependent variables. Three multivariate models with different numbers of independent variables for above-ground biomass and one for below-ground biomass were constructed. Variables that are normally measured in forest inventories were used as independent variables. The simplest model formulations, multivariate models (1) were mainly based on tree diameter and height as independent variables. In more elaborated multivariate models, (2) and (3), additional commonly measured tree variables such as age, crown length, bark thickness and radial growth rate were added. Tree biomass modelling includes consecutive phases, which cause unreliability in the prediction of biomass. First, biomasses of sample trees should be determined reliably to decrease the statistical errors caused by sub-sampling. In this study, methods to improve the accuracy of stem biomass estimates of the sample trees were developed. In addition, the reliability of the method applied to estimate sample-tree crown biomass was tested, and no systematic error was detected. Second, the whole information content of data should be utilized in order to achieve reliable parameter estimates and applicable and flexible model structure. In the modelling approach, the basic assumption was that the biomasses of
Decision tree modeling using R.
Zhang, Zhongheng
2016-08-01
In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.
HMM based automated wheelchair navigation using EOG traces in EEG
Aziz, Fayeem; Arof, Hamzah; Mokhtar, Norrima; Mubin, Marizan
2014-10-01
This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.
Interactive wood combustion for botanical tree models
Pirk, Sö ren; Jarząbek, Michał; Hadrich, Torsten; Michels, Dominik L.; Palubicki, Wojciech
2017-01-01
We present a novel method for the combustion of botanical tree models. Tree models are represented as connected particles for the branching structure and a polygonal surface mesh for the combustion. Each particle stores biological and physical
An HMM posterior decoder for sequence feature prediction that includes homology information
DEFF Research Database (Denmark)
Käll, Lukas; Krogh, Anders Stærmose; Sonnhammer, Erik L. L.
2005-01-01
Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabil......Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines......://phobius.cgb.ki.se/poly.html . An implementation of the algorithm is available on request from the authors....
Finiteness results for Abelian tree models
Draisma, J.; Eggermont, R.H.
2015-01-01
Equivariant tree models are statistical models used in the reconstruction of phylogenetic trees from genetic data. Here equivariant refers to a symmetry group imposed on the root distribution and on the transition matrices in the model. We prove that if that symmetry group is Abelian, then the
Finiteness results for Abelian tree models
Draisma, J.; Eggermont, R.H.
2012-01-01
Equivariant tree models are statistical models used in the reconstruction of phylogenetic trees from genetic data. Here equivariant refers to a symmetry group imposed on the root distribution and on the transition matrices in the model. We prove that if that symmetry group is Abelian, then the
Finiteness results for Abelian tree models
Draisma, J.; Eggermont, R.H.
2015-01-01
Equivariant tree models are statistical models used in the reconstruction of phylogenetic trees from genetic data. Here equivariant§ refers to a symmetry group imposed on the root distribution and on the transition matrices in the model. We prove that if that symmetry group is Abelian, then the
Explorations in the History of Machines and Mechanisms : Proceedings of HMM2012
Ceccarelli, Marco
2012-01-01
This book contains the proceedings of HMM2012, the 4th International Symposium on Historical Developments in the field of Mechanism and Machine Science (MMS). These proceedings cover recent research concerning all aspects of the development of MMS from antiquity until the present and its historiography: machines, mechanisms, kinematics, dynamics, concepts and theories, design methods, collections of methods, collections of models, institutions and biographies.
Objective measures to improve the selection of training speakers in HMM-based child speech synthesis
CSIR Research Space (South Africa)
Govender, Avashna
2016-12-01
Full Text Available Building synthetic child voices is considered a difficult task due to the challenges associated with data collection. As a result, speaker adaptation in conjunction with Hidden Markov Model (HMM)-based synthesis has become prevalent in this domain...
Interactive wood combustion for botanical tree models
Pirk, Sören
2017-11-22
We present a novel method for the combustion of botanical tree models. Tree models are represented as connected particles for the branching structure and a polygonal surface mesh for the combustion. Each particle stores biological and physical attributes that drive the kinetic behavior of a plant and the exothermic reaction of the combustion. Coupled with realistic physics for rods, the particles enable dynamic branch motions. We model material properties, such as moisture and charring behavior, and associate them with individual particles. The combustion is efficiently processed in the surface domain of the tree model on a polygonal mesh. A user can dynamically interact with the model by initiating fires and by inducing stress on branches. The flames realistically propagate through the tree model by consuming the available resources. Our method runs at interactive rates and supports multiple tree instances in parallel. We demonstrate the effectiveness of our approach through numerous examples and evaluate its plausibility against the combustion of real wood samples.
Comprehensive decision tree models in bioinformatics.
Directory of Open Access Journals (Sweden)
Gregor Stiglic
Full Text Available 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
Comprehensive decision tree models in bioinformatics.
Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter
2012-01-01
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. 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. 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. 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 attributes and a high number of possibly
Spotting handwritten words and REGEX using a two stage BLSTM-HMM architecture
Bideault, Gautier; Mioulet, Luc; Chatelain, Clément; Paquet, Thierry
2015-01-01
In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.
Parameter identification in multinomial processing tree models
Schmittmann, V.D.; Dolan, C.V.; Raijmakers, M.E.J.; Batchelder, W.H.
2010-01-01
Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis
Directory of Open Access Journals (Sweden)
El Moubtahij Hicham
2017-12-01
Full Text Available This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM Toolkit (HTK without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK. The simple database âArabic-Numbersâ and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM Toolkit (HTK, Sliding windows
Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR
Hagen, Astrid; Morris, Andrew
2000-01-01
In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood--based MGMs...
Directory of Open Access Journals (Sweden)
Han Kyusuk
2011-01-01
Full Text Available This paper introduces novel attack detection approaches on mobile and wireless device security and network which consider temporal relations between internet packets. In this paper we first present a field selection technique using a Genetic Algorithm and generate a Packet-based Mining Association Rule from an original Mining Association Rule for Support Vector Machine in mobile and wireless network environment. Through the preprocessing with PMAR, SVM inputs can account for time variation between packets in mobile and wireless network. Third, we present Gaussian observation Hidden Markov Model to exploit the hidden relationships between packets based on probabilistic estimation. In our G-HMM approach, we also apply G-HMM feature reduction for better initialization. We demonstrate the usefulness of our SVM and G-HMM approaches with GA on MIT Lincoln Lab datasets and a live dataset that we captured on a real mobile and wireless network. Moreover, experimental results are verified by -fold cross-validation test.
Comparison of HMM and DTW methods in automatic recognition of pathological phoneme pronunciation
Wielgat, Robert; Zielinski, Tomasz P.; Swietojanski, Pawel; Zoladz, Piotr; Król, Daniel; Wozniak, Tomasz; Grabias, Stanislaw
2007-01-01
In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) are used as classifiers in the presented research. Obtained resul...
Workflow Fault Tree Generation Through Model Checking
DEFF Research Database (Denmark)
Herbert, Luke Thomas; Sharp, Robin
2014-01-01
We present a framework for the automated generation of fault trees from models of realworld process workflows, expressed in a formalised subset of the popular Business Process Modelling and Notation (BPMN) language. To capture uncertainty and unreliability in workflows, we extend this formalism...
Guideliness for system modeling: fault tree [analysis
Energy Technology Data Exchange (ETDEWEB)
Lee, Yoon Hwan; Yang, Joon Eon; Kang, Dae Il; Hwang, Mee Jeong
2004-07-01
This document, the guidelines for system modeling related to Fault Tree Analysis(FTA), is intended to provide the guidelines with the analyzer to construct the fault trees in the level of the capability category II of ASME PRA standard. Especially, they are to provide the essential and basic guidelines and the related contents to be used in support of revising the Ulchin 3 and 4 PSA model for risk monitor within the capability category II of ASME PRA standard. Normally the main objective of system analysis is to assess the reliability of system modeled by Event Tree Analysis (ETA). A variety of analytical techniques can be used for the system analysis, however, FTA method is used in this procedures guide. FTA is the method used for representing the failure logic of plant systems deductively using AND, OR or NOT gates. The fault tree should reflect all possible failure modes that may contribute to the system unavailability. This should include contributions due to the mechanical failures of the components, Common Cause Failures (CCFs), human errors and outages for testing and maintenance. This document identifies and describes the definitions and the general procedures of FTA and the essential and basic guidelines for reving the fault trees. Accordingly, the guidelines for FTA will be capable to guide the FTA to the level of the capability category II of ASME PRA standard.
Guideliness for system modeling: fault tree [analysis
International Nuclear Information System (INIS)
Lee, Yoon Hwan; Yang, Joon Eon; Kang, Dae Il; Hwang, Mee Jeong
2004-07-01
This document, the guidelines for system modeling related to Fault Tree Analysis(FTA), is intended to provide the guidelines with the analyzer to construct the fault trees in the level of the capability category II of ASME PRA standard. Especially, they are to provide the essential and basic guidelines and the related contents to be used in support of revising the Ulchin 3 and 4 PSA model for risk monitor within the capability category II of ASME PRA standard. Normally the main objective of system analysis is to assess the reliability of system modeled by Event Tree Analysis (ETA). A variety of analytical techniques can be used for the system analysis, however, FTA method is used in this procedures guide. FTA is the method used for representing the failure logic of plant systems deductively using AND, OR or NOT gates. The fault tree should reflect all possible failure modes that may contribute to the system unavailability. This should include contributions due to the mechanical failures of the components, Common Cause Failures (CCFs), human errors and outages for testing and maintenance. This document identifies and describes the definitions and the general procedures of FTA and the essential and basic guidelines for reving the fault trees. Accordingly, the guidelines for FTA will be capable to guide the FTA to the level of the capability category II of ASME PRA standard
An improved segmentation-based HMM learning method for Condition-based Maintenance
International Nuclear Information System (INIS)
Liu, T; Lemeire, J; Cartella, F; Meganck, S
2012-01-01
In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.
"Growing trees backwards": Description of a stand reconstruction model
Jonathan D. Bakker; Andrew J. Sanchez Meador; Peter Z. Fule; David W. Huffman; Margaret M. Moore
2008-01-01
We describe an individual-tree model that uses contemporary measurements to "grow trees backward" and reconstruct past tree diameters and stand structure in ponderosa pine dominated stands of the Southwest. Model inputs are contemporary structural measurements of all snags, logs, stumps, and living trees, and radial growth measurements, if available. Key...
New substitution models for rooting phylogenetic trees.
Williams, Tom A; Heaps, Sarah E; Cherlin, Svetlana; Nye, Tom M W; Boys, Richard J; Embley, T Martin
2015-09-26
The root of a phylogenetic tree is fundamental to its biological interpretation, but standard substitution models do not provide any information on its position. Here, we describe two recently developed models that relax the usual assumptions of stationarity and reversibility, thereby facilitating root inference without the need for an outgroup. We compare the performance of these models on a classic test case for phylogenetic methods, before considering two highly topical questions in evolutionary biology: the deep structure of the tree of life and the root of the archaeal radiation. We show that all three alignments contain meaningful rooting information that can be harnessed by these new models, thus complementing and extending previous work based on outgroup rooting. In particular, our analyses exclude the root of the tree of life from the eukaryotes or Archaea, placing it on the bacterial stem or within the Bacteria. They also exclude the root of the archaeal radiation from several major clades, consistent with analyses using other rooting methods. Overall, our results demonstrate the utility of non-reversible and non-stationary models for rooting phylogenetic trees, and identify areas where further progress can be made. © 2015 The Authors.
Modelling diameter growth, mortality and recruitment of trees in ...
African Journals Online (AJOL)
Modelling diameter growth, mortality and recruitment of trees in miombo woodlands of Tanzania. ... Individual tree diameter growth and mortality models, and area-based recruitment models were developed. ... AJOL African Journals Online.
Pesticide bioconcentration modelling for fruit trees.
Paraíba, Lourival Costa
2007-01-01
The model presented allows simulating the pesticide concentration evolution in fruit trees and estimating the pesticide bioconcentration factor in fruits. Pesticides are non-ionic organic compounds that are degraded in soils cropped with woody species, fruit trees and other perennials. The model allows estimating the pesticide uptake by plants through the water transpiration stream and also the time in which maximum pesticide concentration occur in the fruits. The equation proposed presents the relationships between bioconcentration factor (BCF) and the following variables: plant water transpiration volume (Q), pesticide transpiration stream concentration factor (TSCF), pesticide stem-water partition coefficient (K(Wood,W)), stem dry biomass (M) and pesticide dissipation rate in the soil-plant system (k(EGS)). The modeling started and was developed from a previous model "Fruit Tree Model" (FTM), reported by Trapp and collaborators in 2003, to which was added the hypothesis that the pesticide degradation in the soil follows a first order kinetic equation. The FTM model for pesticides (FTM-p) was applied to a hypothetic mango plant cropping (Mangifera indica) treated with paclobutrazol (growth regulator) added to the soil. The model fitness was evaluated through the sensitivity analysis of the pesticide BCF values in fruits with respect to the model entry data variability.
Tree-Structured Digital Organisms Model
Suzuki, Teruhiko; Nobesawa, Shiho; Tahara, Ikuo
Tierra and Avida are well-known models of digital organisms. They describe a life process as a sequence of computation codes. A linear sequence model may not be the only way to describe a digital organism, though it is very simple for a computer-based model. Thus we propose a new digital organism model based on a tree structure, which is rather similar to the generic programming. With our model, a life process is a combination of various functions, as if life in the real world is. This implies that our model can easily describe the hierarchical structure of life, and it can simulate evolutionary computation through mutual interaction of functions. We verified our model by simulations that our model can be regarded as a digital organism model according to its definitions. Our model even succeeded in creating species such as viruses and parasites.
Constructing Dynamic Event Trees from Markov Models
International Nuclear Information System (INIS)
Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood
2006-01-01
In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: (a) partitioning the process variable state space into magnitude intervals (cells), (b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, (c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank
Modeling Trees with a Space Colonization Algorithm
Morell Higueras, Marc
2014-01-01
[CATALÀ] Aquest TFG tracta la implementació d'un algorisme de generació procedural que construeixi una estructura reminiscent a la d'un arbre de clima temperat, i també la implementació del pas de l'estructura a un model tridimensional, acompanyat de l'eina per a visualitzar el resultat i fer-ne l'exportació [ANGLÈS] This TFG consists of the implementation of a procedural generation algorithm that builds a structure reminiscent of that of a temperate climate tree, and also consists of the ...
Shavit Grievink, Liat; Penny, David; Hendy, Michael D; Holland, Barbara R
2010-05-01
Commonly used phylogenetic models assume a homogeneous process through time in all parts of the tree. However, it is known that these models can be too simplistic as they do not account for nonhomogeneous lineage-specific properties. In particular, it is now widely recognized that as constraints on sequences evolve, the proportion and positions of variable sites can vary between lineages causing heterotachy. The extent to which this model misspecification affects tree reconstruction is still unknown. Here, we evaluate the effect of changes in the proportions and positions of variable sites on model fit and tree estimation. We consider 5 current models of nucleotide sequence evolution in a Bayesian Markov chain Monte Carlo framework as well as maximum parsimony (MP). We show that for a tree with 4 lineages where 2 nonsister taxa undergo a change in the proportion of variable sites tree reconstruction under the best-fitting model, which is chosen using a relative test, often results in the wrong tree. In this case, we found that an absolute test of model fit is a better predictor of tree estimation accuracy. We also found further evidence that MP is not immune to heterotachy. In addition, we show that increased sampling of taxa that have undergone a change in proportion and positions of variable sites is critical for accurate tree reconstruction.
Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM
International Nuclear Information System (INIS)
Liu, T; Chen, J; Zhou, X N; Xiao, W B
2012-01-01
Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.
Online adaptive learning of Left-Right Continuous HMM for bearings condition assessment
International Nuclear Information System (INIS)
Cartella, F; Liu, T; Meganck, S; Lemeire, J; Sahli, H
2012-01-01
Standard Hidden Markov Models (HMMs) approaches used for condition assessment of bearings assume that all the possible system states are fixed and known a priori and that training data from all of the associated states are available. Moreover, the training procedure is performed offline, and only once at the beginning, with the available training set. These assumptions significantly impede component diagnosis applications when all of the possible states of the system are not known in advance or environmental factors or operative conditions change during the tool's usage. The method introduced in this paper overcomes the above limitations and proposes an approach to detect unknown degradation modalities using a Left-Right Continuous HMM with a variable state space. The proposed HMM is combined with Change Point Detection algorithms to (i) estimate, from historical observations, the initial number of the model's states, as well as to perform an initial guess of the parameters, and (ii) to adaptively recognize new states and, consequently, adjust the model parameters during monitoring. The approach has been tested using real monitoring data taken from the NASA benchmark repository. A comparative study with state of the art techniques shows improvements in terms of reduction of the training procedure iterations, and early detection of unknown states.
Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization
Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.
2011-01-01
One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.
Computational model of a whole tree combustor
Energy Technology Data Exchange (ETDEWEB)
Bryden, K.M.; Ragland, K.W. [Univ. of Wisconsin, Madison, WI (United States)
1993-12-31
A preliminary computational model has been developed for the whole tree combustor and compared to test results. In the simulation model presented hardwood logs, 15 cm in diameter are burned in a 4 m deep fuel bed. Solid and gas temperature, solid and gas velocity, CO, CO{sub 2}, H{sub 2}O, HC and O{sub 2} profiles are calculated. This deep, fixed bed combustor obtains high energy release rates per unit area due to the high inlet air velocity and extended reaction zone. The lowest portion of the overall bed is an oxidizing region and the remainder of the bed acts as a gasification and drying region. The overfire air region completes the combustion. Approximately 40% of the energy is released in the lower oxidizing region. The wood consumption rate obtained from the computational model is 4,110 kg/m{sup 2}-hr which matches well the consumption rate of 3,770 kg/m{sup 2}-hr observed during the peak test period of the Aurora, MN test. The predicted heat release rate is 16 MW/m{sup 2} (5.0*10{sup 6} Btu/hr-ft{sup 2}).
Modeling Caribbean tree stem diameters from tree height and crown width measurements
Thomas Brandeis; KaDonna Randolph; Mike Strub
2009-01-01
Regression models to predict diameter at breast height (DBH) as a function of tree height and maximum crown radius were developed for Caribbean forests based on data collected by the U.S. Forest Service in the Commonwealth of Puerto Rico and Territory of the U.S. Virgin Islands. The model predicting DBH from tree height fit reasonably well (R2 = 0.7110), with...
Total tree, merchantable stem and branch volume models for ...
African Journals Online (AJOL)
Total tree, merchantable stem and branch volume models for miombo woodlands of Malawi. Daud J Kachamba, Tron Eid. Abstract. The objective of this study was to develop general (multispecies) models for prediction of total tree, merchantable stem and branch volume including options with diameter at breast height (dbh) ...
Modeling percent tree canopy cover: a pilot study
John W. Coulston; Gretchen G. Moisen; Barry T. Wilson; Mark V. Finco; Warren B. Cohen; C. Kenneth Brewer
2012-01-01
Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006,...
Algorithmic fault tree construction by component-based system modeling
International Nuclear Information System (INIS)
Majdara, Aref; Wakabayashi, Toshio
2008-01-01
Computer-aided fault tree generation can be easier, faster and less vulnerable to errors than the conventional manual fault tree construction. In this paper, a new approach for algorithmic fault tree generation is presented. The method mainly consists of a component-based system modeling procedure an a trace-back algorithm for fault tree synthesis. Components, as the building blocks of systems, are modeled using function tables and state transition tables. The proposed method can be used for a wide range of systems with various kinds of components, if an inclusive component database is developed. (author)
Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning
Directory of Open Access Journals (Sweden)
An Luo
2017-10-01
Full Text Available Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.
Developing Models to Forcast Sales of Natural Christmas Trees
Lawrence D. Garrett; Thomas H. Pendleton
1977-01-01
A study of practices for marketing Christmas trees in Winston-Salem, North Carolina, and Denver, Colorado, revealed that such factors as retail lot competition, tree price, consumer traffic, and consumer income were very important in determining a particular retailer's sales. Analyses of 4 years of market data were used in developing regression models for...
An HMM-based comparative genomic framework for detecting introgression in eukaryotes.
Directory of Open Access Journals (Sweden)
Kevin J Liu
2014-06-01
Full Text Available One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on PhyloNet-HMM-a new comparative genomic framework for detecting introgression in genomes. PhyloNet-HMM combines phylogenetic networks with hidden Markov models (HMMs to simultaneously capture the (potentially reticulate evolutionary history of the genomes and dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus genome detected a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgressed genomic regions. Based on our analysis, it is estimated that about 9% of all sites within chromosome 7 are of introgressive origin (these cover about 13 Mbp of chromosome 7, and over 300 genes. Further, our model detected no introgression in a negative control data set. We also found that our model accurately detected introgression and other evolutionary processes from synthetic data sets simulated under the coalescent model with recombination, isolation, and migration. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism.
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.
Directory of Open Access Journals (Sweden)
Rocco Pace
2018-02-01
Full Text Available Ecosystem modeling can help decision making regarding planting of urban trees for climate change mitigation and air pollution reduction. Algorithms and models that link the properties of plant functional types, species groups, or single species to their impact on specific ecosystem services have been developed. However, these models require a considerable effort for initialization that is inherently related to uncertainties originating from the high diversity of plant species in urban areas. We therefore suggest a new automated method to be used with the i-Tree Eco model to derive light competition for individual trees and investigate the importance of this property. Since competition depends also on the species, which is difficult to determine from increasingly used remote sensing methodologies, we also investigate the impact of uncertain tree species classification on the ecosystem services by comparing a species-specific inventory determined by field observation with a genus-specific categorization and a model initialization for the dominant deciduous and evergreen species only. Our results show how the simulation of competition affects the determination of carbon sequestration, leaf area, and related ecosystem services and that the proposed method provides a tool for improving estimations. Misclassifications of tree species can lead to large deviations in estimates of ecosystem impacts, particularly concerning biogenic volatile compound emissions. In our test case, monoterpene emissions almost doubled and isoprene emissions decreased to less than 10% when species were estimated to belong only to either two groups instead of being determined by species or genus. It is discussed that this uncertainty of emission estimates propagates further uncertainty in the estimation of potential ozone formation. Overall, we show the importance of using an individual light competition approach and explicitly parameterizing all ecosystem functions at the
Fruit tree model for uptake of organic compounds from soil
DEFF Research Database (Denmark)
Trapp, Stefan; Rasmussen, D.; Samsoe-Petersen, L.
2003-01-01
-state, and an example calculation is given. The Fruit Tree Model is compared to the empirical equation of Travis and Arms (T&A), and to results from fruits, collected in contaminated areas. For polar compounds, both T&A and the Fruit Tree Model predict bioconcentration factors fruit to soil (BCF, wet weight based......) of > 1. No empirical data are available to support this prediction. For very lipophilic compounds (log K-OW > 5), T&A overestimates the uptake. The conclusion from the Fruit Tree Model is that the transfer of lipophilic compounds into fruits is not relevant. This was also found by an empirical study...... with PCDD/F. According to the Fruit Tree Model, polar chemicals are transferred efficiently into fruits, but empirical data to verify these predictions are lacking....
Maximum parsimony, substitution model, and probability phylogenetic trees.
Weng, J F; Thomas, D A; Mareels, I
2011-01-01
The problem of inferring phylogenies (phylogenetic trees) is one of the main problems in computational biology. There are three main methods for inferring phylogenies-Maximum Parsimony (MP), Distance Matrix (DM) and Maximum Likelihood (ML), of which the MP method is the most well-studied and popular method. In the MP method the optimization criterion is the number of substitutions of the nucleotides computed by the differences in the investigated nucleotide sequences. However, the MP method is often criticized as it only counts the substitutions observable at the current time and all the unobservable substitutions that really occur in the evolutionary history are omitted. In order to take into account the unobservable substitutions, some substitution models have been established and they are now widely used in the DM and ML methods but these substitution models cannot be used within the classical MP method. Recently the authors proposed a probability representation model for phylogenetic trees and the reconstructed trees in this model are called probability phylogenetic trees. One of the advantages of the probability representation model is that it can include a substitution model to infer phylogenetic trees based on the MP principle. In this paper we explain how to use a substitution model in the reconstruction of probability phylogenetic trees and show the advantage of this approach with examples.
Fault diagnosis of nuclear-powered equipment based on HMM and SVM
International Nuclear Information System (INIS)
Yue Xia; Zhang Chunliang; Zhu Houyao; Quan Yanming
2012-01-01
For the complexity and the small fault samples of nuclear-powered equipment, a hybrid HMM/SVM method was introduced in fault diagnosis. The hybrid method has two steps: first, HMM is utilized for primary diagnosis, in which the range of possible failure is reduced and the state trends can be observed; then faults can be recognized taking the advantage of the generalization ability of SVM. Experiments on the main pump failure simulator show that the HMM/SVM system has a high recognition rate and can be used in the fault diagnosis of nuclear-powered equipment. (authors)
Context Tree Estimation in Variable Length Hidden Markov Models
Dumont, Thierry
2011-01-01
We address the issue of context tree estimation in variable length hidden Markov models. We propose an estimator of the context tree of the hidden Markov process which needs no prior upper bound on the depth of the context tree. We prove that the estimator is strongly consistent. This uses information-theoretic mixture inequalities in the spirit of Finesso and Lorenzo(Consistent estimation of the order for Markov and hidden Markov chains(1990)) and E.Gassiat and S.Boucheron (Optimal error exp...
Modeling and Testing Landslide Hazard Using Decision Tree
Directory of Open Access Journals (Sweden)
Mutasem Sh. Alkhasawneh
2014-01-01
Full Text Available This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID, Exhaustive CHAID, Classification and Regression Tree (CRT, and Quick-Unbiased-Efficient Statistical Tree (QUEST. Twenty-one factors were extracted using digital elevation models (DEMs and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0% compared to CHAID (81.9%, CRT (75.6%, and QUEST (74.0% model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.
Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach
Klauer, Karl Christoph
2010-01-01
Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…
Stem biomass and volume models of selected tropical tree species ...
African Journals Online (AJOL)
Stem biomass and stem volume were modelled as a function of diameter (at breast height; Dbh) and stem height (height to the crown base). Logarithmic models are presented that utilise Dbh and height data to predict tree component biomass and stem volumes. Alternative models are given that afford prediction based on ...
Context trees for privacy-preserving modeling of genetic data
Kusters, C.J.; Ignatenko, T.
2016-01-01
In this work, we use context trees for privacypreserving modeling of genetic sequences. The resulting estimated models are applied for functional comparison of genetic sequences in a privacy preserving way. Here we define privacy as uncertainty about the genetic source sequence given its model and
A tree-parenchyma coupled model for lung ventilation simulation.
Pozin, Nicolas; Montesantos, Spyridon; Katz, Ira; Pichelin, Marine; Vignon-Clementel, Irene; Grandmont, Céline
2017-11-01
In this article, we develop a lung ventilation model. The parenchyma is described as an elastic homogenized media. It is irrigated by a space-filling dyadic resistive pipe network, which represents the tracheobronchial tree. In this model, the tree and the parenchyma are strongly coupled. The tree induces an extra viscous term in the system constitutive relation, which leads, in the finite element framework, to a full matrix. We consider an efficient algorithm that takes advantage of the tree structure to enable a fast matrix-vector product computation. This framework can be used to model both free and mechanically induced respiration, in health and disease. Patient-specific lung geometries acquired from computed tomography scans are considered. Realistic Dirichlet boundary conditions can be deduced from surface registration on computed tomography images. The model is compared to a more classical exit compartment approach. Results illustrate the coupling between the tree and the parenchyma, at global and regional levels, and how conditions for the purely 0D model can be inferred. Different types of boundary conditions are tested, including a nonlinear Robin model of the surrounding lung structures. Copyright © 2017 John Wiley & Sons, Ltd.
Biomass models to estimate carbon stocks for hardwood tree species
Energy Technology Data Exchange (ETDEWEB)
Ruiz-Peinado, R.; Montero, G.; Rio, M. del
2012-11-01
To estimate forest carbon pools from forest inventories it is necessary to have biomass models or biomass expansion factors. In this study, tree biomass models were developed for the main hardwood forest species in Spain: Alnus glutinosa, Castanea sativa, Ceratonia siliqua, Eucalyptus globulus, Fagus sylvatica, Fraxinus angustifolia, Olea europaea var. sylvestris, Populus x euramericana, Quercus canariensis, Quercus faginea, Quercus ilex, Quercus pyrenaica and Quercus suber. Different tree biomass components were considered: stem with bark, branches of different sizes, above and belowground biomass. For each species, a system of equations was fitted using seemingly unrelated regression, fulfilling the additivity property between biomass components. Diameter and total height were explored as independent variables. All models included tree diameter whereas for the majority of species, total height was only considered in the stem biomass models and in some of the branch models. The comparison of the new biomass models with previous models fitted separately for each tree component indicated an improvement in the accuracy of the models. A mean reduction of 20% in the root mean square error and a mean increase in the model efficiency of 7% in comparison with recently published models. So, the fitted models allow estimating more accurately the biomass stock in hardwood species from the Spanish National Forest Inventory data. (Author) 45 refs.
Dynamic Ising model: reconstruction of evolutionary trees
International Nuclear Information System (INIS)
De Oliveira, P M C
2013-01-01
An evolutionary tree is a cascade of bifurcations starting from a single common root, generating a growing set of daughter species as time goes by. ‘Species’ here is a general denomination for biological species, spoken languages or any other entity which evolves through heredity. From the N currently alive species within a clade, distances are measured through pairwise comparisons made by geneticists, linguists, etc. The larger is such a distance that, for a pair of species, the older is their last common ancestor. The aim is to reconstruct the previously unknown bifurcations, i.e. the whole clade, from knowledge of the N(N − 1)/2 quoted distances, which are taken for granted. A mechanical method is presented and its applicability is discussed. (paper)
Runtime Optimizations for Tree-Based Machine Learning Models
N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)
2014-01-01
htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression
Discrete Discriminant analysis based on tree-structured graphical models
DEFF Research Database (Denmark)
Perez de la Cruz, Gonzalo; Eslava, Guillermina
The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant a...... analysis based on tree{structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well{known linear methods like those assuming mutual independence between variables and linear logistic regression.......The purpose of this paper is to illustrate the potential use of discriminant analysis based on tree{structured graphical models for discrete variables. This is done by comparing its empirical performance using estimated error rates for real and simulated data. The results show that discriminant...
Selective Tree-ring Models: A Novel Method for Reconstructing Streamflow Using Tree Rings
Foard, M. B.; Nelson, A. S.; Harley, G. L.
2017-12-01
Surface water is among the most instrumental and vulnerable resources in the Northwest United States (NW). Recent observations show that overall water quantity is declining in streams across the region, while extreme flooding events occur more frequently. Historical streamflow models inform probabilities of extreme flow events (flood or drought) by describing frequency and duration of past events. There are numerous examples of tree-rings being utilized to reconstruct streamflow in the NW. These models confirm that tree-rings are highly accurate at predicting streamflow, however there are many nuances that limit their applicability through time and space. For example, most models predict streamflow from hydrologically altered rivers (e.g. dammed, channelized) which may hinder our ability to predict natural prehistoric flow. They also have a tendency to over/under-predict extreme flow events. Moreover, they often neglect to capture the changing relationships between tree-growth and streamflow over time and space. To address these limitations, we utilized national tree-ring and streamflow archives to investigate the relationships between the growth of multiple coniferous species and free-flowing streams across the NW using novel species-and site-specific streamflow models - a term we coined"selective tree-ring models." Correlation function analysis and regression modeling were used to evaluate the strengths and directions of the flow-growth relationships. Species with significant relationships in the same direction were identified as strong candidates for selective models. Temporal and spatial patterns of these relationships were examined using running correlations and inverse distance weighting interpolation, respectively. Our early results indicate that (1) species adapted to extreme climates (e.g. hot-dry, cold-wet) exhibit the most consistent relationships across space, (2) these relationships weaken in locations with mild climatic variability, and (3) some
International Nuclear Information System (INIS)
Kee, Ernest J.; Sun, Alice; Rodgers, Shawn; Popova, ElmiraV; Nelson, Paul; Moiseytseva, Vera; Wang, Eric
2006-01-01
South Texas Project uses a large fault tree to produce scenarios (minimal cut sets) used in quantification of plant availability and event frequency predictions. On the other hand, the South Texas Project probabilistic risk assessment model uses a large event tree, small fault tree for quantifying core damage and radioactive release frequency predictions. The South Texas Project is converting its availability and event frequency model to use a large event tree, small fault in an effort to streamline application support and to provide additional detail in results. The availability and event frequency model as well as the applications it supports (maintenance and operational risk management, system engineering health assessment, preventive maintenance optimization, and RIAM) are briefly described. A methodology to perform availability modeling in a large event tree, small fault tree framework is described in detail. How the methodology can be used to support South Texas Project maintenance and operations risk management is described in detail. Differences with other fault tree methods and other recently proposed methods are discussed in detail. While the methods described are novel to the South Texas Project Risk Management program and to large event tree, small fault tree models, concepts in the area of application support and availability modeling have wider applicability to the industry. (authors)
Verification of Fault Tree Models with RBDGG Methodology
International Nuclear Information System (INIS)
Kim, Man Cheol
2010-01-01
Currently, fault tree analysis is widely used in the field of probabilistic safety assessment (PSA) of nuclear power plants (NPPs). To guarantee the correctness of fault tree models, which are usually manually constructed by analysts, a review by other analysts is widely used for verifying constructed fault tree models. Recently, an extension of the reliability block diagram was developed, which is named as RBDGG (reliability block diagram with general gates). The advantage of the RBDGG methodology is that the structure of a RBDGG model is very similar to the actual structure of the analyzed system and, therefore, the modeling of a system for a system reliability and unavailability analysis becomes very intuitive and easy. The main idea of the development of the RBDGG methodology is similar to that of the development of the RGGG (Reliability Graph with General Gates) methodology. The difference between the RBDGG methodology and RGGG methodology is that the RBDGG methodology focuses on the block failures while the RGGG methodology focuses on the connection line failures. But, it is also known that an RGGG model can be converted to an RBDGG model and vice versa. In this paper, a new method for the verification of the constructed fault tree models using the RBDGG methodology is proposed and demonstrated
Directory of Open Access Journals (Sweden)
Stefanie M. Herrmann
2013-10-01
Full Text Available Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1 a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview as the dependent variable, and (2 ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS Normalized Difference Vegetation Index (NDVI composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.
Modeling tree crown dynamics with 3D partial differential equations.
Beyer, Robert; Letort, Véronique; Cournède, Paul-Henry
2014-01-01
We characterize a tree's spatial foliage distribution by the local leaf area density. Considering this spatially continuous variable allows to describe the spatiotemporal evolution of the tree crown by means of 3D partial differential equations. These offer a framework to rigorously take locally and adaptively acting effects into account, notably the growth toward light. Biomass production through photosynthesis and the allocation to foliage and wood are readily included in this model framework. The system of equations stands out due to its inherent dynamic property of self-organization and spontaneous adaptation, generating complex behavior from even only a few parameters. The density-based approach yields spatially structured tree crowns without relying on detailed geometry. We present the methodological fundamentals of such a modeling approach and discuss further prospects and applications.
Hagopian, Raffi; Davidson, John R; Datta, Ruchira S; Samad, Bushra; Jarvis, Glen R; Sjölander, Kimmen
2010-07-01
We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is an extension of the SATCHMO algorithm, and employs a divide-and-conquer strategy to jump-start SATCHMO at a higher point in the phylogenetic tree, reducing the computational complexity of the progressive all-versus-all HMM-HMM scoring and alignment. Results on a benchmark dataset of 983 structurally aligned pairs from the PREFAB benchmark dataset show that SATCHMO-JS provides a statistically significant improvement in alignment accuracy over MUSCLE, Multiple Alignment using Fast Fourier Transform (MAFFT), ClustalW and the original SATCHMO algorithm. The SATCHMO-JS webserver is available at http://phylogenomics.berkeley.edu/satchmo-js. The datasets used in these experiments are available for download at http://phylogenomics.berkeley.edu/satchmo-js/supplementary/.
"Growing trees backwards": Description of a stand reconstruction model (P-53)
Jonathan D. Bakker; Andrew J. Sanchez Meador; Peter Z. Fule; David W. Huffman; Margaret M. Moore
2008-01-01
We describe an individual-tree model that uses contemporary measurements to "grow trees backward" and reconstruct past tree diameters and stand structure in ponderosa pine dominated stands of the Southwest. Model inputs are contemporary structural measurements of all snags, logs, stumps, and living trees, and radial growth measurements, if available. Key...
Fractal approach to computer-analytical modelling of tree crown
International Nuclear Information System (INIS)
Berezovskaya, F.S.; Karev, G.P.; Kisliuk, O.F.; Khlebopros, R.G.; Tcelniker, Yu.L.
1993-09-01
In this paper we discuss three approaches to the modeling of a tree crown development. These approaches are experimental (i.e. regressive), theoretical (i.e. analytical) and simulation (i.e. computer) modeling. The common assumption of these is that a tree can be regarded as one of the fractal objects which is the collection of semi-similar objects and combines the properties of two- and three-dimensional bodies. We show that a fractal measure of crown can be used as the link between the mathematical models of crown growth and light propagation through canopy. The computer approach gives the possibility to visualize a crown development and to calibrate the model on experimental data. In the paper different stages of the above-mentioned approaches are described. The experimental data for spruce, the description of computer system for modeling and the variant of computer model are presented. (author). 9 refs, 4 figs
Approximate dynamic fault tree calculations for modelling water supply risks
International Nuclear Information System (INIS)
Lindhe, Andreas; Norberg, Tommy; Rosén, Lars
2012-01-01
Traditional fault tree analysis is not always sufficient when analysing complex systems. To overcome the limitations dynamic fault tree (DFT) analysis is suggested in the literature as well as different approaches for how to solve DFTs. For added value in fault tree analysis, approximate DFT calculations based on a Markovian approach are presented and evaluated here. The approximate DFT calculations are performed using standard Monte Carlo simulations and do not require simulations of the full Markov models, which simplifies model building and in particular calculations. It is shown how to extend the calculations of the traditional OR- and AND-gates, so that information is available on the failure probability, the failure rate and the mean downtime at all levels in the fault tree. Two additional logic gates are presented that make it possible to model a system's ability to compensate for failures. This work was initiated to enable correct analyses of water supply risks. Drinking water systems are typically complex with an inherent ability to compensate for failures that is not easily modelled using traditional logic gates. The approximate DFT calculations are compared to results from simulations of the corresponding Markov models for three water supply examples. For the traditional OR- and AND-gates, and one gate modelling compensation, the errors in the results are small. For the other gate modelling compensation, the error increases with the number of compensating components. The errors are, however, in most cases acceptable with respect to uncertainties in input data. The approximate DFT calculations improve the capabilities of fault tree analysis of drinking water systems since they provide additional and important information and are simple and practically applicable.
Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.
Beck, Kirk A.
2005-01-01
This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…
Modelling dimensional growth of three street tree species in the ...
African Journals Online (AJOL)
The results could also be used in the process of modelling energy use reduction, air pollution uptake, rainfall interception, carbon sequestration and microclimate modification of urban forests such as those found in the City of Tshwane. Keywords: allometry; regression; size relationships; tree growth; urban forests. Southern ...
Hydrochemical analysis of groundwater using a tree-based model
Litaor, M. Iggy; Brielmann, H.; Reichmann, O.; Shenker, M.
2010-06-01
SummaryHydrochemical indices are commonly used to ascertain aquifer characteristics, salinity problems, anthropogenic inputs and resource management, among others. This study was conducted to test the applicability of a binary decision tree model to aquifer evaluation using hydrochemical indices as input. The main advantage of the tree-based model compared to other commonly used statistical procedures such as cluster and factor analyses is the ability to classify groundwater samples with assigned probability and the reduction of a large data set into a few significant variables without creating new factors. We tested the model using data sets collected from headwater springs of the Jordan River, Israel. The model evaluation consisted of several levels of complexity, from simple separation between the calcium-magnesium-bicarbonate water type of karstic aquifers to the more challenging separation of calcium-sodium-bicarbonate water type flowing through perched and regional basaltic aquifers. In all cases, the model assigned measures for goodness of fit in the form of misclassification errors and singled out the most significant variable in the analysis. The model proceeded through a sequence of partitions providing insight into different possible pathways and changing lithology. The model results were extremely useful in constraining the interpretation of geological heterogeneity and constructing a conceptual flow model for a given aquifer. The tree model clearly identified the hydrochemical indices that were excluded from the analysis, thus providing information that can lead to a decrease in the number of routinely analyzed variables and a significant reduction in laboratory cost.
Water-Tree Modelling and Detection for Underground Cables
Chen, Qi
In recent years, aging infrastructure has become a major concern for the power industry. Since its inception in early 20th century, the electrical system has been the cornerstone of an industrial society. Stable and uninterrupted delivery of electrical power is now a base necessity for the modern world. As the times march-on, however, the electrical infrastructure ages and there is the inevitable need to renew and replace the existing system. Unfortunately, due to time and financial constraints, many electrical systems today are forced to operate beyond their original design and power utilities must find ways to prolong the lifespan of older equipment. Thus, the concept of preventative maintenance arises. Preventative maintenance allows old equipment to operate longer and at better efficiency, but in order to implement preventative maintenance, the operators must know minute details of the electrical system, especially some of the harder to assess issues such water-tree. Water-tree induced insulation degradation is a problem typically associated with older cable systems. It is a very high impedance phenomenon and it is difficult to detect using traditional methods such as Tan-Delta or Partial Discharge. The proposed dissertation studies water-tree development in underground cables, potential methods to detect water-tree location and water-tree severity estimation. The dissertation begins by developing mathematical models of water-tree using finite element analysis. The method focuses on surface-originated vented tree, the most prominent type of water-tree fault in the field. Using the standard operation parameters of North American electrical systems, the water-tree boundary conditions are defined. By applying finite element analysis technique, the complex water-tree structure is broken down to homogeneous components. The result is a generalized representation of water-tree capacitance at different stages of development. The result from the finite element analysis
Price, B; Gomez, A; Mathys, L; Gardi, O; Schellenberger, A; Ginzler, C; Thürig, E
2017-03-01
Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.
Systems analysis approach to probabilistic modeling of fault trees
International Nuclear Information System (INIS)
Bartholomew, R.J.; Qualls, C.R.
1985-01-01
A method of probabilistic modeling of fault tree logic combined with stochastic process theory (Markov modeling) has been developed. Systems are then quantitatively analyzed probabilistically in terms of their failure mechanisms including common cause/common mode effects and time dependent failure and/or repair rate effects that include synergistic and propagational mechanisms. The modeling procedure results in a state vector set of first order, linear, inhomogeneous, differential equations describing the time dependent probabilities of failure described by the fault tree. The solutions of this Failure Mode State Variable (FMSV) model are cumulative probability distribution functions of the system. A method of appropriate synthesis of subsystems to form larger systems is developed and applied to practical nuclear power safety systems
Sequential and Parallel Attack Tree Modelling
Arnold, Florian; Guck, Dennis; Kumar, Rajesh; Stoelinga, Mariëlle Ida Antoinette; Koornneef, Floor; van Gulijk, Coen
The intricacy of socio-technical systems requires a careful planning and utilisation of security resources to ensure uninterrupted, secure and reliable services. Even though many studies have been conducted to understand and model the behaviour of a potential attacker, the detection of crucial
DNA sequence modeling based on context trees
Kusters, C.J.; Ignatenko, T.; Roland, J.; Horlin, F.
2015-01-01
Genomic sequences contain instructions for protein and cell production. Therefore understanding and identification of biologically and functionally meaningful patterns in DNA sequences is of paramount importance. Modeling of DNA sequences in its turn can help to better understand and identify such
Brian J. Clough; Matthew B. Russell; Grant M. Domke; Christopher W. Woodall; Philip J. Radtke
2016-01-01
tEstimation of live tree biomass is an important task for both forest carbon accounting and studies of nutri-ent dynamics in forest ecosystems. In this study, we took advantage of an extensive felled-tree database(with 2885 foliage biomass observations) to compare different models and grouping schemes based onphylogenetic and geographic variation for predicting foliage...
Hierarchical models for informing general biomass equations with felled tree data
Brian J. Clough; Matthew B. Russell; Christopher W. Woodall; Grant M. Domke; Philip J. Radtke
2015-01-01
We present a hierarchical framework that uses a large multispecies felled tree database to inform a set of general models for predicting tree foliage biomass, with accompanying uncertainty, within the FIA database. Results suggest significant prediction uncertainty for individual trees and reveal higher errors when predicting foliage biomass for larger trees and for...
GEOMETRIC MODELLING OF TREE ROOTS WITH DIFFERENT LEVELS OF DETAIL
Directory of Open Access Journals (Sweden)
J. I. Guerrero Iñiguez
2017-09-01
Full Text Available This paper presents a geometric approach for modelling tree roots with different Levels of Detail, suitable for analysis of the tree anchoring, potentially occupied underground space, interaction with urban elements and damage produced and taken in the built-in environment. Three types of tree roots are considered to cover several species: tap root, heart shaped root and lateral roots. Shrubs and smaller plants are not considered, however, a similar approach can be considered if the information is available for individual species. The geometrical approach considers the difficulties of modelling the actual roots, which are dynamic and almost opaque to direct observation, proposing generalized versions. For each type of root, different geometric models are considered to capture the overall shape of the root, a simplified block model, and a planar or surface projected version. Lower detail versions are considered as compatibility version for 2D systems while higher detail models are suitable for 3D analysis and visualization. The proposed levels of detail are matched with CityGML Levels of Detail, enabling both analysis and aesthetic views for urban modelling.
Geometric Modelling of Tree Roots with Different Levels of Detail
Guerrero Iñiguez, J. I.
2017-09-01
This paper presents a geometric approach for modelling tree roots with different Levels of Detail, suitable for analysis of the tree anchoring, potentially occupied underground space, interaction with urban elements and damage produced and taken in the built-in environment. Three types of tree roots are considered to cover several species: tap root, heart shaped root and lateral roots. Shrubs and smaller plants are not considered, however, a similar approach can be considered if the information is available for individual species. The geometrical approach considers the difficulties of modelling the actual roots, which are dynamic and almost opaque to direct observation, proposing generalized versions. For each type of root, different geometric models are considered to capture the overall shape of the root, a simplified block model, and a planar or surface projected version. Lower detail versions are considered as compatibility version for 2D systems while higher detail models are suitable for 3D analysis and visualization. The proposed levels of detail are matched with CityGML Levels of Detail, enabling both analysis and aesthetic views for urban modelling.
Dimensional Reduction for the General Markov Model on Phylogenetic Trees.
Sumner, Jeremy G
2017-03-01
We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.
Directory of Open Access Journals (Sweden)
Jörgen Wallerman
2013-04-01
Full Text Available Individual tree crowns may be delineated from airborne laser scanning (ALS data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E. For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3% was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1% with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.
International Nuclear Information System (INIS)
Zhou Yunlong; Zhang Xueqing; Gao Yunpeng; Cheng Yue
2009-01-01
For studying flow regimes of gas/liquid two-phase in a vertical upward pipe, the conductance fluctuation information of four typical flow regimes was collected by a measuring the system with self-made multiple conductivity probes. Owing to the non-stationarity of conductance fluctuation signals of gas-liquid two-phase flow, a kind of' flow regime identification method based on wavelet packet Multi-scale Information Entropy and Hidden Markov Model (HMM) was put forward. First of all, the collected conductance fluctuation signals were decomposed into eight different frequency bands signals. Secondly, the wavelet packet multi-scale information entropy of different frequency bands signals were regarded as the input characteristic vectors of all states HMM which had been trained. In the end the regime identification of' the gas-liquid two-phase flow could be performed. The study showed that the method that HMM was applied to identify the flow regime was superior to the one that BP neural network was used, and the results proved that the method was efficient and feasible. (authors)
Optimization Method of Fusing Model Tree into Partial Least Squares
Directory of Open Access Journals (Sweden)
Yu Fang
2017-01-01
Full Text Available Partial Least Square (PLS can’t adapt to the characteristics of the data of many fields due to its own features multiple independent variables, multi-dependent variables and non-linear. However, Model Tree (MT has a good adaptability to nonlinear function, which is made up of many multiple linear segments. Based on this, a new method combining PLS and MT to analysis and predict the data is proposed, which build MT through the main ingredient and the explanatory variables(the dependent variable extracted from PLS, and extract residual information constantly to build Model Tree until well-pleased accuracy condition is satisfied. Using the data of the maxingshigan decoction of the monarch drug to treat the asthma or cough and two sample sets in the UCI Machine Learning Repository, the experimental results show that, the ability of explanation and predicting get improved in the new method.
A diagnostic tree model for polytomous responses with multiple strategies.
Ma, Wenchao
2018-04-23
Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students' problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students' partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM. © 2018 The British Psychological Society.
Model-Based Design of Tree WSNs for Decentralized Detection
Directory of Open Access Journals (Sweden)
Ashraf Tantawy
2015-08-01
Full Text Available The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.
Application of Goal Tree-Success Tree model as the knowledge-base of operator advisory systems
International Nuclear Information System (INIS)
Kim, I.S.; Modarres, M.
1987-01-01
The most important portion of an expert system development is the articulation of knowledge by the expert and its satisfactory formulation in a suitable knowledge representation scheme for mechanization by a computer. A 'deep knowledge' approach called Goal Tree-Success Tree model is devised to represent complex dynamic domain knowledge. This approach can hierarchically model the underlying principles of a given process domain (for example nuclear power plant operations domain). The Goal Tree-Success Tree can then be used to represent the knowledge-base and provide means of selecting an efficient search routine in the inference engine of an expert system. A prototype expert system has been developed to demonstrate the method. This expert system models the operation of a typical system used in the pressurized water reactors. The expert system is modeled for real-time operations if an interface between plant parameters and the expert system is established. The real-time operation provides an ability to quickly remedy minor disturbances that can quickly lead to a system malfunction or trip. A description of both the Goal Tree-Success Tree model and the prototype expert system is presented. (orig.)
Data acquisition in modeling using neural networks and decision trees
Directory of Open Access Journals (Sweden)
R. Sika
2011-04-01
Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too
Harvesting cost model for small trees in natural stands in the interior northwest.
Bruce R. Hartsough; Xiaoshan Zhang; Roger D. Fight
2001-01-01
Realistic logging cost models are needed for long-term forest management planning. Data from numerous published studies were combined to estimate the costs of harvesting small trees in natural stands in the Interior Northwest of North America. Six harvesting systems were modeled. Four address gentle terrain: manual log-length, manual whole-tree, mechanized whole-tree,...
A deterministic model for the growth of non-conducting electrical tree structures
International Nuclear Information System (INIS)
Dodd, S J
2003-01-01
Electrical treeing is of interest to the electrical generation, transmission and distribution industries as it is one of the causes of insulation failure in electrical machines, switchgear and transformer bushings. In this paper a deterministic electrical tree growth model is described. The model is based on electrostatics and local electron avalanches to model partial discharge activity within the growing tree structure. Damage to the resin surrounding the tree structure is dependent on the local electrostatic energy dissipation by partial discharges within the tree structure and weighted by the magnitudes of the local electric fields in the resin surrounding the tree structure. The model is successful in simulating the formation of branched structures without the need of a random variable, a requirement of previous stochastic models. Instability in the spatial development of partial discharges within the tree structure takes the role of the stochastic element as used in previous models to produce branched tree structures. The simulated electrical trees conform to the experimentally observed behaviour; tree length versus time and electrical tree growth rate as a function of applied voltage for non-conducting electrical trees. The phase synchronous partial discharge activity and the spatial distribution of emitted light from the tree structure are also in agreement with experimental data for non-conducting trees as grown in a flexible epoxy resin and in polyethylene. The fact that similar tree growth behaviour is found using pure amorphous (epoxy resin) and semicrystalline (polyethylene) materials demonstrate that neither annealed or quenched noise, representing material inhomogeneity, is required for the formation of irregular branched structures (electrical trees). Instead, as shown in this paper, branched growth can occur due to the instability of individual discharges within the tree structure
HMMEditor: a visual editing tool for profile hidden Markov model
Directory of Open Access Journals (Sweden)
Cheng Jianlin
2008-03-01
Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.
Tree shrew (Tupaia belangeri as a novel laboratory disease animal model
Directory of Open Access Journals (Sweden)
Ji Xiao
2017-05-01
Full Text Available The tree shrew (Tupaia belangeri is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.
Susan L. King
2003-01-01
The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...
Study on competitive interaction models in Cayley tree
International Nuclear Information System (INIS)
Moreira, J.G.M.A.
1987-12-01
We propose two kinds of models in the Cayley tree to simulate Ising models with axial anisotropy in the cubic lattice. The interaction in the direction of the anisotropy is simulated by the interaction along the branches of the tree. The interaction in the planes perpendicular to the anisotropy direction, in the first model, is simulated by interactions between spins in neighbour branches of the same generation arising from same site of the previous generation. In the second model, the simulation of the interaction in the planes are produced by mean field interactions among all spins in sites of the same generation arising from the same site of the previous generations. We study these models in the limit of infinite coordination number. First, we analyse a situation with antiferromagnetic interactions along the branches between first neighbours only, and we find the analogous of a metamagnetic Ising model. In the following, we introduce competitive interactions between first and second neighbours along the branches, to simulate the ANNNI model. We obtain one equation of differences which relates the magnetization of one generation with the magnetization of the two previous generations, to permit a detailed study of the modulated phase region. We note that the wave number of the modulation, for one fixed temperature, changes with the competition parameter to form a devil's staircase with a fractal dimension which increases with the temperature. We discuss the existence of strange atractors, related to a possible caothic phase. Finally, we show the obtained results when we consider interactions along the branches with three neighbours. (author)
Brown, Molly E.; McGroddy, Megan; Spence, Caitlin; Flake, Leah; Sarfraz, Amna; Nowak, David J.; Milesi, Cristina
2012-01-01
As the world becomes increasingly urban, the need to quantify the effect of trees in urban environments on energy usage, air pollution, local climate and nutrient run-off has increased. By identifying, quantifying and valuing the ecological activity that provides services in urban areas, stronger policies and improved quality of life for urban residents can be obtained. Here we focus on two radically different models that can be used to characterize urban forests. The i-Tree Eco model (formerly UFORE model) quantifies ecosystem services (e.g., air pollution removal, carbon storage) and values derived from urban trees based on field measurements of trees and local ancillary data sets. Biome-BGC (Biome BioGeoChemistry) is used to simulate the fluxes and storage of carbon, water, and nitrogen in natural environments. This paper compares i-Tree Eco's methods to those of Biome-BGC, which estimates the fluxes and storage of energy, carbon, water and nitrogen for vegetation and soil components of the ecosystem. We describe the two models and their differences in the way they calculate similar properties, with a focus on carbon and nitrogen. Finally, we discuss the implications of further integration of these two communities for land managers such as those in Maryland.
Macroscopic Models of Clique Tree Growth for Bayesian Networks
National Aeronautics and Space Administration — In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to...
Process health management using success tree and empirical model
Energy Technology Data Exchange (ETDEWEB)
Heo, Gyunyoung [Kyung Hee Univ., Yongin (Korea, Republic of); Kim, Suyoung [BNF Technology, Daejeon (Korea, Republic of); Sung, Wounkyoung [Korea South-East Power Co. Ltd., Seoul (Korea, Republic of)
2012-03-15
Interests on predictive or condition-based maintenance are heightening in power industries. The ultimate goal of the condition-based maintenance is to prioritize and optimize the maintenance resources by taking a reasonable decision-making process depending op plant's conditions. Such decision-making process should be able to not only observe the deviation from a normal state but also determine the severity or impact of the deviation on different levels such as a component, a system, or a plant. In order to achieve this purpose, a Plant Health Index (PHI) monitoring system was developed, which is operational in more than 10 units of large steam turbine cycles in Korea as well as desalination plants in Saudi Arabia as a proto-type demonstration. The PHI monitoring system has capability to detect whether the deviation between a measured and an estimated parameter which is the result of kernel regression using the accumulated operation data and the current plant boundary conditions (referred as an empirical model) is statistically meaningful. This deviation is converted into a certain index considering the margin to set points which are associated with safety. This index is referred as a PHI and the PHIs can be monitored for an individual parameter as well as a component, system, or plant level. In order to organize the PHIs at the component, system, or plant level, a success tree was developed. At the top of the success tree, the PHIs nodes in the middle of the success tree, the PHIs represent the health status of a component or a system. The concept and definition of the PHI, the key methodologies, the architecture of the developed system, and a practical case of using the PHI monitoring system are described in this article.
Process health management using success tree and empirical model
International Nuclear Information System (INIS)
Heo, Gyunyoung; Kim, Suyoung; Sung, Wounkyoung
2012-01-01
Interests on predictive or condition-based maintenance are heightening in power industries. The ultimate goal of the condition-based maintenance is to prioritize and optimize the maintenance resources by taking a reasonable decision-making process depending op plant's conditions. Such decision-making process should be able to not only observe the deviation from a normal state but also determine the severity or impact of the deviation on different levels such as a component, a system, or a plant. In order to achieve this purpose, a Plant Health Index (PHI) monitoring system was developed, which is operational in more than 10 units of large steam turbine cycles in Korea as well as desalination plants in Saudi Arabia as a proto-type demonstration. The PHI monitoring system has capability to detect whether the deviation between a measured and an estimated parameter which is the result of kernel regression using the accumulated operation data and the current plant boundary conditions (referred as an empirical model) is statistically meaningful. This deviation is converted into a certain index considering the margin to set points which are associated with safety. This index is referred as a PHI and the PHIs can be monitored for an individual parameter as well as a component, system, or plant level. In order to organize the PHIs at the component, system, or plant level, a success tree was developed. At the top of the success tree, the PHIs nodes in the middle of the success tree, the PHIs represent the health status of a component or a system. The concept and definition of the PHI, the key methodologies, the architecture of the developed system, and a practical case of using the PHI monitoring system are described in this article
Modeling Answer Change Behavior: An Application of a Generalized Item Response Tree Model
Jeon, Minjeong; De Boeck, Paul; van der Linden, Wim
2017-01-01
We present a novel application of a generalized item response tree model to investigate test takers' answer change behavior. The model allows us to simultaneously model the observed patterns of the initial and final responses after an answer change as a function of a set of latent traits and item parameters. The proposed application is illustrated…
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.
Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar
2017-01-01
DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features
Directory of Open Access Journals (Sweden)
Rianon Zaman
2017-01-01
Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
Development of TTS Engine for Indian Accent using Modified HMM Algorithm
Directory of Open Access Journals (Sweden)
Sasanko Sekhar Gantayat
2018-03-01
Full Text Available A text-to-speech (TTS system converts normal language text into speech. An intelligent text-to-speech program allows people with visual impairments or reading disabilities, to listen to written works on a home computer. Many computer operating systems and day to day software applications like Adobe Reader have included text-to-speech systems. This paper is presented to show that how HMM can be used as a tool to convert text to speech.
U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...
Tree Branching: Leonardo da Vinci's Rule versus Biomechanical Models
Minamino, Ryoko; Tateno, Masaki
2014-01-01
This study examined Leonardo da Vinci's rule (i.e., the sum of the cross-sectional area of all tree branches above a branching point at any height is equal to the cross-sectional area of the trunk or the branch immediately below the branching point) using simulations based on two biomechanical models: the uniform stress and elastic similarity models. Model calculations of the daughter/mother ratio (i.e., the ratio of the total cross-sectional area of the daughter branches to the cross-sectional area of the mother branch at the branching point) showed that both biomechanical models agreed with da Vinci's rule when the branching angles of daughter branches and the weights of lateral daughter branches were small; however, the models deviated from da Vinci's rule as the weights and/or the branching angles of lateral daughter branches increased. The calculated values of the two models were largely similar but differed in some ways. Field measurements of Fagus crenata and Abies homolepis also fit this trend, wherein models deviated from da Vinci's rule with increasing relative weights of lateral daughter branches. However, this deviation was small for a branching pattern in nature, where empirical measurements were taken under realistic measurement conditions; thus, da Vinci's rule did not critically contradict the biomechanical models in the case of real branching patterns, though the model calculations described the contradiction between da Vinci's rule and the biomechanical models. The field data for Fagus crenata fit the uniform stress model best, indicating that stress uniformity is the key constraint of branch morphology in Fagus crenata rather than elastic similarity or da Vinci's rule. On the other hand, mechanical constraints are not necessarily significant in the morphology of Abies homolepis branches, depending on the number of daughter branches. Rather, these branches were often in agreement with da Vinci's rule. PMID:24714065
Tree branching: Leonardo da Vinci's rule versus biomechanical models.
Minamino, Ryoko; Tateno, Masaki
2014-01-01
This study examined Leonardo da Vinci's rule (i.e., the sum of the cross-sectional area of all tree branches above a branching point at any height is equal to the cross-sectional area of the trunk or the branch immediately below the branching point) using simulations based on two biomechanical models: the uniform stress and elastic similarity models. Model calculations of the daughter/mother ratio (i.e., the ratio of the total cross-sectional area of the daughter branches to the cross-sectional area of the mother branch at the branching point) showed that both biomechanical models agreed with da Vinci's rule when the branching angles of daughter branches and the weights of lateral daughter branches were small; however, the models deviated from da Vinci's rule as the weights and/or the branching angles of lateral daughter branches increased. The calculated values of the two models were largely similar but differed in some ways. Field measurements of Fagus crenata and Abies homolepis also fit this trend, wherein models deviated from da Vinci's rule with increasing relative weights of lateral daughter branches. However, this deviation was small for a branching pattern in nature, where empirical measurements were taken under realistic measurement conditions; thus, da Vinci's rule did not critically contradict the biomechanical models in the case of real branching patterns, though the model calculations described the contradiction between da Vinci's rule and the biomechanical models. The field data for Fagus crenata fit the uniform stress model best, indicating that stress uniformity is the key constraint of branch morphology in Fagus crenata rather than elastic similarity or da Vinci's rule. On the other hand, mechanical constraints are not necessarily significant in the morphology of Abies homolepis branches, depending on the number of daughter branches. Rather, these branches were often in agreement with da Vinci's rule.
Linking definitions, mechanisms, and modeling of drought-induced tree death.
Anderegg, William R L; Berry, Joseph A; Field, Christopher B
2012-12-01
Tree death from drought and heat stress is a critical and uncertain component in forest ecosystem responses to a changing climate. Recent research has illuminated how tree mortality is a complex cascade of changes involving interconnected plant systems over multiple timescales. Explicit consideration of the definitions, dynamics, and temporal and biological scales of tree mortality research can guide experimental and modeling approaches. In this review, we draw on the medical literature concerning human death to propose a water resource-based approach to tree mortality that considers the tree as a complex organism with a distinct growth strategy. This approach provides insight into mortality mechanisms at the tree and landscape scales and presents promising avenues into modeling tree death from drought and temperature stress. Copyright © 2012 Elsevier Ltd. All rights reserved.
Research study on harmonized molecular materials (HMM); Bunshi kyocho zairyo ni kansuru chosa kenkyu
Energy Technology Data Exchange (ETDEWEB)
NONE
1997-03-01
As functional material to satisfy various needs for environmental harmonization and efficient conversion for information-oriented and aging societies, HMM were surveyed. Living bodies effectively carry out transmission/processing of information, and transport/conversion of substances, and these functions are based on harmonization between organic molecules, and between those and metal or inorganic ones. HMM is a key substance to artificially realize these bio-related functions. Its R & D aims at (1) Making a breakthrough in production process based on innovation of material separation/conversion technology, (2) Contribution to an information-oriented society by high-efficiency devices, and (3) Growth of a functional bio-material industry. HMM is classified into three categories: (1) Assembly materials such as organic ultra-thin films (LB film, self-organizing film), and organic/inorganic hybrid materials for optoelectronics, sensors and devices, (2) Mesophase materials such as functional separation membrane and photo-conductive material, and (3) Microporous materials such as synthetic catalyst using guest/host materials. 571 refs., 88 figs., 21 tabs.
Effects of lightning on trees: A predictive model based on in situ electrical resistivity.
Gora, Evan M; Bitzer, Phillip M; Burchfield, Jeffrey C; Schnitzer, Stefan A; Yanoviak, Stephen P
2017-10-01
The effects of lightning on trees range from catastrophic death to the absence of observable damage. Such differences may be predictable among tree species, and more generally among plant life history strategies and growth forms. We used field-collected electrical resistivity data in temperate and tropical forests to model how the distribution of power from a lightning discharge varies with tree size and identity, and with the presence of lianas. Estimated heating density (heat generated per volume of tree tissue) and maximum power (maximum rate of heating) from a standardized lightning discharge differed 300% among tree species. Tree size and morphology also were important; the heating density of a hypothetical 10 m tall Alseis blackiana was 49 times greater than for a 30 m tall conspecific, and 127 times greater than for a 30 m tall Dipteryx panamensis . Lianas may protect trees from lightning by conducting electric current; estimated heating and maximum power were reduced by 60% (±7.1%) for trees with one liana and by 87% (±4.0%) for trees with three lianas. This study provides the first quantitative mechanism describing how differences among trees can influence lightning-tree interactions, and how lianas can serve as natural lightning rods for trees.
USAHA PENINGKATAN PRODUKTIVITAS DENGAN PRODUCTIVITY EVALUATION TREE (PET MODELS
Directory of Open Access Journals (Sweden)
Muchlison Anis
2007-04-01
Full Text Available Usaha peningkatan produktivitas merupakan suatu langkah menuju perbaikan perusahaan dimasa yang akan datang. Model perencanaan produktivitas Productivity Evaluation Tree (PET memberikan kemudahan bagi perusahaan dalam mengembangkan dan menilai seluruh alternatif yang mungkin dilakukan dalam menetapkan target peningkatan produktivitas dan usaha peningkatan produktivitas. Dalam penelitian ini alternatif perencanaan ada tiga. Pertama, meningkatkan standart penggunaan bahan baku dari 20% menjadi 30%. Kedua, pengeluaran bahan baku diusulkan sama dengan bulan lalu dengan menerapkan peningkatan standart penggunaan bahan baku sama seperti dengan alternatif pertama, Ketiga, menstimulasi alternatif 2 dengan melakukan manajemen motivasi terhadap tenaga kerja. Dari hasil evaluasi pohon produktivitas maka dapat diketahui estimasi peningkatan produktivitas yang tertinggi adalah alternatif ke tiga dengan perubahan tingkat produktivitas sebesar 0,39.
Engineering of Algorithms for Hidden Markov models and Tree Distances
DEFF Research Database (Denmark)
Sand, Andreas
Bioinformatics is an interdisciplinary scientific field that combines biology with mathematics, statistics and computer science in an effort to develop computational methods for handling, analyzing and learning from biological data. In the recent decades, the amount of available biological data has...... speed up all the classical algorithms for analyses and training of hidden Markov models. And I show how two particularly important algorithms, the forward algorithm and the Viterbi algorithm, can be accelerated through a reformulation of the algorithms and a somewhat more complicated parallelization...... contribution to the theoretically fastest set of algorithms presently available to compute two closely related measures of tree distance, the triplet distance and the quartet distance. And I further demonstrate that they are also the fastest algorithms in almost all cases when tested in practice....
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates
Ronald E. McRoberts; James A. Westfall
2014-01-01
Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...
Tree-Based Global Model Tests for Polytomous Rasch Models
Komboz, Basil; Strobl, Carolin; Zeileis, Achim
2018-01-01
Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these…
Thomas C. Edwards; D. Richard Cutler; Niklaus E. Zimmermann; Linda Geiser; Gretchen G. Moisen
2006-01-01
We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by...
Parallel family trees for transfer matrices in the Potts model
Navarro, Cristobal A.; Canfora, Fabrizio; Hitschfeld, Nancy; Navarro, Gonzalo
2015-02-01
The computational cost of transfer matrix methods for the Potts model is related to the question in how many ways can two layers of a lattice be connected? Answering the question leads to the generation of a combinatorial set of lattice configurations. This set defines the configuration space of the problem, and the smaller it is, the faster the transfer matrix can be computed. The configuration space of generic (q , v) transfer matrix methods for strips is in the order of the Catalan numbers, which grows asymptotically as O(4m) where m is the width of the strip. Other transfer matrix methods with a smaller configuration space indeed exist but they make assumptions on the temperature, number of spin states, or restrict the structure of the lattice. In this paper we propose a parallel algorithm that uses a sub-Catalan configuration space of O(3m) to build the generic (q , v) transfer matrix in a compressed form. The improvement is achieved by grouping the original set of Catalan configurations into a forest of family trees, in such a way that the solution to the problem is now computed by solving the root node of each family. As a result, the algorithm becomes exponentially faster than the Catalan approach while still highly parallel. The resulting matrix is stored in a compressed form using O(3m ×4m) of space, making numerical evaluation and decompression to be faster than evaluating the matrix in its O(4m ×4m) uncompressed form. Experimental results for different sizes of strip lattices show that the parallel family trees (PFT) strategy indeed runs exponentially faster than the Catalan Parallel Method (CPM), especially when dealing with dense transfer matrices. In terms of parallel performance, we report strong-scaling speedups of up to 5.7 × when running on an 8-core shared memory machine and 28 × for a 32-core cluster. The best balance of speedup and efficiency for the multi-core machine was achieved when using p = 4 processors, while for the cluster
Forward modeling of tree-ring data: a case study with a global network
Breitenmoser, P. D.; Frank, D.; Brönnimann, S.
2012-04-01
Information derived from tree-rings is one of the most powerful tools presently available for studying past climatic variability as well as identifying fundamental relationships between tree-growth and climate. Climate reconstructions are typically performed by extending linear relationships, established during the overlapping period of instrumental and climate proxy archives into the past. Such analyses, however, are limited by methodological assumptions, including stationarity and linearity of the climate-proxy relationship. We investigate climate and tree-ring data using the Vaganov-Shashkin-Lite (VS-Lite) forward model of tree-ring width formation to examine the relations among actual tree growth and climate (as inferred from the simulated chronologies) to reconstruct past climate variability. The VS-lite model has been shown to produce skill comparable to that achieved using classical dendrochronological statistical modeling techniques when applied on simulations of a network of North American tree-ring chronologies. Although the detailed mechanistic processes such as photosynthesis, storage, or cell processes are not modeled directly, the net effect of the dominating nonlinear climatic controls on tree-growth are implemented into the model by the principle of limiting factors and threshold growth response functions. The VS-lite model requires as inputs only latitude, monthly mean temperature and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree-rings to monthly climate conditions obtained from the 20th century reanalysis project back to 1871. These simulated tree-ring chronologies are compared to the climate-driven variability in worldwide observed tree-ring chronologies from the International Tree Ring Database. Results point toward the suitability of the relationship among actual tree
Dynamics models and modeling of tree stand development
Directory of Open Access Journals (Sweden)
M. V. Rogozin
2015-04-01
Full Text Available Brief analysis of scientific works in Russia and in the CIS over the past 100 years. Logical and mathematical models consider the conceptual and show some of the results of their verification. It was found that the models include different laws and the parameters, the sum of which allows you to divide them into four categories: models of static states, development models, models of care for the natural forest and models of cultivation. Each category has fulfilled and fulfills its tasks in economic management. Thus, the model states in statics (table traverse growth played a prominent role in figuring out what may be the most productive (full stands in different regions of the country. However, they do not answer the question of what the initial states lead to the production of complete stands. In a study of the growth of stands used system analysis, and it is observed dominance of works studying static state, snatched from the biological time. Therefore, the real drama of the growth of stands remained almost unexplored. It is no accident there were «chrono-forestry» «plantation forestry» and even «non-traditional forestry», where there is a strong case of a number of new concepts of development stands. That is quite in keeping with Kuhn (Kuhn, 2009 in the forestry crisis began – there were alternative theories and coexist conflicting scientific schools. To develop models of stand development, it is proposed to use a well-known method of repeated observations within 10–20 years, in conjunction with the explanation of the history of the initial density. It mounted on the basis of studying the dynamics of its indicators: the trunk, crown overlap coefficient, the sum of volumes of all crowns and the relative length of the crown. According to these indicators, the researcher selects natural series of development stands with the same initial density. As a theoretical basis for the models it is possible to postulate the general properties of
Directory of Open Access Journals (Sweden)
Neng-Sheng Pai
2014-01-01
Full Text Available This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC. Then, the Hidden Markov Model (HMM was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot.
Bayesian averaging over Decision Tree models for trauma severity scoring.
Schetinin, V; Jakaite, L; Krzanowski, W
2018-01-01
Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.
Modelling of electric tree progression due to space charge modified fields
International Nuclear Information System (INIS)
Seralathan, K E; Mahajan, A; Gupta, Nandini
2008-01-01
Tree initiation and growth require localized field enhancement that results in material erosion and formation of tree channels. Tree progression is linked to partial discharges within the tree tubules, characterized by recurrent periods of activity followed by quiescent states. Charge builds up across the non-conducting tree channels during the inactive regime, and discharge follows. In this work, the role of the space charge modified field during the non-discharging regime in deciding the site of subsequent discharges and thereby shaping tree structures is studied. A simple stochastic model was developed, in order to understand the respective effects of charges trapped on the walls of tree tubules, at channel tips, or in the volume of the dielectric. While some charge distributions are seen to arrest tree growth, others encourage axial growth towards the other electrode, and some aid in producing bushy trees clustered around the needle tip. The effect of carbon deposition within tree channels, making them effectively conducting, was also investigated. The insights gained from the simulations were successfully used to explain tree growth in the laboratory under high- and low-field conditions
Bridging process-based and empirical approaches to modeling tree growth
Harry T. Valentine; Annikki Makela; Annikki Makela
2005-01-01
The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...
HMM Adaptation for Improving a Human Activity Recognition System
Directory of Open Access Journals (Sweden)
Rubén San-Segundo
2016-09-01
Full Text Available When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs. This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity, and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.
Modeling the effectiveness of tree planting to mitigate habitat loss in blue oak woodlands
Richard B. Standiford; Douglas McCreary; William Frost
2002-01-01
Many local conservation policies have attempted to mitigate the loss of oak woodland habitat resulting from conversion to urban or intensive agricultural land uses through tree planting. This paper models the development of blue oak (Quercus douglasii) stand structure attributes over 50 years after planting. The model uses a single tree, distance...
Models for estimation of tree volume in the miombo woodlands of ...
African Journals Online (AJOL)
Volume of trees is an important parameter in forest management, but only volume models with limited geographical and tree size coverage have previously been developed for Tanzanian miombo woodlands. This study developed models for estimating total, merchantable stem and branches volume applicable for the entire ...
A. Morani; D. Nowak; S. Hirabayashi; G. Guidolotti; M. Medori; V. Muzzini; S. Fares; G. Scarascia Mugnozza; C. Calfapietra
2014-01-01
Ozone flux estimates from the i-Tree model were compared with ozone flux measurements using the Eddy Covariance technique in a periurban Mediterranean forest near Rome (Castelporziano). For the first time i-Tree model outputs were compared with field measurements in relation to dry deposition estimates. Results showed generally a...
Incorporating additional tree and environmental variables in a lodgepole pine stem profile model
John C. Byrne
1993-01-01
A new variable-form segmented stem profile model is developed for lodgepole pine (Pinus contorta) trees from the northern Rocky Mountains of the United States. I improved estimates of stem diameter by predicting two of the model coefficients with linear equations using a measure of tree form, defined as a ratio of dbh and total height. Additional improvements were...
A Model of Desired Performance in Phylogenetic Tree Construction for Teaching Evolution.
Brewer, Steven D.
This research paper examines phylogenetic tree construction-a form of problem solving in biology-by studying the strategies and heuristics used by experts. One result of the research is the development of a model of desired performance for phylogenetic tree construction. A detailed description of the model and the sample problems which illustrate…
A modeling study of the impact of urban trees on ozone
David J. Nowak; Kevin L. Civerolo; S. Trivikrama Rao; Gopal Sistla; Christopher J. Luley; Daniel E. Crane
2000-01-01
Modeling the effects of increased urban tree cover on ozone concentrations (July 13-15, 1995) from Washington, DC, to central Massachusetts reveals that urban trees generally reduce ozone concentrations in cities, but tend to increase average ozone concentrations in the overall modeling domain. During the daytime, average ozone reductions in urban areas (1 ppb) were...
Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments
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…
A maximum pseudo-likelihood approach for estimating species trees under the coalescent model
Directory of Open Access Journals (Sweden)
Edwards Scott V
2010-10-01
Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the
A stochastic model of nanoparticle self-assembly on Cayley trees
International Nuclear Information System (INIS)
Mazilu, I; Schwen, E M; Banks, W E; Pope, B K; Mazilu, D A
2015-01-01
Nanomedicine is an emerging area of medical research that uses innovative nanotechnologies to improve the delivery of therapeutic and diagnostic agents with maximum clinical benefit. We present a versatile stochastic model that can be used to capture the basic features of drug encapsulation of nanoparticles on tree-like synthetic polymers called dendrimers. The geometry of a dendrimer is described mathematically as a Cayley tree. We use our stochastic model to study the dynamics of deposition and release of monomers (simulating the drug molecules) on Cayley trees (simulating dendrimers). We present analytical and Monte Carlo simulation results for the particle density on Cayley trees of coordination number three and four
International Nuclear Information System (INIS)
Modarres, Mohammad; Cheon, Se Woo
1999-01-01
Most of the complex systems are formed through some hierarchical evolution. Therefore, those systems can be best described through hierarchical frameworks. This paper describes some fundamental attributes of complex physical systems and several hierarchies such as functional, behavioral, goal/condition, and event hierarchies, then presents a function-centered approach to system modeling. Based on the function-centered concept, this paper describes the joint goal tree-success tree (GTST) and the master logic diagram (MLD) as a framework for developing models of complex physical systems. A function-based lexicon for classifying the most common elements of engineering systems for use in the GTST-MLD framework has been proposed. The classification is based on the physical conservation laws that govern the engineering systems. Functional descriptions based on conservation laws provide a simple and rich vocabulary for modeling complex engineering systems
Prediction of adverse drug reactions using decision tree modeling.
Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J
2010-07-01
Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.
Isoprene Emission Factors for Subtropical Street Trees for Regional Air Quality Modeling.
Dunn-Johnston, Kristina A; Kreuzwieser, Jürgen; Hirabayashi, Satoshi; Plant, Lyndal; Rennenberg, Heinz; Schmidt, Susanne
2016-01-01
Evaluating the environmental benefits and consequences of urban trees supports their sustainable management in cities. Models such as i-Tree Eco enable decision-making by quantifying effects associated with particular tree species. Of specific concern are emissions of biogenic volatile organic compounds, particularly isoprene, that contribute to the formation of photochemical smog and ground level ozone. Few studies have quantified these potential disservices of urban trees, and current models predominantly use emissions data from trees that differ from those in our target region of subtropical Australia. The present study aimed (i) to quantify isoprene emission rates of three tree species that together represent 16% of the inventoried street trees in the target region; (ii) to evaluate outputs of the i-Tree Eco model using species-specific versus currently used, generic isoprene emission rates; and (iii) to evaluate the findings in the context of regional air quality. Isoprene emission rates of (Myrtaceae) and (Proteaceae) were 2.61 and 2.06 µg g dry leaf weight h, respectively, whereas (Sapindaceae) was a nonisoprene emitter. We substituted the generic isoprene emission rates with these three empirical values in i-Tree Eco, resulting in a 182 kg yr (97%) reduction in isoprene emissions, totaling 6284 kg yr when extrapolated to the target region. From these results we conclude that care has to be taken when using generic isoprene emission factors for urban tree models. We recommend that emissions be quantified for commonly planted trees, allowing decision-makers to select tree species with the greatest overall benefit for the urban environment. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Modelling fruit-temperature dynamics within apple tree crowns using virtual plants.
Saudreau, M; Marquier, A; Adam, B; Sinoquet, H
2011-10-01
Fruit temperature results from a complex system involving the climate, the tree architecture, the fruit location within the tree crown and the fruit thermal properties. Despite much theoretical and experimental evidence for large differences (up to 10 °C in sunny conditions) between fruit temperature and air temperature, fruit temperature is never used in horticultural studies. A way of modelling fruit-temperature dynamics from climate data is addressed in this work. The model is based upon three-dimensional virtual representation of apple trees and links three-dimensional virtual trees with a physical-based fruit-temperature dynamical model. The overall model was assessed by comparing model outputs to field measures of fruit-temperature dynamics. The model was able to simulate both the temperature dynamics at fruit scale, i.e. fruit-temperature gradients and departure from air temperature, and at the tree scale, i.e. the within-tree-crown variability in fruit temperature (average root mean square error value over fruits was 1·43 °C). This study shows that linking virtual plants with the modelling of the physical plant environment offers a relevant framework to address the modelling of fruit-temperature dynamics within a tree canopy. The proposed model offers opportunities for modelling effects of the within-crown architecture on fruit thermal responses in horticultural studies.
DEFF Research Database (Denmark)
Kheir, Rania Bou; Greve, Mogens Humlekrog; Bøcher, Peder Klith
2010-01-01
the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature...... field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v......) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME...
A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction
De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David
2016-01-01
Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847
A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction.
De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David
2016-05-01
Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.
Directory of Open Access Journals (Sweden)
Yi Lin
2018-02-01
Full Text Available Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology—light detection and ranging (LiDAR. Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.
Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne
2018-01-01
Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.
Modeling the Uptake and Transpiration of TCE Using Phreatophytic Trees
National Research Council Canada - National Science Library
Wise, Douglas
1997-01-01
.... The purpose of this research is to develop quantitative concepts for understanding the dynamics of TCE uptake and transpiration by phreatophytic trees over a short rotation woody crop time frame...
Reconstructing 3D Tree Models Using Motion Capture and Particle Flow
Directory of Open Access Journals (Sweden)
Jie Long
2013-01-01
Full Text Available Recovering tree shape from motion capture data is a first step toward efficient and accurate animation of trees in wind using motion capture data. Existing algorithms for generating models of tree branching structures for image synthesis in computer graphics are not adapted to the unique data set provided by motion capture. We present a method for tree shape reconstruction using particle flow on input data obtained from a passive optical motion capture system. Initial branch tip positions are estimated from averaged and smoothed motion capture data. Branch tips, as particles, are also generated within a bounding space defined by a stack of bounding boxes or a convex hull. The particle flow, starting at branch tips within the bounding volume under forces, creates tree branches. The forces are composed of gravity, internal force, and external force. The resulting shapes are realistic and similar to the original tree crown shape. Several tunable parameters provide control over branch shape and arrangement.
Model trees for identifying exceptional players in the NHL and NBA draft
Liu, Yejia
2018-01-01
Drafting players is crucial for a team’s success. We describe a data-driven interpretable approach for assessing prospects in the National Hockey League and National Basketball Association. Previous approaches have built a predictive model based on player features, or derived performance predictions from comparable players. Our work develops model tree learning, which incorporates strengths of both model-based and cohort-based approaches. A model tree partitions the feature space according to...
Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H
2017-10-25
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.
A structurally based analytic model for estimation of biomass and fuel loads of woodland trees
Robin J. Tausch
2009-01-01
Allometric/structural relationships in tree crowns are a consequence of the physical, physiological, and fluid conduction processes of trees, which control the distribution, efficient support, and growth of foliage in the crown. The structural consequences of these processes are used to develop an analytic model based on the concept of branch orders. A set of...
Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett
2009-01-01
Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....
International Nuclear Information System (INIS)
Antonopoulos-Domis, M.; Clouvas, A.; Gagianas, A.
1990-01-01
Radiocesium contamination from the Chernobyl accident of different parts (fruits, leaves, and shoots) of selected apricot trees in North Greece was systematically measured in 1987 and 1988. The results are presented and discussed in the framework of a simple compartment model describing the long-term contamination uptake mechanism of deciduous fruit trees after a nuclear accident
Estimating tree bole volume using artificial neural network models for four species in Turkey.
Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V
2010-01-01
Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.
Estimates of live-tree carbon stores in the Pacific Northwest are sensitive to model selection
Susanna L. Melson; Mark E. Harmon; Jeremy S. Fried; James B. Domingo
2011-01-01
Estimates of live-tree carbon stores are influenced by numerous uncertainties. One of them is model-selection uncertainty: one has to choose among multiple empirical equations and conversion factors that can be plausibly justified as locally applicable to calculate the carbon store from inventory measurements such as tree height and diameter at breast height (DBH)....
Modeled PM2.5 removal by trees in ten US cities and associated health effects
David J. Nowak; Satoshi Hirabayashi; Allison Bodine; Robert. Hoehn
2013-01-01
Urban particulate air pollution is a serious health issue. Trees within cities can remove ﬁne particles from the atmosphere and consequently improve air quality and human health. Tree effects on PM2.5 concentrations and human health are modeled for 10 U.S. cities. The total amount of PM2.5 removed annually by...
Systematic evaluation of fault trees using real-time model checker UPPAAL
International Nuclear Information System (INIS)
Cha, Sungdeok; Son, Hanseong; Yoo, Junbeom; Jee, Eunkyung; Seong, Poong Hyun
2003-01-01
Fault tree analysis, the most widely used safety analysis technique in industry, is often applied manually. Although techniques such as cutset analysis or probabilistic analysis can be applied on the fault tree to derive further insights, they are inadequate in locating flaws when failure modes in fault tree nodes are incorrectly identified or when causal relationships among failure modes are inaccurately specified. In this paper, we demonstrate that model checking technique is a powerful tool that can formally validate the accuracy of fault trees. We used a real-time model checker UPPAAL because the system we used as the case study, nuclear power emergency shutdown software named Wolsong SDS2, has real-time requirements. By translating functional requirements written in SCR-style tabular notation into timed automata, two types of properties were verified: (1) if failure mode described in a fault tree node is consistent with the system's behavioral model; and (2) whether or not a fault tree node has been accurately decomposed. A group of domain engineers with detailed technical knowledge of Wolsong SDS2 and safety analysis techniques developed fault tree used in the case study. However, model checking technique detected subtle ambiguities present in the fault tree
HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.
Bharath, A; Madhvanath, Sriganesh
2012-04-01
Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.
Sequence Tree Modeling for Combined Accident and Feed-and-Bleed Operation
International Nuclear Information System (INIS)
Kim, Bo Gyung; Kang Hyun Gook; Yoon, Ho Joon
2016-01-01
In order to address this issue, this study suggests the sequence tree model to analyze accident sequence systematically. Using the sequence tree model, all possible scenarios which need a specific safety action to prevent the core damage can be identified and success conditions of safety action under complicated situation such as combined accident will be also identified. Sequence tree is branch model to divide plant condition considering the plant dynamics. Since sequence tree model can reflect the plant dynamics, arising from interaction of different accident timing and plant condition and from the interaction between the operator action, mitigation system, and the indicators for operation, sequence tree model can be used to develop the dynamic event tree model easily. Target safety action for this study is a feed-and-bleed (F and B) operation. A F and B operation directly cools down the reactor cooling system (RCS) using the primary cooling system when residual heat removal by the secondary cooling system is not available. In this study, a TLOFW accident and a TLOFW accident with LOCA were the target accidents. Based on the conventional PSA model and indicators, the sequence tree model for a TLOFW accident was developed. If sampling analysis is performed, practical accident sequences can be identified based on the sequence analysis. If a realistic distribution for the variables can be obtained for sampling analysis, much more realistic accident sequences can be described. Moreover, if the initiating event frequency under a combined accident can be quantified, the sequence tree model can translate into a dynamic event tree model based on the sampling analysis results
Sequence Tree Modeling for Combined Accident and Feed-and-Bleed Operation
Energy Technology Data Exchange (ETDEWEB)
Kim, Bo Gyung; Kang Hyun Gook [KAIST, Daejeon (Korea, Republic of); Yoon, Ho Joon [Khalifa University of Science, Abu Dhabi (United Arab Emirates)
2016-05-15
In order to address this issue, this study suggests the sequence tree model to analyze accident sequence systematically. Using the sequence tree model, all possible scenarios which need a specific safety action to prevent the core damage can be identified and success conditions of safety action under complicated situation such as combined accident will be also identified. Sequence tree is branch model to divide plant condition considering the plant dynamics. Since sequence tree model can reflect the plant dynamics, arising from interaction of different accident timing and plant condition and from the interaction between the operator action, mitigation system, and the indicators for operation, sequence tree model can be used to develop the dynamic event tree model easily. Target safety action for this study is a feed-and-bleed (F and B) operation. A F and B operation directly cools down the reactor cooling system (RCS) using the primary cooling system when residual heat removal by the secondary cooling system is not available. In this study, a TLOFW accident and a TLOFW accident with LOCA were the target accidents. Based on the conventional PSA model and indicators, the sequence tree model for a TLOFW accident was developed. If sampling analysis is performed, practical accident sequences can be identified based on the sequence analysis. If a realistic distribution for the variables can be obtained for sampling analysis, much more realistic accident sequences can be described. Moreover, if the initiating event frequency under a combined accident can be quantified, the sequence tree model can translate into a dynamic event tree model based on the sampling analysis results.
Fang, F. J.
2017-12-01
Reconciling observations at fundamentally different scales is central in understanding the global carbon cycle. This study investigates a model-based melding of forest inventory data, remote-sensing data and micrometeorological-station data ("flux towers" estimating forest heat, CO2 and H2O fluxes). The individual tree-based model FORCCHN was used to evaluate the tree DBH increment and forest carbon fluxes. These are the first simultaneous simulations of the forest carbon budgets from flux towers and individual-tree growth estimates of forest carbon budgets using the continuous forest inventory data — under circumstances in which both predictions can be tested. Along with the global implications of such findings, this also improves the capacity for forest sustainable management and the comprehensive understanding of forest ecosystems. In forest ecology, diameter at breast height (DBH) of a tree significantly determines an individual tree's cross-sectional sapwood area, its biomass and carbon storage. Evaluation the annual DBH increment (ΔDBH) of an individual tree is central to understanding tree growth and forest ecology. Ecosystem Carbon flux is a consequence of key ecosystem processes in the forest-ecosystem carbon cycle, Gross and Net Primary Production (GPP and NPP, respectively) and Net Ecosystem Respiration (NEP). All of these closely relate with tree DBH changes and tree death. Despite advances in evaluating forest carbon fluxes with flux towers and forest inventories for individual tree ΔDBH, few current ecological models can simultaneously quantify and predict the tree ΔDBH and forest carbon flux.
Component-based modeling of systems for automated fault tree generation
International Nuclear Information System (INIS)
Majdara, Aref; Wakabayashi, Toshio
2009-01-01
One of the challenges in the field of automated fault tree construction is to find an efficient modeling approach that can support modeling of different types of systems without ignoring any necessary details. In this paper, we are going to represent a new system of modeling approach for computer-aided fault tree generation. In this method, every system model is composed of some components and different types of flows propagating through them. Each component has a function table that describes its input-output relations. For the components having different operational states, there is also a state transition table. Each component can communicate with other components in the system only through its inputs and outputs. A trace-back algorithm is proposed that can be applied to the system model to generate the required fault trees. The system modeling approach and the fault tree construction algorithm are applied to a fire sprinkler system and the results are presented
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling
Galelli, S.; Castelletti, A.
2013-07-01
Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.
The Prediction of Drought-Related Tree Mortality in Vegetation Models
Schwinning, S.; Jensen, J.; Lomas, M. R.; Schwartz, B.; Woodward, F. I.
2013-12-01
Drought-related tree die-off events at regional scales have been reported from all wooded continents and it has been suggested that their frequency may be increasing. The prediction of these drought-related die-off events from regional to global scales has been recognized as a critical need for the conservation of forest resources and improving the prediction of climate-vegetation interactions. However, there is no conceptual consensus on how to best approach the quantitative prediction of tree mortality. Current models use a variety of mechanisms to represent demographic events. Mortality is modeled to represent a number of different processes, including death by fire, wind throw, extreme temperatures, and self-thinning, and each vegetation model differs in the emphasis they place on specific mechanisms. Dynamic global vegetation models generally operate on the assumption of incremental vegetation shift due to changes in the carbon economy of plant functional types and proportional effects on recruitment, growth, competition and mortality, but this may not capture sudden and sweeping tree death caused by extreme weather conditions. We tested several different approaches to predicting tree mortality within the framework of the Sheffield Dynamic Global Vegetation Model. We applied the model to the state of Texas, USA, which in 2011 experienced extreme drought conditions, causing the death of an estimated 300 million trees statewide. We then compared predicted to actual mortality to determine which algorithms most accurately predicted geographical variation in tree mortality. We discuss implications regarding the ongoing debate on the causes of tree death.
Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul
2012-06-01
Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.
Study on reliability analysis based on multilevel flow models and fault tree method
International Nuclear Information System (INIS)
Chen Qiang; Yang Ming
2014-01-01
Multilevel flow models (MFM) and fault tree method describe the system knowledge in different forms, so the two methods express an equivalent logic of the system reliability under the same boundary conditions and assumptions. Based on this and combined with the characteristics of MFM, a method mapping MFM to fault tree was put forward, thus providing a way to establish fault tree rapidly and realizing qualitative reliability analysis based on MFM. Taking the safety injection system of pressurized water reactor nuclear power plant as an example, its MFM was established and its reliability was analyzed qualitatively. The analysis result shows that the logic of mapping MFM to fault tree is correct. The MFM is easily understood, created and modified. Compared with the traditional fault tree analysis, the workload is greatly reduced and the modeling time is saved. (authors)
Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas
2013-11-22
Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.
Mulder, Willem H; Crawford, Forrest W
2015-01-07
Efforts to reconstruct phylogenetic trees and understand evolutionary processes depend fundamentally on stochastic models of speciation and mutation. The simplest continuous-time model for speciation in phylogenetic trees is the Yule process, in which new species are "born" from existing lineages at a constant rate. Recent work has illuminated some of the structural properties of Yule trees, but it remains mostly unknown how these properties affect sequence and trait patterns observed at the tips of the phylogenetic tree. Understanding the interplay between speciation and mutation under simple models of evolution is essential for deriving valid phylogenetic inference methods and gives insight into the optimal design of phylogenetic studies. In this work, we derive the probability distribution of interspecies covariance under Brownian motion and Ornstein-Uhlenbeck models of phenotypic change on a Yule tree. We compute the probability distribution of the number of mutations shared between two randomly chosen taxa in a Yule tree under discrete Markov mutation models. Our results suggest summary measures of phylogenetic information content, illuminate the correlation between site patterns in sequences or traits of related organisms, and provide heuristics for experimental design and reconstruction of phylogenetic trees. Copyright © 2014 Elsevier Ltd. All rights reserved.
3D Visualization of Trees Based on a Sphere-Board Model
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Jiangfeng She
2018-01-01
Full Text Available Because of the smooth interaction of tree systems, the billboard and crossed-plane techniques of image-based rendering (IBR have been used for tree visualization for many years. However, both the billboard-based tree model (BBTM and the crossed-plane tree model (CPTM have several notable limitations; for example, they give an impression of slicing when viewed from the top side, and they produce an unimpressive stereoscopic effect and insufficient lighted effects. In this study, a sphere-board-based tree model (SBTM is proposed to eliminate these defects and to improve the final visual effects. Compared with the BBTM or CPTM, the proposed SBTM uses one or more sphere-like 3D geometric surfaces covered with a virtual texture, which can present more details about the foliage than can 2D planes, to represent the 3D outline of a tree crown. However, the profile edge presented by a continuous surface is overly smooth and regular, and when used to delineate the outline of a tree crown, it makes the tree appear very unrealistic. To overcome this shortcoming and achieve a more natural final visual effect of the tree model, an additional process is applied to the edge of the surface profile. In addition, the SBTM can better support lighted effects because of its cubic geometrical features. Interactive visualization effects for a single tree and a grove are presented in a case study of Sabina chinensis. The results show that the SBTM can achieve a better compromise between realism and performance than can the BBTM or CPTM.
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
DEFF Research Database (Denmark)
Hauberg, Søren; Sloth, Jakob
2008-01-01
For many years, the hidden Markov model (HMM) has been one of the most popular tools for analysing sequential data. One frequently used special case is the left-right model, in which the order of the hidden states is known. If knowledge of the duration of a state is available it is not possible...... to represent it explicitly with an HMM. Methods for modelling duration with HMM's do exist (Rabiner in Proc. IEEE 77(2):257---286, [1989]), but they come at the price of increased computational complexity. Here we present an efficient and robust algorithm for modelling duration in HMM's, and this algorithm...
Mechanical properties of tree roots for soil reinforcement models
Cofie, P.
2001-01-01
Evidence from forestry has shown that part of the forest floor bearing capacity is delivered by tree roots. The beneficial effect however varies and diminishes with increasing number of vehicle passes. Roots potential for reinforcing the soil is known to depend among others on root
Maria Theresa I. Cabaraban; Charles N. Kroll; Satoshi Hirabayashi; David J. Nowak
2013-01-01
A distributed adaptation of i-Tree Eco was used to simulate dry deposition in an urban area. This investigation focused on the effects of varying temperature, LAI, and NO2 concentration inputs on estimated NO2 dry deposition to trees in Baltimore, MD. A coupled modeling system is described, wherein WRF provided temperature...
Yield curve event tree construction for multi stage stochastic programming models
DEFF Research Database (Denmark)
Rasmussen, Kourosh Marjani; Poulsen, Rolf
Dynamic stochastic programming (DSP) provides an intuitive framework for modelling of financial portfolio choice problems where market frictions are present and dynamic re--balancing has a significant effect on initial decisions. The application of these models in practice, however, is limited....... Indeed defining a universal and tractable framework for fully ``appropriate'' event trees is in our opinion an impossible task. A problem specific approach to designing such event trees is the way ahead. In this paper we propose a number of desirable properties which should be present in an event tree...
Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description
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Jan Hackenberg
2014-05-01
Full Text Available This paper presents a method for fitting cylinders into a point cloud, derived from a terrestrial laser-scanned tree. Utilizing high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter smaller than a centimeter. The cylinders are stored as a hierarchical tree-like data structure encapsulating parent-child neighbor relations and incorporating the tree’s direction of growth. This structure enables the efficient extraction of tree components, such as the stem or a single branch. The method was validated both by applying a comparison of the resulting cylinder models with ground truth data and by an analysis between the input point clouds and the models. Tree models were accomplished representing more than 99% of the input point cloud, with an average distance from the cylinder model to the point cloud within sub-millimeter accuracy. After validation, the method was applied to build two allometric models based on 24 tree point clouds as an example of the application. Computation terminated successfully within less than 30 min. For the model predicting the total above ground volume, the coefficient of determination was 0.965, showing the high potential of terrestrial laser-scanning for forest inventories.
3D modeling of olive tree and simulating the harvesting forces
Directory of Open Access Journals (Sweden)
Glăvan Dan Ovidiu
2017-01-01
Full Text Available The paper presents the results of the study regarding the influence of shaking forces on olive tree harvesting systems. Shaking forces can be released through several methods. Important is the end result, namely the shaking force and the cadence of shaking speed. Mechanical and automatic harvesting methods collect more olives than traditional methods but may damage the olive trees. In order to prevent this damage, we need to calculate the necessary shaking force. An original research method is proposed to simulate shaking forces using a 3D olive tree model with Autodesk Inventor software. In the experiments, we use different shaking forces and various shaking speeds. We also use different diameters of the olive tree trunk. We analyze the results from this experiment to determine the optimal shaking force for harvesting olives without damaging the olive tree.
Baudena, Mara|info:eu-repo/dai/nl/340303867; D'Andrea, Fabio; Provenzale, A.
2010-01-01
1. We discuss a simple implicit-space model for the competition of trees and grasses in an idealized savanna environment. The model represents patch occupancy dynamics within the habitat and introduces life stage structure in the tree population, namely adults and seedlings. A tree can be
iTree-Hydro: Snow hydrology update for the urban forest hydrology model
Yang Yang; Theodore A. Endreny; David J. Nowak
2011-01-01
This article presents snow hydrology updates made to iTree-Hydro, previously called the Urban Forest EffectsâHydrology model. iTree-Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process-based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate...
International Nuclear Information System (INIS)
Ding, Y.; Arai, K.
2007-01-01
A method for estimation of forest parameters, species, tree shape, distance between canopies by means of Monte-Carlo based radiative transfer model with forestry surface model is proposed. The model is verified through experiments with the miniature model of forest, tree array of relatively small size of trees. Two types of miniature trees, ellipse-looking and cone-looking canopy are examined in the experiments. It is found that the proposed model and experimental results show a coincidence so that the proposed method is validated. It is also found that estimation of tree shape, trunk tree distance as well as distinction between deciduous or coniferous trees can be done with the proposed model. Furthermore, influences due to multiple reflections between trees and interaction between trees and under-laying grass are clarified with the proposed method
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. Copyright © 2013. Published by Elsevier Ltd.
Truth-telling and Nash equilibria in minimum cost spanning tree models
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Tvede, Mich
2012-01-01
In this paper we consider the minimum cost spanning tree model. We assume that a central planner aims at implementing a minimum cost spanning tree not knowing the true link costs. The central planner sets up a game where agents announce link costs, a tree is chosen and costs are allocated according...... to the rules of the game. We characterize ways of allocating costs such that true announcements constitute Nash equilibria both in case of full and incomplete information. In particular, we find that the Shapley rule based on the irreducible cost matrix is consistent with truthful announcements while a series...
MRI-based decision tree model for diagnosis of biliary atresia.
Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung
2018-02-23
To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.
Effect of different tree mortality patterns on stand development in the forest model SIBYLA
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Trombik Jiří
2016-09-01
Full Text Available Forest mortality critically affects stand structure and the quality of ecosystem services provided by forests. Spruce bark beetle (Ips typographus generates rather complex infestation and mortality patterns, and implementation of such patterns in forest models is challenging. We present here the procedure, which allows to simulate the bark beetle-related tree mortality in the forest dynamics model Sibyla. We explored how sensitive various production and stand structure indicators are to tree mortality patterns, which can be generated by bark beetles. We compared the simulation outputs for three unmanaged forest stands with 40, 70 and 100% proportion of spruce as affected by the disturbance-related mortality that occurred in a random pattern and in a patchy pattern. The used tree species and age class-specific mortality rates were derived from the disturbance-related mortality records from Slovakia. The proposed algorithm was developed in the SQLite using the Python language, and the algorithm allowed us to define the degree of spatial clustering of dead trees ranging from a random distribution to a completely clustered distribution; a number of trees that died in either mode is set to remain equal. We found significant differences between the long-term developments of the three investigated forest stands, but we found very little effect of the tested mortality modes on stand increment, tree species composition and diversity, and tree size diversity. Hence, our hypothesis that the different pattern of dead trees emergence should affect the competitive interactions between trees and regeneration, and thus affect selected productivity and stand structure indicators was not confirmed.
Above- and Belowground Biomass Models for Trees in the Miombo Woodlands of Malawi
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Daud J. Kachamba
2016-02-01
Full Text Available In this study we present general (multiple tree species from several sites above- and belowground biomass models for trees in the miombo woodlands of Malawi. Such models are currently lacking in the country. The modelling was based on 74 trees comprising 33 different species with diameters at breast height (dbh and total tree height (ht ranging from 5.3 to 2 cm and from 3.0 to 25.0 m, respectively. Trees were collected from four silvicultural zones covering a wide range of conditions. We tested different models including dbh, ht and wood specific gravity ( ρ as independent variables. We evaluated model performance using pseudo-R2, root mean square error (RMSE, a covariance matrix for the parameter estimates, mean prediction error (MPE and relative mean prediction error (MPE%. Computation of MPE% was based on leave-one-out cross-validation. Values of pseudo-R2 and MPE% ranged 0.82–0.97 and 0.9%–2.8%, respectively. Model performance indicated that the models can be used over a wide range of geographical and ecological conditions in Malawi.
A homeostatic-partly dynamic model for 137Cs in trees
International Nuclear Information System (INIS)
Frissel, M.
1994-01-01
A model has been developed to describe the behaviour of 137 Cs in trees. The core of the model is the assumption that 137 Cs/K ratio in soil determines the 137 Cs/K ratio in various parts of a tree. This is an equilibrium model but taking into account the time dependence of Cs/K ratio in the soil (caused by K-fertilization) it has been extended to a dynamic model. The model desribes a growing tree. Four compartments are considered: soil; easily accessible parts of the tree; woody parts difficult to access; fruits or leaves. The model is homeostatic, i.e. all 137 Cs concentrations and fluxes are controlled by K concentrations and fluxes, respectively. The addition of K-fertilizer to the soil manifests itself in an immediate change of the Cs/K ratio in the soil and in the easily accessible plant parts, but only slowly - in the woody parts. Also an excess of Cs in the woody part is only slowly released. Important processes are the discrimination between Cs and K and the luxurious consumption of K. The cycling of K in the system (throughput of K via falling leaves, branches, etc.) is also important. Furthermore, a good insight in accessibility of the various parts of the tree seems required, the division in only three compartments, as in the model is probably unsufficient. (author)
Don C. Bragg
2002-01-01
This article is an introduction to the computer software used by the Potential Relative Increment (PRI) approach to optimal tree diameter growth modeling. These DOS programs extract qualified tree and plot data from the Eastwide Forest Inventory Data Base (EFIDB), calculate relative tree increment, sort for the highest relative increments by diameter class, and...
HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data
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Maher Christopher A
2010-07-01
Full Text Available Abstract Background Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP with next-generation sequencing, (ChIP-Seq. This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method. Results Here we introduce HPeak, a Hidden Markov model (HMM-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage. Conclusions Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
An object-oriented forest landscape model and its representation of tree species
Hong S. He; David J. Mladenoff; Joel Boeder
1999-01-01
LANDIS is a forest landscape model that simulates the interaction of large landscape processes and forest successional dynamics at tree species level. We discuss how object-oriented design (OOD) approaches such as modularity, abstraction and encapsulation are integrated into the design of LANDIS. We show that using OOD approaches, model decisions (olden as model...
Inverse modeling and animation of growing single-stemmed trees at interactive rates
S. Rudnick; L. Linsen; E.G. McPherson
2007-01-01
For city planning purposes, animations of growing trees of several species can be used to deduce which species may best fit a particular environment. The models used for the animation must conform to real measured data. We present an approach for inverse modeling to fit global growth parameters. The model comprises local production rules, which are iteratively and...
Directory of Open Access Journals (Sweden)
Esther Merlo
2014-04-01
Full Text Available Aim of study: Modelling the structural quality of Pinus pinaster Ait. wood on the basis of measurements made on standing trees is essential because of the importance of the species in the Galician forestry and timber industries and the good mechanical properties of its wood. In this study, we investigated how timber stiffness is affected by tree and stand properties, climatic and edaphic characteristics and competition. Area of study: The study was performed in Galicia, north-western Spain.Material and methods: Ten pure and even-aged P. pinaster stands were selected and tree and stand variables and the stress wave velocity of 410 standing trees were measured. A sub-sample of 73 trees, representing the variability in acoustic velocity, were felled and sawed into structural timber pieces (224 which were subjected to a bending test to determine the modulus of elasticity (MOE. Main results: Linear models including wood properties explained more than 97%, 73% and 60% of the observed MOE variability at site, tree and board level, respectively, with acoustic velocity and wood density as the main regressors. Other linear models, which did not include wood density, explained more than 88%, 69% and 55% of the observed MOE variability at site, tree and board level, respectively, with acoustic velocity as the main regressor. Moreover, a classification tree for estimating the visual grade according to standard UNE 56544:2011 was developed. Research highlights: The results have demonstrated the usefulness of acoustic velocity for predicting MOE in standing trees. The use of the fitted equations together with existing dynamic growth models will enable preliminary assessment of timber stiffness in relation to different silvicultural alternatives used with this species.Keywords: stress wave velocity, modulus of elasticity, site index, competition index, stepwise regression, CART.
International Nuclear Information System (INIS)
Mukhamedov, Farrukh; Saburov, Mansoor
2010-06-01
In the present paper we study forward Quantum Markov Chains (QMC) defined on a Cayley tree. Using the tree structure of graphs, we give a construction of quantum Markov chains on a Cayley tree. By means of such constructions we prove the existence of a phase transition for the XY-model on a Cayley tree of order three in QMC scheme. By the phase transition we mean the existence of two distinct QMC for the given family of interaction operators {K }. (author)
Oncogenetic tree model of somatic mutations and DNA methylation in colon tumors.
Sweeney, Carol; Boucher, Kenneth M; Samowitz, Wade S; Wolff, Roger K; Albertsen, Hans; Curtin, Karen; Caan, Bette J; Slattery, Martha L
2009-01-01
Our understanding of somatic alterations in colon cancer has evolved from a concept of a series of events taking place in a single sequence to a recognition of multiple pathways. An oncogenetic tree is a model intended to describe the pathways and sequence of somatic alterations in carcinogenesis without assuming that tumors will fall in mutually exclusive categories. We applied this model to data on colon tumor somatic alterations. An oncogenetic tree model was built using data on mutations of TP53, KRAS2, APC, and BRAF genes, methylation at CpG sites of MLH1 and TP16 genes, methylation in tumor (MINT) markers, and microsatellite instability (MSI) for 971 colon tumors from a population-based series. Oncogenetic tree analysis resulted in a reproducible tree with three branches. The model represents methylation of MINT markers as initiating a branch and predisposing to MSI, methylation of MHL1 and TP16, and BRAF mutation. APC mutation is the first alteration in an independent branch and is followed by TP53 mutation. KRAS2 mutation was placed a third independent branch, implying that it neither depends on, nor predisposes to, the other alterations. Individual tumors were observed to have alteration patterns representing every combination of one, two, or all three branches. The oncogenetic tree model assumptions are appropriate for the observed heterogeneity of colon tumors, and the model produces a useful visual schematic of the sequence of events in pathways of colon carcinogenesis.
Development of a model of the coronary arterial tree for the 4D XCAT phantom
International Nuclear Information System (INIS)
Fung, George S K; Tsui, Benjamin M W; Segars, W Paul; Gullberg, Grant T
2011-01-01
A detailed three-dimensional (3D) model of the coronary artery tree with cardiac motion has great potential for applications in a wide variety of medical imaging research areas. In this work, we first developed a computer-generated 3D model of the coronary arterial tree for the heart in the extended cardiac-torso (XCAT) phantom, thereby creating a realistic computer model of the human anatomy. The coronary arterial tree model was based on two datasets: (1) a gated cardiac dual-source computed tomography (CT) angiographic dataset obtained from a normal human subject and (2) statistical morphometric data of porcine hearts. The initial proximal segments of the vasculature and the anatomical details of the boundaries of the ventricles were defined by segmenting the CT data. An iterative rule-based generation method was developed and applied to extend the coronary arterial tree beyond the initial proximal segments. The algorithm was governed by three factors: (1) statistical morphometric measurements of the connectivity, lengths and diameters of the arterial segments; (2) avoidance forces from other vessel segments and the boundaries of the myocardium, and (3) optimality principles which minimize the drag force at the bifurcations of the generated tree. Using this algorithm, the 3D computational model of the largest six orders of the coronary arterial tree was generated, which spread across the myocardium of the left and right ventricles. The 3D coronary arterial tree model was then extended to 4D to simulate different cardiac phases by deforming the original 3D model according to the motion vector map of the 4D cardiac model of the XCAT phantom at the corresponding phases. As a result, a detailed and realistic 4D model of the coronary arterial tree was developed for the XCAT phantom by imposing constraints of anatomical and physiological characteristics of the coronary vasculature. This new 4D coronary artery tree model provides a unique simulation tool that can be
Rooting phylogenetic trees under the coalescent model using site pattern probabilities.
Tian, Yuan; Kubatko, Laura
2017-12-19
Phylogenetic tree inference is a fundamental tool to estimate ancestor-descendant relationships among different species. In phylogenetic studies, identification of the root - the most recent common ancestor of all sampled organisms - is essential for complete understanding of the evolutionary relationships. Rooted trees benefit most downstream application of phylogenies such as species classification or study of adaptation. Often, trees can be rooted by using outgroups, which are species that are known to be more distantly related to the sampled organisms than any other species in the phylogeny. However, outgroups are not always available in evolutionary research. In this study, we develop a new method for rooting species tree under the coalescent model, by developing a series of hypothesis tests for rooting quartet phylogenies using site pattern probabilities. The power of this method is examined by simulation studies and by application to an empirical North American rattlesnake data set. The method shows high accuracy across the simulation conditions considered, and performs well for the rattlesnake data. Thus, it provides a computationally efficient way to accurately root species-level phylogenies that incorporates the coalescent process. The method is robust to variation in substitution model, but is sensitive to the assumption of a molecular clock. Our study establishes a computationally practical method for rooting species trees that is more efficient than traditional methods. The method will benefit numerous evolutionary studies that require rooting a phylogenetic tree without having to specify outgroups.
Shi, Pei-Jian; Huang, Jian-Guo; Hui, Cang; Grissino-Mayer, Henri D; Tardif, Jacques C; Zhai, Li-Hong; Wang, Fu-Sheng; Li, Bai-Lian
2015-01-01
Tree-rings are often assumed to approximate a circular shape when estimating forest productivity and carbon dynamics. However, tree rings are rarely, if ever, circular, thereby possibly resulting in under- or over-estimation in forest productivity and carbon sequestration. Given the crucial role played by tree ring data in assessing forest productivity and carbon storage within a context of global change, it is particularly important that mathematical models adequately render cross-sectional area increment derived from tree rings. We modeled the geometric shape of tree rings using the superellipse equation and checked its validation based on the theoretical simulation and six actual cross sections collected from three conifers. We found that the superellipse better describes the geometric shape of tree rings than the circle commonly used. We showed that a spiral growth trend exists on the radial section over time, which might be closely related to spiral grain along the longitudinal axis. The superellipse generally had higher accuracy than the circle in predicting the basal area increment, resulting in an improved estimate for the basal area. The superellipse may allow better assessing forest productivity and carbon storage in terrestrial forest ecosystems.
Thematic and spatial resolutions affect model-based predictions of tree species distribution.
Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei
2013-01-01
Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.
Directory of Open Access Journals (Sweden)
Juri Taborri
2014-09-01
Full Text Available In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98 for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95 when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.
Anomalous diffusion in a lattice-gas wind-tree model
International Nuclear Information System (INIS)
Kong, X.P.; Cohen, E.G.D.
1989-01-01
Two new strictly deterministic lattice-gas automata derived from Ehrenfest's wind-tree model are studied. While in one model normal diffusion occurs, the other model exhibits abnormal diffusion in that the distribution function of the displacements of the wind particle is non-Gaussian, but its second moment, the mean-square displacement, is proportional to the time, so that a diffusion coefficient can be defined. A connection with the percolation problem and a self-avoiding random walk for the case in which the lattice is completely covered with trees is discussed
Coupled 0D-1D CFD Modeling of Right Heart and Pulmonary Artery Morphometry Tree
Dong, Melody; Yang, Weiguang; Feinstein, Jeffrey A.; Marsden, Alison
2017-11-01
Pulmonary arterial hypertension (PAH) is characterized by elevated pulmonary artery (PA) pressure and remodeling of the distal PAs resulting in right ventricular (RV) dysfunction and failure. It is hypothesized that patients with untreated ventricular septal defects (VSD) may develop PAH due to elevated flows and pressures in the PAs. Wall shear stress (WSS), due to elevated flows, and circumferential stress, due to elevated pressures, are known to play a role in vascular mechanobiology. Thus, simulating VSD hemodynamics and wall mechanics may facilitate our understanding of mechanical stimuli leading to PAH initiation and progression. Although 3D CFD models can capture detailed hemodynamics in the proximal PAs, they cannot easily model hemodynamics and wave propagation in the distal PAs, where remodeling occurs. To improve current PA models, we will present a new method that couples distal PA hemodynamics with RV function. Our model couples a 0D lumped parameter model of the RV to a 1D model of the PA tree, based on human PA morphometry data, to characterize RV performance and WSS changes in the PA tree. We will compare a VSD 0D-1D model and a 0D-3D model coupled to a mathematical morphometry tree model to quantify WSS in the entire PA vascular tree.
Low Tree-Growth Elasticity of Forest Biomass Indicated by an Individual-Based Model
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Robbie A. Hember
2018-01-01
Full Text Available Environmental conditions and silviculture fundamentally alter the metabolism of individual trees and, therefore, need to be studied at that scale. However, changes in forest biomass density (Mg C ha−1 may be decoupled from changes in growth (kg C year−1 when the latter also accelerates the life cycle of trees and strains access to light, nutrients, and water. In this study, we refer to an individual-based model of forest biomass dynamics to constrain the magnitude of system feedbacks associated with ontogeny and competition and estimate the scaling relationship between changes in tree growth and forest biomass density. The model was driven by fitted equations of annual aboveground biomass growth (Gag, probability of recruitment (Pr, and probability of mortality (Pm parameterized against field observations of black spruce (Picea mariana (Mill. BSP, interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn. Franco, and western hemlock (Tsuga heterophylla (Raf. Sarg.. A hypothetical positive step-change in mean tree growth was imposed half way through the simulations and landscape-scale responses were then evaluated by comparing pre- and post-stimulus periods. Imposing a 100% increase in tree growth above calibrated predictions (i.e., contemporary rates only translated into 36% to 41% increases in forest biomass density. This corresponded with a tree-growth elasticity of forest biomass (εG,SB ranging from 0.33 to 0.55. The inelastic nature of stand biomass density was attributed to the dependence of mortality on intensity of competition and tree size, which decreased stand density by 353 to 495 trees ha−1, and decreased biomass residence time by 10 to 23 years. Values of εG,SB depended on the magnitude of the stimulus. For example, a retrospective scenario in which tree growth increased from 50% below contemporary rates up to contemporary rates indicated values of εG,SB ranging from 0.66 to 0.75. We conclude that: (1 effects of
Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling
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Mehrab Sayadi
2017-06-01
Full Text Available Background: Type 2 Diabetes Mellitus (T2DM is one of the most important risk factors in cardiovascular disorders considered as a common clinical and public health problem. Early diagnosis can reduce the burden of the disease. Decision tree, as an advanced data mining method, can be used as a reliable tool to predict T2DM. Objectives: This study aimed to present a simple model for predicting T2DM using decision tree modeling. Materials and Methods: This analytical model-based study used a part of the cohort data obtained from a database in Healthy Heart House of Shiraz, Iran. The data included routine information, such as age, gender, Body Mass Index (BMI, family history of diabetes, and systolic and diastolic blood pressure, which were obtained from the individuals referred for gathering baseline data in Shiraz cohort study from 2014 to 2015. Diabetes diagnosis was used as binary datum. Decision tree technique and J48 algorithm were applied using the WEKA software (version 3.7.5, New Zealand. Additionally, Receiver Operator Characteristic (ROC curve and Area Under Curve (AUC were used for checking the goodness of fit. Results: The age of the 11302 cases obtained after data preparation ranged from 18 to 89 years with the mean age of 48.1 ± 11.4 years. Additionally, 51.1% of the cases were male. In the tree structure, blood pressure and age were placed where most information was gained. In our model, however, gender was not important and was placed on the final branch of the tree. Total precision and AUC were 87% and 89%, respectively. This indicated that the model had good accuracy for distinguishing patients from normal individuals. Conclusions: The results showed that T2DM could be predicted via decision tree model without laboratory tests. Thus, this model can be used in pre-clinical and public health screening programs.
Zhou, Xiaoqi; Wang, Shen S. J.; Chen, Chengrong
2017-12-01
Forest plantations have been widely used as an effective measure for increasing soil carbon (C), and nitrogen (N) stocks and soil enzyme activities play a key role in soil C and N losses during decomposition of soil organic matter. However, few studies have been carried out to elucidate the mechanisms behind the differences in soil C and N cycling by different tree species in response to climate warming. Here, we measured the responses of soil's extracellular enzyme activity (EEA) to a gradient of temperatures using incubation methods in 78-year-old forest plantations with different tree species. Based on a soil enzyme kinetics model, we established a new statistical model to investigate the effects of temperature and tree species on soil EEA. In addition, we established a tree species-enzyme-C/N model to investigate how temperature and tree species influence soil C/N contents over time without considering plant C inputs. These extracellular enzymes included C acquisition enzymes (β-glucosidase, BG), N acquisition enzymes (N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP) and phosphorus acquisition enzymes (acid phosphatases). The results showed that incubation temperature and tree species significantly influenced all soil EEA and Eucalyptus had 1.01-2.86 times higher soil EEA than coniferous tree species. Modeling showed that Eucalyptus had larger soil C losses but had 0.99-2.38 times longer soil C residence time than the coniferous tree species over time. The differences in the residual soil C and N contents between Eucalyptus and coniferous tree species, as well as between slash pine (Pinus elliottii Engelm. var. elliottii) and hoop pine (Araucaria cunninghamii Ait.), increase with time. On the other hand, the modeling results help explain why exotic slash pine can grow faster, as it has 1.22-1.38 times longer residual soil N residence time for LAP, which mediate soil N cycling in the long term, than native coniferous tree species like hoop pine and
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X. Zhou
2017-12-01
Full Text Available Forest plantations have been widely used as an effective measure for increasing soil carbon (C, and nitrogen (N stocks and soil enzyme activities play a key role in soil C and N losses during decomposition of soil organic matter. However, few studies have been carried out to elucidate the mechanisms behind the differences in soil C and N cycling by different tree species in response to climate warming. Here, we measured the responses of soil's extracellular enzyme activity (EEA to a gradient of temperatures using incubation methods in 78-year-old forest plantations with different tree species. Based on a soil enzyme kinetics model, we established a new statistical model to investigate the effects of temperature and tree species on soil EEA. In addition, we established a tree species–enzyme–C∕N model to investigate how temperature and tree species influence soil C∕N contents over time without considering plant C inputs. These extracellular enzymes included C acquisition enzymes (β-glucosidase, BG, N acquisition enzymes (N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP and phosphorus acquisition enzymes (acid phosphatases. The results showed that incubation temperature and tree species significantly influenced all soil EEA and Eucalyptus had 1.01–2.86 times higher soil EEA than coniferous tree species. Modeling showed that Eucalyptus had larger soil C losses but had 0.99–2.38 times longer soil C residence time than the coniferous tree species over time. The differences in the residual soil C and N contents between Eucalyptus and coniferous tree species, as well as between slash pine (Pinus elliottii Engelm. var. elliottii and hoop pine (Araucaria cunninghamii Ait., increase with time. On the other hand, the modeling results help explain why exotic slash pine can grow faster, as it has 1.22–1.38 times longer residual soil N residence time for LAP, which mediate soil N cycling in the long term, than native
Rich Interfaces for Dependability: Compositional Methods for Dynamic Fault Trees and Arcade models
Boudali, H.; Crouzen, Pepijn; Haverkort, Boudewijn R.H.M.; Kuntz, G.W.M.; Stoelinga, Mariëlle Ida Antoinette
This paper discusses two behavioural interfaces for reliability analysis: dynamic fault trees, which model the system reliability in terms of the reliability of its components and Arcade, which models the system reliability at an architectural level. For both formalisms, the reliability is analyzed
New efficient utility upper bounds for the fully adaptive model of attack trees
Buldas, Ahto; Lenin, Aleksandr
2013-01-01
We present a new fully adaptive computational model for attack trees that allows attackers to repeat atomic attacks if they fail and to play on if they are caught and have to pay penalties. The new model allows safer conclusions about the security of real-life systems and is somewhat
Using decision tree induction systems for modeling space-time behavior
Arentze, T.A.; Hofman, F.; Mourik, van H.; Timmermans, H.J.P.; Wets, G.
2000-01-01
Discrete choice models are commonly used to predict individuals' activity and travel choices either separately or simultaneously in activity scheduling models. This paper investigates the possibilities of decision tree induction systems as an alternative approach. The ability of decision frees to
Behavior and sensitivity of an optimal tree diameter growth model under data uncertainty
Don C. Bragg
2005-01-01
Using loblolly pine, shortleaf pine, white oak, and northern red oak as examples, this paper considers the behavior of potential relative increment (PRI) models of optimal tree diameter growth under data uncertainity. Recommendations on intial sample size and the PRI iteractive curve fitting process are provided. Combining different state inventories prior to PRI model...
A method for generating stochastic 3D tree models with Python in Autodesk Maya
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Nemanja Stojanović
2016-12-01
Full Text Available This paper introduces a method for generating 3D tree models using stochastic L-systems with stochastic parameters and Perlin noise. L-system is the most popular method for plant modeling and Perlin noise is extensively used for generating high detailed textures. Our approach is probabilistic. L-systems with a random choice of parameters can represent observed objects quite well and they are used for modeling and generating realistic plants. Textures and normal maps are generated with combinations of Perlin noises what make these trees completely unique. Script for generating these trees, textures and normal maps is written with Python/PyMEL/NumPy in Autodesk Maya.
Event and fault tree model for reliability analysis of the greek research reactor
International Nuclear Information System (INIS)
Albuquerque, Tob R.; Guimaraes, Antonio C.F.; Moreira, Maria de Lourdes
2013-01-01
Fault trees and event trees are widely used in industry to model and to evaluate the reliability of safety systems. Detailed analyzes in nuclear installations require the combination of these two techniques. This work uses the methods of fault tree (FT) and event tree (ET) to perform the Probabilistic Safety Assessment (PSA) in research reactors. The PSA according to IAEA (International Atomic Energy Agency) is divided into Level 1, Level 2 and level 3. At Level 1, conceptually safety systems act to prevent the accident, at Level 2, the accident occurred and seeks to minimize the consequences, known as stage management of the accident, and at Level 3 are determined consequences. This paper focuses on Level 1 studies, and searches through the acquisition of knowledge consolidation of methodologies for future reliability studies. The Greek Research Reactor, GRR - 1, was used as a case example. The LOCA (Loss of Coolant Accident) was chosen as the initiating event and from there were developed the possible accident sequences, using event tree, which could lead damage to the core. Furthermore, for each of the affected systems, the possible accidents sequences were made fault tree and evaluated the probability of each event top of the FT. The studies were conducted using a commercial computational tool SAPHIRE. The results thus obtained, performance or failure to act of the systems analyzed were considered satisfactory. This work is directed to the Greek Research Reactor due to data availability. (author)
Event and fault tree model for reliability analysis of the greek research reactor
Energy Technology Data Exchange (ETDEWEB)
Albuquerque, Tob R.; Guimaraes, Antonio C.F.; Moreira, Maria de Lourdes, E-mail: atalbuquerque@ien.gov.br, E-mail: btony@ien.gov.br, E-mail: malu@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2013-07-01
Fault trees and event trees are widely used in industry to model and to evaluate the reliability of safety systems. Detailed analyzes in nuclear installations require the combination of these two techniques. This work uses the methods of fault tree (FT) and event tree (ET) to perform the Probabilistic Safety Assessment (PSA) in research reactors. The PSA according to IAEA (International Atomic Energy Agency) is divided into Level 1, Level 2 and level 3. At Level 1, conceptually safety systems act to prevent the accident, at Level 2, the accident occurred and seeks to minimize the consequences, known as stage management of the accident, and at Level 3 are determined consequences. This paper focuses on Level 1 studies, and searches through the acquisition of knowledge consolidation of methodologies for future reliability studies. The Greek Research Reactor, GRR - 1, was used as a case example. The LOCA (Loss of Coolant Accident) was chosen as the initiating event and from there were developed the possible accident sequences, using event tree, which could lead damage to the core. Furthermore, for each of the affected systems, the possible accidents sequences were made fault tree and evaluated the probability of each event top of the FT. The studies were conducted using a commercial computational tool SAPHIRE. The results thus obtained, performance or failure to act of the systems analyzed were considered satisfactory. This work is directed to the Greek Research Reactor due to data availability. (author)
Comparing Two Different Approaches to the Modeling of the Common Cause Failures in Fault Trees
International Nuclear Information System (INIS)
Vukovic, I.; Mikulicic, V.; Vrbanic, I.
2002-01-01
The potential for common cause failures in systems that perform critical functions has been recognized as very important contributor to risk associated with operation of nuclear power plants. Consequentially, modeling of common cause failures (CCF) in fault trees has become one among the essential elements in any probabilistic safety assessment (PSA). Detailed and realistic representation of CCF potential in fault tree structure is sometimes very challenging task. This is especially so in the cases where a common cause group involves more than two components. During the last ten years the difficulties associated with this kind of modeling have been overcome to some degree by development of integral PSA tools with high capabilities. Some of them allow for the definition of CCF groups and their automated expanding in the process of Boolean resolution and generation of minimal cutsets. On the other hand, in PSA models developed and run by more traditional tools, CCF-potential had to be modeled in the fault trees explicitly. With explicit CCF modeling, fault trees can grow very large, especially in the cases when they involve CCF groups with 3 or more members, which can become an issue for the management of fault trees and basic events with traditional non-integral PSA models. For these reasons various simplifications had to be made. Speaking in terms of an overall PSA model, there are also some other issues that need to be considered, such as maintainability and accessibility of the model. In this paper a comparison is made between the two approaches to CCF modeling. Analysis is based on a full-scope Level 1 PSA model for internal initiating events that had originally been developed by a traditional PSA tool and later transferred to a new-generation PSA tool with automated CCF modeling capabilities. Related aspects and issues mentioned above are discussed in the paper. (author)
MODELING BILL-OF-MATERIAL WITH TREE DATA STRUCTURE: CASE STUDY IN FURNITURE MANUFACTURER
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Toni Prahasto
2012-02-01
Full Text Available This paper presents a modeling of Bill-of-Material with tree data structure. The BOM represents wooden furniture products. The management of BOM is incorporated into an MRP software which is specially built for a furniture manufacturer. The tree data structure is approached with an object oriented programming to provide the creation and modification of the data. The tree object is designed so that a downstream programmer can create an application with high productivity, using the BOM object of course. Legality of the development is ensured by adapting open source resources, i.e. MySQL database engine, PHP server script, and client-side Javascript. The BOM object is used extensively in the MRP software that is being developed. A couple of screenshots are presented to demonstrate the ease of creation and manipulation of Bill-of-Material. The proper approach of modeling BOM with tree structure allows the programmer to reach high productivity during the development of the aforementioned MRP customized software. Keyword : Modeling, Bill of Material, Tree Data Structure
Reversible polymorphism-aware phylogenetic models and their application to tree inference.
Schrempf, Dominik; Minh, Bui Quang; De Maio, Nicola; von Haeseler, Arndt; Kosiol, Carolin
2016-10-21
We present a reversible Polymorphism-Aware Phylogenetic Model (revPoMo) for species tree estimation from genome-wide data. revPoMo enables the reconstruction of large scale species trees for many within-species samples. It expands the alphabet of DNA substitution models to include polymorphic states, thereby, naturally accounting for incomplete lineage sorting. We implemented revPoMo in the maximum likelihood software IQ-TREE. A simulation study and an application to great apes data show that the runtimes of our approach and standard substitution models are comparable but that revPoMo has much better accuracy in estimating trees, divergence times and mutation rates. The advantage of revPoMo is that an increase of sample size per species improves estimations but does not increase runtime. Therefore, revPoMo is a valuable tool with several applications, from speciation dating to species tree reconstruction. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Name segmentation using hidden Markov models and its application in record linkage
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Rita de Cassia Braga Gonçalves
2014-10-01
Full Text Available This study aimed to evaluate the use of hidden Markov models (HMM for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM, Information Subsystem for High Complexity Procedures (APAC, and Hospital Information System (AIH. A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.
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A. Lavergne
2017-11-01
Full Text Available Oxygen isotopes in tree rings (δ18OTR are widely used to reconstruct past climates. However, the complexity of climatic and biological processes controlling isotopic fractionation is not yet fully understood. Here, we use the MAIDENiso model to decipher the variability in δ18OTR of two temperature-sensitive species of relevant palaeoclimatological interest (Picea mariana and Nothofagus pumilio and growing at cold high latitudes in North and South America. In this first modelling study on δ18OTR values in both northeastern Canada (53.86° N and western Argentina (41.10° S, we specifically aim at (1 evaluating the predictive skill of MAIDENiso to simulate δ18OTR values, (2 identifying the physical processes controlling δ18OTR by mechanistic modelling and (3 defining the origin of the temperature signal recorded in the two species. Although the linear regression models used here to predict daily δ18O of precipitation (δ18OP may need to be improved in the future, the resulting daily δ18OP values adequately reproduce observed (from weather stations and simulated (by global circulation model δ18OP series. The δ18OTR values of the two species are correctly simulated using the δ18OP estimation as MAIDENiso input, although some offset in mean δ18OTR levels is observed for the South American site. For both species, the variability in δ18OTR series is primarily linked to the effect of temperature on isotopic enrichment of the leaf water. We show that MAIDENiso is a powerful tool for investigating isotopic fractionation processes but that the lack of a denser isotope-enabled monitoring network recording oxygen fractionation in the soil–vegetation–atmosphere compartments limits our capacity to decipher the processes at play. This study proves that the eco-physiological modelling of δ18OTR values is necessary to interpret the recorded climate signal more reliably.
MB3-Miner: efficiently mining eMBedded subTREEs using Tree Model Guided candidate generation
Tan, H.; Dillon, T.; Hadzic, F.; Chang, E.; Feng, L.
2005-01-01
Tree mining has many useful applications in areas such as Bioinformatics, XML mining, Web mining, etc. In general, most of the formally represented information in these domains is a tree structured form. In this paper we focus on mining frequent embedded subtrees from databases of rooted labeled
The algebra of the general Markov model on phylogenetic trees and networks.
Sumner, J G; Holland, B R; Jarvis, P D
2012-04-01
It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.
Modeling the temporal dynamics of nonstructural carbohydrate pools in forest trees
Energy Technology Data Exchange (ETDEWEB)
Richardson, Andrew [Northern Arizona Univ., Flagstaff, AZ (United States); Harvard Univ., Cambridge, MA (United States)
2017-11-09
Trees store carbohydrates, in the form of sugars and starch, as reserves to be used to power both future growth as well as to support day-to-day metabolic functions. These reserves are particularly important in the context of how trees cope with disturbance and stress—for example, as related to pest outbreaks, wind or ice damage, and extreme climate events. In this project, we measured the size of carbon reserves in forest trees, and determined how quickly these reserves are used and replaced—i.e., their “turnover time”. Our work was conducted at Harvard Forest, a temperate deciduous forest in central Massachusetts. Through field sampling, laboratory-based chemical analyses, and allometric modeling, we scaled these measurements up to whole-tree NSC budgets. We used these data to test and improve computer simulation models of carbon flow through forest ecosystems. Our modeling focused on the mathematical representation of these stored carbon reserves, and we examined the sensitivity of model performance to different model structures. This project contributes to DOE’s goal to improve next-generation models of the earth system, and to understand the impacts of climate change on terrestrial ecosystems.
Neuroevolution Mechanism for Hidden Markov Model
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Nabil M. Hewahi
2011-12-01
Full Text Available Hidden Markov Model (HMM is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language
processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by
applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
Directory of Open Access Journals (Sweden)
Mitra Rahmati
2018-01-01
Full Text Available Climate change projections predict warmer and drier conditions. In general, moderate to severe water stress reduce plant vegetative growth and leaf photosynthesis. However, vegetative and reproductive growths show different sensitivities to water deficit. In fruit trees, water restrictions may have serious implications not only on tree growth and yield, but also on fruit quality, which might be improved. Therefore, it is of paramount importance to understand the complex interrelations among the physiological processes involved in within-tree carbon acquisition and allocation, water uptake and transpiration, organ growth, and fruit composition when affected by water stress. This can be studied using process-based models of plant functioning, which allow assessing the sensitivity of various physiological processes to water deficit and their relative impact on vegetative growth and fruit quality. In the current study, an existing fruit-tree model (QualiTree was adapted for describing the water stress effects on peach (Prunus persica L. Batsch vegetative growth, fruit size and composition. First, an energy balance calculation at the fruit-bearing shoot level and a water transfer formalization within the plant were integrated into the model. Next, a reduction function of vegetative growth according to tree water status was added to QualiTree. Then, the model was parameterized and calibrated for a late-maturing peach cultivar (“Elberta” under semi-arid conditions, and for three different irrigation practices. Simulated vegetative and fruit growth variability over time was consistent with observed data. Sugar concentrations in fruit flesh were well simulated. Finally, QualiTree allowed for determining the relative importance of photosynthesis and vegetative growth reduction on carbon acquisition, plant growth and fruit quality under water constrains. According to simulations, water deficit impacted vegetative growth first through a direct effect on
Rahmati, Mitra; Mirás-Avalos, José M; Valsesia, Pierre; Lescourret, Françoise; Génard, Michel; Davarynejad, Gholam H; Bannayan, Mohammad; Azizi, Majid; Vercambre, Gilles
2018-01-01
Climate change projections predict warmer and drier conditions. In general, moderate to severe water stress reduce plant vegetative growth and leaf photosynthesis. However, vegetative and reproductive growths show different sensitivities to water deficit. In fruit trees, water restrictions may have serious implications not only on tree growth and yield, but also on fruit quality, which might be improved. Therefore, it is of paramount importance to understand the complex interrelations among the physiological processes involved in within-tree carbon acquisition and allocation, water uptake and transpiration, organ growth, and fruit composition when affected by water stress. This can be studied using process-based models of plant functioning, which allow assessing the sensitivity of various physiological processes to water deficit and their relative impact on vegetative growth and fruit quality. In the current study, an existing fruit-tree model (QualiTree) was adapted for describing the water stress effects on peach ( Prunus persica L. Batsch) vegetative growth, fruit size and composition. First, an energy balance calculation at the fruit-bearing shoot level and a water transfer formalization within the plant were integrated into the model. Next, a reduction function of vegetative growth according to tree water status was added to QualiTree. Then, the model was parameterized and calibrated for a late-maturing peach cultivar ("Elberta") under semi-arid conditions, and for three different irrigation practices. Simulated vegetative and fruit growth variability over time was consistent with observed data. Sugar concentrations in fruit flesh were well simulated. Finally, QualiTree allowed for determining the relative importance of photosynthesis and vegetative growth reduction on carbon acquisition, plant growth and fruit quality under water constrains. According to simulations, water deficit impacted vegetative growth first through a direct effect on its sink strength
Hydrodynamics of isohydric and anisohydric trees: insights from models and measurements
Novick, K. A.; Oishi, A. C.; Roman, D. T.; Benson, M. C.; Miniat, C.
2016-12-01
In an effort to understand and predict the mechanisms that govern tree response to hydrologic stress, plant hydraulic theory, which classifies trees along a continuum of isohydric to anisohydric water use strategies, is increasingly being used. Isohydry maintains relatively constant leaf water potential during periods of water stress, promoting wide hydraulic safety margins that reduce the risk of xylem cavitation. In contrast, anisohydry allows leaf water potential to fall as soil water potential falls, but in doing so trees incur a greater risk of hydraulic failure. As a result, unique patterns of stomatal functioning between isohydric and anisohydric species are both predicted and observed in leaf-, tree-, and stand-level water use. We use a novel model formulation to examine the dynamics of three mechanisms that are potentially limiting to leaf-level gas exchange in trees during drought: (1) a `demand limitation' driven by an assumption of stomatal optimization of water loss and carbon uptake; (2) `hydraulic limitation' of water movement from the roots to the leaves; and (3) `non-stomatal' limitations imposed by declining leaf water status within the leaf. Model results suggest that species-specific `economics' of stomatal behavior may play an important role in differentiating species along the continuum of isohydric to anisohydric behavior; specifically, we show that non-stomatal and demand limitations may reduce stomatal conductance and increase leaf water potential, promoting wide safety margins characteristic of isohydric species. Direct comparisons of modeled and measured stomatal conductance further indicated that non-stomatal and demand limitations reproduced observed patterns of tree water use well for an isohydric species but that a hydraulic limitation likely applies in the case of an anisohydric species. This modeling framework used in concert with climate data may help land managers and scientists predict when and what forest species and communities
Suchetana, Bihu; Rajagopalan, Balaji; Silverstein, JoAnn
2017-11-15
A regression tree-based diagnostic approach is developed to evaluate factors affecting US wastewater treatment plant compliance with ammonia discharge permit limits using Discharge Monthly Report (DMR) data from a sample of 106 municipal treatment plants for the period of 2004-2008. Predictor variables used to fit the regression tree are selected using random forests, and consist of the previous month's effluent ammonia, influent flow rates and plant capacity utilization. The tree models are first used to evaluate compliance with existing ammonia discharge standards at each facility and then applied assuming more stringent discharge limits, under consideration in many states. The model predicts that the ability to meet both current and future limits depends primarily on the previous month's treatment performance. With more stringent discharge limits predicted ammonia concentration relative to the discharge limit, increases. In-sample validation shows that the regression trees can provide a median classification accuracy of >70%. The regression tree model is validated using ammonia discharge data from an operating wastewater treatment plant and is able to accurately predict the observed ammonia discharge category approximately 80% of the time, indicating that the regression tree model can be applied to predict compliance for individual treatment plants providing practical guidance for utilities and regulators with an interest in controlling ammonia discharges. The proposed methodology is also used to demonstrate how to delineate reliable sources of demand and supply in a point source-to-point source nutrient credit trading scheme, as well as how planners and decision makers can set reasonable discharge limits in future. Copyright © 2017 Elsevier B.V. All rights reserved.
McHugh, Nicola; Edmondson, Jill L; Gaston, Kevin J; Leake, Jonathan R; O'Sullivan, Odhran S
2015-10-01
The capacity of urban areas to deliver provisioning ecosystem services is commonly overlooked and underutilized. Urban populations have globally increased fivefold since 1950, and they disproportionately consume ecosystem services and contribute to carbon emissions, highlighting the need to increase urban sustainability and reduce environmental impacts of urban dwellers. Here, we investigated the potential for increasing carbon sequestration, and biomass fuel production, by planting trees and short-rotation coppice (SRC), respectively, in a mid-sized UK city as a contribution to meeting national commitments to reduce CO 2 emissions.Iterative GIS models were developed using high-resolution spatial data. The models were applied to patches of public and privately owned urban greenspace suitable for planting trees and SRC, across the 73 km 2 area of the city of Leicester. We modelled tree planting with a species mix based on the existing tree populations, and SRC with willow and poplar to calculate biomass production in new trees, and carbon sequestration into harvested biomass over 25 years.An area of 11 km 2 comprising 15% of the city met criteria for tree planting and had the potential over 25 years to sequester 4200 tonnes of carbon above-ground. Of this area, 5·8 km 2 also met criteria for SRC planting and over the same period this could yield 71 800 tonnes of carbon in harvested biomass.The harvested biomass could supply energy to over 1566 domestic homes or 30 municipal buildings, resulting in avoided carbon emissions of 29 236 tonnes of carbon over 25 years when compared to heating by natural gas. Together with the net carbon sequestration into trees, a total reduction of 33 419 tonnes of carbon in the atmosphere could be achieved in 25 years by combined SRC and tree planting across the city. Synthesis and applications . We demonstrate that urban greenspaces in a typical UK city are underutilized for provisioning ecosystem services by trees and
SVM-dependent pairwise HMM: an application to protein pairwise alignments.
Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F
2017-12-15
Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.
2014-12-01
Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents
Assessing the Cooling Benefits of Tree Shade by an Outdoor Urban Physical Scale Model at Tempe, AZ
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Qunshan Zhao
2018-01-01
Full Text Available Urban green infrastructure, especially shade trees, offers benefits to the urban residential environment by mitigating direct incoming solar radiation on building facades, particularly in hot settings. Understanding the impact of different tree locations and arrangements around residential properties has the potential to maximize cooling and can ultimately guide urban planners, designers, and homeowners on how to create the most sustainable urban environment. This research measures the cooling effect of tree shade on building facades through an outdoor urban physical scale model. The physical scale model is a simulated neighborhood consisting of an array of concrete cubes to represent houses with identical artificial trees. We tested and compared 10 different tree densities, locations, and arrangement scenarios in the physical scale model. The experimental results show that a single tree located at the southeast of the building can provide up to 2.3 °C hourly cooling benefits to east facade of the building. A two-tree cluster arrangement provides more cooling benefits (up to 6.6 °C hourly cooling benefits to the central facade when trees are located near the south and southeast sides of the building. The research results confirm the cooling benefits of tree shade and the importance of wisely designing tree locations and arrangements in the built environment.
Modeling stem increment in individual Pinus occidentalis Sw. trees in La Sierra, Dominican Republic
Energy Technology Data Exchange (ETDEWEB)
Bueno, S.; Bevilacqua, E.
2010-07-01
One of the most common and important tree characteristics used in forest management decision-making is tree diameter-at-breast height (DBH). This paper presents results on an evaluation of two growth functions developed to model stem diameter increases in individual Pinus occidentalis Sw. trees in La Sierra, Dominican Republic. The first model was developed in order to predict future DBH (FDM) at different intervals of time and the other for predicting growth, that is, periodic annual diameter increment (PADIM). Each model employed two statistical techniques for fitting model parameters: stepwise ordinary least squares (OLS) regression, and mixed models. The two statistical approaches varied in how they accounted for the repeated measurements on individual trees over time, affecting standard error estimates and statistical inference of model parameters. Each approach was evaluated based on six goodness of- fit statistics, using both calibration and validation data sets. The objectives were 1) to determine the best model for predicting future tree DBH; 2) to determine the best model for predicting periodic annual diameter increment, both models using tree size, age, site index and different indices of competitive status; and 3) compare which of these two modeling approaches predicts better the future DBH. OLS provided a better fit for both of the growth functions, especially in regards to bias. Both models showed advantages and disadvantages when they were used to predict growth and future diameter. For the prediction of future diameter with FDM, accuracy of predictions were within one centimeter for a five-year projection interval. The PADIM presented negligible bias in estimating future diameter, although there was a small increase in bias as time of prediction increased. As expected, each model was the best in estimating the response variable it was developed for.. However, a closer examination of the distribution of errors showed a slight advantage of the FDM
Uniqueness of Gibbs Measure for Models with Uncountable Set of Spin Values on a Cayley Tree
International Nuclear Information System (INIS)
Eshkabilov, Yu. Kh.; Haydarov, F. H.; Rozikov, U. A.
2013-01-01
We consider models with nearest-neighbor interactions and with the set [0, 1] of spin values, on a Cayley tree of order K ≥ 1. It is known that the ‘splitting Gibbs measures’ of the model can be described by solutions of a nonlinear integral equation. For arbitrary k ≥ 2 we find a sufficient condition under which the integral equation has unique solution, hence under the condition the corresponding model has unique splitting Gibbs measure.
Are self-thinning contraints needed in a tree-specific mortality model.
Robert A. Monserud; Thomas Ledermann; Hubert. Sterba
2005-01-01
Can a tree-specific mortality model elicit expected forest stand density dynamics without imposing stand-level constraints such as Reineke's maximum stand density index (SDI,) or the -312 power law of self-thinning? We examine this emergent properties question using the Austrian stand simulator PROGNAUS. This simulator was chosen specifically because it does not...
Are self-thinning constraints needed in a tree-specific mortality model?
Robert A. Monserud; Thomas Ledermann; Hubert. Sterba
2005-01-01
Can a tree-specific mortality model elicit expected forest stand density dynamics without imposing stand-level constraints such as Reineke's maximum stand density index (SDImax) or the -3/2 power law of self-thinning? We examine this emergent properties question using the Austrian stand simulator PROGNAUS. This simulator was chosen...
A biologically-based individual tree model for managing the longleaf pine ecosystem
Rick Smith; Greg Somers
1998-01-01
Duration: 1995-present Objective: Develop a longleaf pine dynamics model and simulation system to define desirable ecosystem management practices in existing and future longleaf pine stands. Methods: Naturally-regenerated longleaf pine trees are being destructively sampled to measure their recent growth and dynamics. Soils and climate data will be combined with the...
A tree-based method to price American options in the Heston model
Vellekoop, M.; Nieuwenhuis, H.
2009-01-01
We develop an algorithm to price American options on assets that follow the stochastic volatility model defined by Heston. We use an approach which is based on a modification of a combined tree for stock prices and volatilities, where the number of nodes grows quadratically in the number of time
Numerical modeling of flow and pollutant dispersion in street canyons with tree planting
Balczó, M.; Gromke, C.B.; Ruck, B.
2009-01-01
Numerical simulations of the impact of tree planting on airflow and traffic pollutant dispersion in urban street canyons have been performed using the commercial CFD (Computational Fluid Dynamics) code MISKAM. A k-e turbulence model including additional terms for the treatment of vegetation, has
Exact solution of an Ising model with competing interactions on a Cayley tree
Ganikhodjaev, N N; Wahiddin, M R B
2003-01-01
The exact solution of an Ising model with competing restricted interactions on the Cayley tree, and in the absence of an external field is presented. A critical curve is defined where it is possible to get phase transitions above it, and a single Gibbs state is obtained elsewhere.
Francis, Andrew; Moulton, Vincent
2018-06-07
Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Koh, Kwang Yong; Seong, Poong Hyun
2010-01-01
Fault tree analysis (FTA) has suffered from several drawbacks such that it uses only static gates and hence can not capture dynamic behaviors of the complex system precisely, and it is in lack of rigorous semantics, and reasoning process which is to check whether basic events really cause top events is done manually and hence very labor-intensive and time-consuming for the complex systems while it has been one of the most widely used safety analysis technique in nuclear industry. Although several attempts have been made to overcome this problem, they can not still do absolute or actual time modeling because they adapt relative time concept and can capture only sequential behaviors of the system. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA
Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris
2016-09-01
Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have
Fader, Marianela; von Bloh, Werner; Shi, Sinan; Bondeau, Alberte; Cramer, Wolfgang
2016-04-01
In the Mediterranean region, climate and land use change are expected to impact on natural and agricultural ecosystems by warming, reduced rainfall and direct degradation of ecosystems. Human population growth and socioeconomic changes, notably on the Eastern and Southern shores, will require increases in food production and put additional pressure on agro-ecosystems and water resources. Coping with these challenges requires informed decisions that, in turn, require assessments by means of a comprehensive ecohydrological model. Here we present here the inclusion of 10 Mediterranean agricultural plants, mainly perennial crops, in an agro-ecosystem model (LPJmL, "Lund-Potsdam-Jena managed Land"): nut trees, date palms, citrus trees, orchards, olive trees, grapes, cotton, potatoes, vegetables and fodder grasses. The model was then successfully tested in three model outputs: agricultural yields, irrigation requirements and soil carbon density. A first application of the model indicates that, currently, agricultural trees consume in average more irrigation water per hectare than annual crops. Also, different crops show different magnitude of changes in net irrigation requirements due to climate change, being the increases most pronounced in agricultural trees. This is very relevant since the Mediterranean area as a whole might face an increase in gross irrigation requirements between 4% and 74% from climate change and population growth if irrigation systems and conveyance are not improved. Additionally, future water scarcity might pose further challenges to the agricultural sector: Algeria, Libya, Israel, Jordan, Lebanon, Syria, Serbia, Morocco, Tunisia and Spain have a high risk of not being able to sustainably meet future irrigation water requirements in some scenarios by the end of the century (1). The importance of including agricultural trees in the ecohydrological models is also shown in the results concerning soil organic carbon (SOC). Since in former model
A model for the inverse 1-median problem on trees under uncertain costs
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Kien Trung Nguyen
2016-01-01
Full Text Available We consider the problem of justifying vertex weights of a tree under uncertain costs so that a prespecified vertex become optimal and the total cost should be optimal in the uncertainty scenario. We propose a model which delivers the information about the optimal cost which respect to each confidence level \\(\\alpha \\in [0,1]\\. To obtain this goal, we first define an uncertain variable with respect to the minimum cost in each confidence level. If all costs are independently linear distributed, we present the inverse distribution function of this uncertain variable in \\(O(n^{2}\\log n\\ time, where \\(n\\ is the number of vertices in the tree.
A time-dependent event tree technique for modelling recovery operations
International Nuclear Information System (INIS)
Kohut, P.; Fitzpatrick, R.
1991-01-01
The development of a simplified time dependent event tree methodology is presented. The technique is especially applicable to describe recovery operations in nuclear reactor accident scenarios initiated by support system failures. The event tree logic is constructed using time dependent top events combined with a damage function that contains information about the final state time behavior of the reactor core. Both the failure and the success states may be utilized for the analysis. The method is illustrated by modeling the loss of service water function with special emphasis on the RCP [reactor coolant pump] seal LOCA [loss of coolant accident] scenario. 5 refs., 2 figs., 2 tabs
Decision tree based knowledge acquisition and failure diagnosis using a PWR loop vibration model
International Nuclear Information System (INIS)
Bauernfeind, V.; Ding, Y.
1993-01-01
An analytical vibration model of the primary system of a 1300 MW PWR was used for simulating mechanical faults. Deviations in the calculated power density spectra and coherence functions are determined and classified. The decision tree technique is then used for a personal computer supported knowledge presentation and for optimizing the logical relationships between the simulated faults and the observed symptoms. The optimized decision tree forms the knowledge base and can be used to diagnose known cases as well as to include new data into the knowledge base if new faults occur. (author)
Spatial distribution of block falls using volumetric GIS-decision-tree models
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).
Hu, Jia; Moore, David J P; Riveros-Iregui, Diego A; Burns, Sean P; Monson, Russell K
2010-03-01
*Understanding controls over plant-atmosphere CO(2) exchange is important for quantifying carbon budgets across a range of spatial and temporal scales. In this study, we used a simple approach to estimate whole-tree CO(2) assimilation rate (A(Tree)) in a subalpine forest ecosystem. *We analysed the carbon isotope ratio (delta(13)C) of extracted needle sugars and combined it with the daytime leaf-to-air vapor pressure deficit to estimate tree water-use efficiency (WUE). The estimated WUE was then combined with observations of tree transpiration rate (E) using sap flow techniques to estimate A(Tree). Estimates of A(Tree) for the three dominant tree species in the forest were combined with species distribution and tree size to estimate and gross primary productivity (GPP) using an ecosystem process model. *A sensitivity analysis showed that estimates of A(Tree) were more sensitive to dynamics in E than delta(13)C. At the ecosystem scale, the abundance of lodgepole pine trees influenced seasonal dynamics in GPP considerably more than Engelmann spruce and subalpine fir because of its greater sensitivity of E to seasonal climate variation. *The results provide the framework for a nondestructive method for estimating whole-tree carbon assimilation rate and ecosystem GPP over daily-to weekly time scales.
van Mantgem, P.J.; Stephenson, N.L.
2005-01-01
1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.
Modeling time-to-event (survival) data using classification tree analysis.
Linden, Ariel; Yarnold, Paul R
2017-12-01
Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.
Singh, Gyanendra; Sachdeva, S N; Pal, Mahesh
2016-11-01
This work examines the application of M5 model tree and conventionally used fixed/random effect negative binomial (FENB/RENB) regression models for accident prediction on non-urban sections of highway in Haryana (India). Road accident data for a period of 2-6 years on different sections of 8 National and State Highways in Haryana was collected from police records. Data related to road geometry, traffic and road environment related variables was collected through field studies. Total two hundred and twenty two data points were gathered by dividing highways into sections with certain uniform geometric characteristics. For prediction of accident frequencies using fifteen input parameters, two modeling approaches: FENB/RENB regression and M5 model tree were used. Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values. M5 model tree provides simple linear equations that are easy to interpret and provide better insight, indicating that this approach can effectively be used as an alternative to RENB approach if the sole purpose is to predict motor vehicle crashes. Sensitivity analysis using M5 model tree also suggests that its results reflect the physical conditions. Both models clearly indicate that to improve safety on Indian highways minor accesses to the highways need to be properly designed and controlled, the service roads to be made functional and dispersion of speeds is to be brought down. Copyright © 2016 Elsevier Ltd. All rights reserved.
Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen. Fitzgerald
2012-01-01
Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...
Chakra B. Budhathoki; Thomas B. Lynch; James M. Guldin
2010-01-01
Nonlinear mixed-modeling methods were used to estimate parameters in an individual-tree basal area growth model for shortleaf pine (Pinus echinata Mill.). Shortleaf pine individual-tree growth data were available from over 200 permanently established 0.2-acre fixed-radius plots located in naturally-occurring even-aged shortleaf pine forests on the...
Hill, Ryan M; Oosterhoff, Benjamin; Kaplow, Julie B
2017-07-01
Although a large number of risk markers for suicide ideation have been identified, little guidance has been provided to prospectively identify adolescents at risk for suicide ideation within community settings. The current study addressed this gap in the literature by utilizing classification tree analysis (CTA) to provide a decision-making model for screening adolescents at risk for suicide ideation. Participants were N = 4,799 youth (Mage = 16.15 years, SD = 1.63) who completed both Waves 1 and 2 of the National Longitudinal Study of Adolescent to Adult Health. CTA was used to generate a series of decision rules for identifying adolescents at risk for reporting suicide ideation at Wave 2. Findings revealed 3 distinct solutions with varying sensitivity and specificity for identifying adolescents who reported suicide ideation. Sensitivity of the classification trees ranged from 44.6% to 77.6%. The tree with greatest specificity and lowest sensitivity was based on a history of suicide ideation. The tree with moderate sensitivity and high specificity was based on depressive symptoms, suicide attempts or suicide among family and friends, and social support. The most sensitive but least specific tree utilized these factors and gender, ethnicity, hours of sleep, school-related factors, and future orientation. These classification trees offer community organizations options for instituting large-scale screenings for suicide ideation risk depending on the available resources and modality of services to be provided. This study provides a theoretically and empirically driven model for prospectively identifying adolescents at risk for suicide ideation and has implications for preventive interventions among at-risk youth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
A Root water uptake model to compensate disease stress in citrus trees
Peddinti, S. R.; Kambhammettu, B. P.; Lad, R. S.; Suradhaniwar, S.
2017-12-01
Plant root water uptake (RWU) controls a number of hydrologic fluxes in simulating unsaturated flow and transport processes. Variable saturated models that simulate soil-water-plant interactions within the rizhosphere do not account for the health of the tree. This makes them difficult to analyse RWU patterns for diseased trees. Improper irrigation management activities on diseased (Phytopthora spp. affected) citrus trees of central India has resulted in a significant reduction in crop yield accompanied by disease escalation. This research aims at developing a quantitative RWU model that accounts for the reduction in water stress as a function of plant disease level (hereafter called as disease stress). A total of four research plots with varying disease severity were considered for our field experimentation. A three-dimensional electrical resistivity tomography (ERT) was performed to understand spatio-temporal distribution in soil moisture following irrigation. Evaporation and transpiration were monitored daily using micro lysimeter and sap flow meters respectively. Disease intensity was quantified (on 0 to 9 scale) using pathological analysis on soil samples. Pedo-physocal and pedo-electric relations were established under controlled laboratory conditions. A non-linear disease stress response function for citrus trees was derived considering phonological, hydrological, and pathological parameters. Results of numerical simulations conclude that the propagation of error in RWU estimates by ignoring the health condition of the tree is significant. The developed disease stress function was then validated in the presence of deficit water and nutrient stress conditions. Results of numerical analysis showed a good agreement with experimental data, corroborating the need for alternate management practices for disease citrus trees.
Comparison of tree types of models for the prediction of final academic achievement
Directory of Open Access Journals (Sweden)
Silvana Gasar
2002-12-01
Full Text Available For efficient prevention of inappropriate secondary school choices and by that academic failure, school counselors need a tool for the prediction of individual pupil's final academic achievements. Using data mining techniques on pupils' data base and expert modeling, we developed several models for the prediction of final academic achievement in an individual high school educational program. For data mining, we used statistical analyses, clustering and two machine learning methods: developing classification decision trees and hierarchical decision models. Using an expert system shell DEX, an expert system, based on a hierarchical multi-attribute decision model, was developed manually. All the models were validated and evaluated from the viewpoint of their applicability. The predictive accuracy of DEX models and decision trees was equal and very satisfying, as it reached the predictive accuracy of an experienced counselor. With respect on the efficiency and difficulties in developing models, and relatively rapid changing of our education system, we propose that decision trees are used in further development of predictive models.
Biogeochemical modelling vs. tree-ring data - comparison of forest ecosystem productivity estimates
Zorana Ostrogović Sever, Maša; Barcza, Zoltán; Hidy, Dóra; Paladinić, Elvis; Kern, Anikó; Marjanović, Hrvoje
2017-04-01
Forest ecosystems are sensitive to environmental changes as well as human-induce disturbances, therefore process-based models with integrated management modules represent valuable tool for estimating and forecasting forest ecosystem productivity under changing conditions. Biogeochemical model Biome-BGC simulates carbon, nitrogen and water fluxes, and it is widely used for different terrestrial ecosystems. It was modified and parameterised by many researchers in the past to meet the specific local conditions. In this research, we used recently published improved version of the model Biome-BGCMuSo (BBGCMuSo), with multilayer soil module and integrated management module. The aim of our research is to validate modelling results of forest ecosystem productivity (NPP) from BBGCMuSo model with observed productivity estimated from an extensive dataset of tree-rings. The research was conducted in two distinct forest complexes of managed Pedunculate oak in SE Europe (Croatia), namely Pokupsko basin and Spačva basin. First, we parameterized BBGCMuSo model at a local level using eddy-covariance (EC) data from Jastrebarsko EC site. Parameterized model was used for the assessment of productivity on a larger scale. Results of NPP assessment with BBGCMuSo are compared with NPP estimated from tree ring data taken from trees on over 100 plots in both forest complexes. Keywords: Biome-BGCMuSo, forest productivity, model parameterization, NPP, Pedunculate oak
Process based model sheds light on climate sensitivity of Mediterranean tree-ring width
Directory of Open Access Journals (Sweden)
R. Touchan
2012-03-01
Full Text Available We use the process-based VS (Vaganov-Shashkin model to investigate whether a regional Pinus halepensis tree-ring chronology from Tunisia can be simulated as a function of climate alone by employing a biological model linking day length and daily temperature and precipitation (AD 1959–2004 from a climate station to ring-width variations. We check performance of the model on independent data by a validation exercise in which the model's parameters are tuned using data for 1982–2004 and the model is applied to generate tree-ring indices for 1959–1981. The validation exercise yields a highly significant positive correlation between the residual chronology and estimated growth curve (r=0.76 p<0.0001, n=23. The model shows that the average duration of the growing season is 191 days, with considerable variation from year to year. On average, soil moisture limits tree-ring growth for 128 days and temperature for 63 days. Model results depend on chosen values of parameters, in particular a parameter specifying a balance ratio between soil moisture and precipitation. Future work in the Mediterranean region should include multi-year natural experiments to verify patterns of cambial-growth variation suggested by the VS model.
Flow regulation in coronary vascular tree: a model study.
Directory of Open Access Journals (Sweden)
Xinzhou Xie
Full Text Available Coronary blood flow can always be matched to the metabolic demand of the myocardium due to the regulation of vasoactive segments. Myocardial compressive forces play an important role in determining coronary blood flow but its impact on flow regulation is still unknown. The purpose of this study was to develop a coronary specified flow regulation model, which can integrate myocardial compressive forces and other identified regulation factors, to further investigate the coronary blood flow regulation behavior.A theoretical coronary flow regulation model including the myogenic, shear-dependent and metabolic responses was developed. Myocardial compressive forces were included in the modified wall tension model. Shear-dependent response was estimated by using the experimental data from coronary circulation. Capillary density and basal oxygen consumption were specified to corresponding to those in coronary circulation. Zero flow pressure was also modeled by using a simplified capillary model.Pressure-flow relations predicted by the proposed model are consistent with previous experimental data. The predicted diameter changes in small arteries are in good agreement with experiment observations in adenosine infusion and inhibition of NO synthesis conditions. Results demonstrate that the myocardial compressive forces acting on the vessel wall would extend the auto-regulatory range by decreasing the myogenic tone at the given perfusion pressure.Myocardial compressive forces had great impact on coronary auto-regulation effect. The proposed model was proved to be consistent with experiment observations and can be employed to investigate the coronary blood flow regulation effect in physiological and pathophysiological conditions.
An Integrated Approach of Model checking and Temporal Fault Tree for System Safety Analysis
Energy Technology Data Exchange (ETDEWEB)
Koh, Kwang Yong; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)
2009-10-15
Digitalization of instruments and control systems in nuclear power plants offers the potential to improve plant safety and reliability through features such as increased hardware reliability and stability, and improved failure detection capability. It however makes the systems and their safety analysis more complex. Originally, safety analysis was applied to hardware system components and formal methods mainly to software. For software-controlled or digitalized systems, it is necessary to integrate both. Fault tree analysis (FTA) which has been one of the most widely used safety analysis technique in nuclear industry suffers from several drawbacks as described in. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA.
Model checking software for phylogenetic trees using distribution and database methods
Directory of Open Access Journals (Sweden)
Requeno José Ignacio
2013-12-01
Full Text Available Model checking, a generic and formal paradigm stemming from computer science based on temporal logics, has been proposed for the study of biological properties that emerge from the labeling of the states defined over the phylogenetic tree. This strategy allows us to use generic software tools already present in the industry. However, the performance of traditional model checking is penalized when scaling the system for large phylogenies. To this end, two strategies are presented here. The first one consists of partitioning the phylogenetic tree into a set of subgraphs each one representing a subproblem to be verified so as to speed up the computation time and distribute the memory consumption. The second strategy is based on uncoupling the information associated to each state of the phylogenetic tree (mainly, the DNA sequence and exporting it to an external tool for the management of large information systems. The integration of all these approaches outperforms the results of monolithic model checking and helps us to execute the verification of properties in a real phylogenetic tree.
Energy Technology Data Exchange (ETDEWEB)
Chadoeuf, J.; Joannes, H.; Nandris, D.; Pierrat, J.C.
1988-12-01
The spread of root diseases in rubber tree (Hevea brasiliensis) due to Rigidoporus lignosus and Phellinus noxius was investigated epidemiologically using data collected every 6 month during a 6-year survey in a plantation. The aim of the present study is to see what factors could predict whether a given tree would be infested at the following inspection. Using a qualitative regression method we expressed the probability of pathogenic attack on a tree in terms of three factors: the state of health of the surrounding trees, the method used to clear the forest prior to planting, and evolution with time. The effects of each factor were ranked, and the roles of the various classes of neighbors were established and quantified. Variability between successive inspections was small, and the method of forest clearing was important only while primary inocula in the soil were still infectious. The state of health of the immediate neighbors was most significant; more distant neighbors in the same row had some effect; interrow spread was extremely rare. This investigation dealt only with trees as individuals, and further study of the interrelationships of groups of trees is needed.
Wang, Ling; Zhao, Geng-xing; Zhu, Xi-cun; Lei, Tong; Dong, Fang
2010-10-01
Hyperspectral technique has become the basis of quantitative remote sensing. Hyperspectrum of apple tree canopy at prosperous fruit stage consists of the complex information of fruits, leaves, stocks, soil and reflecting films, which was mostly affected by component features of canopy at this stage. First, the hyperspectrum of 18 sample apple trees with reflecting films was compared with that of 44 trees without reflecting films. It could be seen that the impact of reflecting films on reflectance was obvious, so the sample trees with ground reflecting films should be separated to analyze from those without ground films. Secondly, nine indexes of canopy components were built based on classified digital photos of 44 apple trees without ground films. Thirdly, the correlation between the nine indexes and canopy reflectance including some kinds of conversion data was analyzed. The results showed that the correlation between reflectance and the ratio of fruit to leaf was the best, among which the max coefficient reached 0.815, and the correlation between reflectance and the ratio of leaf was a little better than that between reflectance and the density of fruit. Then models of correlation analysis, linear regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the hyperspectral reflectance and the ratio of fruit to leaf with the softwares of DPS and LIBSVM. It was feasible that all of the four models in 611-680 nm characteristic band are feasible to be used to predict, while the model accuracy of BP neural network and support vector regression was better than one-variable linear regression and multi-variable regression, and the accuracy of support vector regression model was the best. This study will be served as a reliable theoretical reference for the yield estimation of apples based on remote sensing data.
UML Statechart Fault Tree Generation By Model Checking
DEFF Research Database (Denmark)
Herbert, Luke Thomas; Herbert-Hansen, Zaza Nadja Lee
of the popular Business Process Modelling and Nota-tion (BPMN) language. To capture uncertainty and unreliability in workflows, we extend this formalism with probabilistic non-deterministic branching.We present an algorithm that allows for exhaustive gen-eration of possible error states that could arise in ex...
Directory of Open Access Journals (Sweden)
Hyun-Joo Oh
2017-09-01
Full Text Available The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN and boosted tree (BT, for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.
Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree
Directory of Open Access Journals (Sweden)
S. Samadianfard
2017-01-01
Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned
On P-adic λ-model on the Cayley tree
International Nuclear Information System (INIS)
Khamaraev, M.; Mukhamedov, F.
2004-04-01
We consider a nearest-neighbour p-adic A-model with spin values ±1 on the Cayley tree of order k ≥ 1. We prove that a p-adic Gibbs measure is unique for p≥ 3. If p 2 then we find a condition which guarantees uniqueness of p-adic Gibbs measure. Besides, the results are applied to the p-adic Ising model. (author)
Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model
International Nuclear Information System (INIS)
Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung
2015-01-01
To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). 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. 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). 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
Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.
Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung
2015-01-01
To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). 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. 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). 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.
Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model
Energy Technology Data Exchange (ETDEWEB)
Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung [Dept. of Radiology, Seoul National University College of Medicine, Seoul (Korea, Republic of)
2015-12-15
To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). 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. 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). 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.
Directory of Open Access Journals (Sweden)
Zheng Zhao
2018-01-01
Full Text Available Urban trees are more about people than trees. Urban trees programs need public support and engagement, from the intentions to support to implement actions in supporting the programs. Built upon the theory of planned behavior and Structural Equation Modeling (SEM, this study uses Beijing as a case study to investigate how subjective norm (cognition of urban trees, attitude (benefits residents’ believe urban trees can provide, and perceived behavioral control (the believed ability of what residents can do affect intention and its transformation into implemented of supporting action. A total of 800 residents were interviewed in 2016 and asked about their opinion of neighborhood trees, park trees, and historical trees, and analyzed, respectively. The results show that subjective norm has a significant positive effect on intentions pertaining to historical and neighborhood trees. Attitudes influence intentions, but its overall influence is much lower than that of the subjective norm, indicating that residents are more likely to be influenced by external factors. The perceived behavioral control has the strongest effect among the three, suggesting the importance of public participation in strengthening intention. The transformation from intention to behavior seems relatively small, especially regarding neighborhood trees, suggesting that perceptions and participation need to be strengthened.
International Nuclear Information System (INIS)
Meloni, S.
1998-01-01
To simulate transmitted radiation in agroforestry systems, radiative transfer models usually require a detailed three-dimensional description of the tree canopy. We propose here a simplification of the description of the three-dimensional structure of wild cherry trees (Prunus avium). The simplified tree description was tested against the detailed one for five-year-old wild cherry. It allowed accurate simulation of transmitted radiation and avoided tedious measurements of tree structure. The simplified description was then applied to older trees. Allometric relationships were used to compute the parameters not available on free-grown trees. The transmitted radiation in an agroforestry system was simulated at four different ages: 5, 10, 15 and 20 years. The trees were planted on a 5 m square grid. Two row orientations, chosen to provide different transmitted radiation patterns, were tested: north/south and north- east/south-west. The simulations showed that the daily mean transmitted radiation was reduced from 92% of incident radiation under five-year-old trees to 37% under 20-year-old trees. The variability of transmitted radiation increased with tree growth. The row orientation had only small effects on the shaded area at the beginning and end of the day when solar elevation was low. (author)
DG TO FT - AUTOMATIC TRANSLATION OF DIGRAPH TO FAULT TREE MODELS
Iverson, D. L.
1994-01-01
Fault tree and digraph models are frequently used for system failure analysis. Both types of models represent a failure space view of the system using AND and OR nodes in a directed graph structure. Each model has its advantages. While digraphs can be derived in a fairly straightforward manner from system schematics and knowledge about component failure modes and system design, fault tree structure allows for fast processing using efficient techniques developed for tree data structures. The similarities between digraphs and fault trees permits the information encoded in the digraph to be translated into a logically equivalent fault tree. The DG TO FT translation tool will automatically translate digraph models, including those with loops or cycles, into fault tree models that have the same minimum cut set solutions as the input digraph. This tool could be useful, for example, if some parts of a system have been modeled using digraphs and others using fault trees. The digraphs could be translated and incorporated into the fault trees, allowing them to be analyzed using a number of powerful fault tree processing codes, such as cut set and quantitative solution codes. A cut set for a given node is a group of failure events that will cause the failure of the node. A minimum cut set for a node is any cut set that, if any of the failures in the set were to be removed, the occurrence of the other failures in the set will not cause the failure of the event represented by the node. Cut sets calculations can be used to find dependencies, weak links, and vital system components whose failures would cause serious systems failure. The DG TO FT translation system reads in a digraph with each node listed as a separate object in the input file. The user specifies a terminal node for the digraph that will be used as the top node of the resulting fault tree. A fault tree basic event node representing the failure of that digraph node is created and becomes a child of the terminal
Surface-based geometric modelling using teaching trees for advanced robots
International Nuclear Information System (INIS)
Nakamura, Akira; Ogasawara, Tsukasa; Tsukune, Hideo; Oshima, Masaki
2000-01-01
Geometric modelling of the environment is important in robot motion planning. Generally, shapes can be stored in a data base, so the elements that need to be decided are positions and orientations. In this paper, surface-based geometric modelling using a teaching tree is proposed. In this modelling, combinations of surfaces are considered in order to decide positions and orientations of objects. The combinations are represented by a depth-first tree, which makes it easy for the operator to select one combination out of several. This method is effective not only in the case when perfect data can be obtained, but also when conditions for measurement of three-dimensional data are unfavorable, which often occur in the environment of a working robot. (author)
Energy Technology Data Exchange (ETDEWEB)
Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)
2011-10-17
Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.
A conceptual approach to approximate tree root architecture in infinite slope models
Schmaltz, Elmar; Glade, Thomas
2016-04-01
Vegetation-related properties - particularly tree root distribution and coherent hydrologic and mechanical effects on the underlying soil mantle - are commonly not considered in infinite slope models. Indeed, from a geotechnical point of view, these effects appear to be difficult to be reproduced reliably in a physically-based modelling approach. The growth of a tree and the expansion of its root architecture are directly connected with both intrinsic properties such as species and age, and extrinsic factors like topography, availability of nutrients, climate and soil type. These parameters control four main issues of the tree root architecture: 1) Type of rooting; 2) maximum growing distance to the tree stem (radius r); 3) maximum growing depth (height h); and 4) potential deformation of the root system. Geometric solids are able to approximate the distribution of a tree root system. The objective of this paper is to investigate whether it is possible to implement root systems and the connected hydrological and mechanical attributes sufficiently in a 3-dimensional slope stability model. Hereby, a spatio-dynamic vegetation module should cope with the demands of performance, computation time and significance. However, in this presentation, we focus only on the distribution of roots. The assumption is that the horizontal root distribution around a tree stem on a 2-dimensional plane can be described by a circle with the stem located at the centroid and a distinct radius r that is dependent on age and species. We classified three main types of tree root systems and reproduced the species-age-related root distribution with three respective mathematical solids in a synthetic 3-dimensional hillslope ambience. Thus, two solids in an Euclidian space were distinguished to represent the three root systems: i) cylinders with radius r and height h, whilst the dimension of latter defines the shape of a taproot-system or a shallow-root-system respectively; ii) elliptic
Heather Sander; Stephen Polasky; Robert. Haight
2010-01-01
Urban tree cover benefits communities. These benefits' economic values, however, are poorly recognized and often ignored by landowners and planners. We use hedonic property price modeling to estimate urban tree cover's value in Dakota and Ramsey Counties, MN, USA, predicting housing value as a function of structural, neighborhood, and environmental variables...
Eric M. Pfeifer; Jeffrey A. Hicke; Arjan J.H. Meddens
2011-01-01
Bark beetle epidemics result in tree mortality across millions of hectares in North America. However, few studies have quantified impacts on carbon (C) cycling. In this study, we quantified the immediate response and subsequent trajectories of stand-level aboveground tree C stocks and fluxes using field measurements and modeling for a location in central Idaho, USA...
International Nuclear Information System (INIS)
Vichev, S.; Bogdanov, D.
2000-01-01
The purpose of this paper is to introduce the fault tree analysis method as a tool for unit protection reliability estimation. The constant failure rate model applies for making reliability assessment, and especially availability assessment. For that purpose an example for unit primary equipment structure and fault tree example for simplified unit protection system is presented (author)
Complementary models of tree species-soil relationships in old-growth temperate forests
Cross, Alison; Perakis, Steven S.
2011-01-01
Ecosystem level studies identify plant soil feed backs as important controls on soil nutrient availability,particularly for nitrogen and phosphorus. Although site and species specific studies of tree species soil relationships are relatively common,comparatively fewer studies consider multiple coexisting speciesin old-growth forests across a range of sites that vary underlying soil fertility. We characterized patterns in forest floor and mineral soil nutrients associated with four common tree species across eight undisturbed old-growth forests in Oregon, USA, and used two complementary conceptual models to assess tree species soil relationships. Plant soil feedbacks that could reinforce sitelevel differences in nutrient availability were assessed using the context dependent relationships model, where by relative species based differences in each soil nutrient divergedorconvergedas nutrient status changed across sites. Tree species soil relationships that did not reflect strong feedbacks were evaluated using a site independent relationships model, where by forest floor and surface mineral soil nutrient tools differed consistently by tree species across sites,without variation in deeper mineral soils. We found that theorganically cycled elements carbon, nitrogen, and phosphorus exhibited context-dependent differences among species in both forest floor and mineral soil, and most of ten followed adivergence model,where by species differences were greatest at high-nutrient sites. These patterns are consistent with the oryemphasizing biotic control of these elements through plant soil feedback mechanisms. Site independent species differences were strongest for pool so if the weather able cations calcium, magnesium, potassium,as well as phosphorus, in mineral soils. Site independent species differences in forest floor nutrients we reattributable too nespecies that displayed significant greater forest floor mass accumulation. Our finding confirmed that site-independent and
Tondjo, Kodjo; Brancheriau, Loïc; Sabatier, Sylvie; Kokutse, Adzo Dzifa; Kokou, Kouami; Jaeger, Marc; de Reffye, Philippe; Fourcaud, Thierry
2018-06-08
For a given genotype, the observed variability of tree forms results from the stochasticity of meristem functioning and from changing and heterogeneous environmental factors affecting biomass formation and allocation. In response to climate change, trees adapt their architecture by adjusting growth processes such as pre- and neoformation, as well as polycyclic growth. This is the case for the teak tree. The aim of this work was to adapt the plant model, GreenLab, in order to take into consideration both these processes using existing data on this tree species. This work adopted GreenLab formalism based on source-sink relationships at organ level that drive biomass production and partitioning within the whole plant over time. The stochastic aspect of phytomer production can be modelled by a Bernoulli process. The teak model was designed, parameterized and analysed using the architectural data from 2- to 5-year-old teak trees in open field stands. Growth and development parameters were identified, fitting the observed compound organic series with the theoretical series, using generalized least squares methods. Phytomer distributions of growth units and branching pattern varied depending on their axis category, i.e. their physiological age. These emerging properties were in accordance with the observed growth patterns and biomass allocation dynamics during a growing season marked by a short dry season. Annual growth patterns observed on teak, including shoot pre- and neoformation and polycyclism, were reproduced by the new version of the GreenLab model. However, further updating is discussed in order to ensure better consideration of radial variation in basic specific gravity of wood. Such upgrading of the model will enable teak ideotypes to be defined for improving wood production in terms of both volume and quality.
On Models with Uncountable Set of Spin Values on a Cayley Tree: Integral Equations
International Nuclear Information System (INIS)
Rozikov, Utkir A.; Eshkobilov, Yusup Kh.
2010-01-01
We consider models with nearest-neighbor interactions and with the set [0, 1] of spin values, on a Cayley tree of order k ≥ 1. We reduce the problem of describing the 'splitting Gibbs measures' of the model to the description of the solutions of some nonlinear integral equation. For k = 1 we show that the integral equation has a unique solution. In case k ≥ 2 some models (with the set [0, 1] of spin values) which have a unique splitting Gibbs measure are constructed. Also for the Potts model with uncountable set of spin values it is proven that there is unique splitting Gibbs measure.
Baños, Hector; Bushek, Nathaniel; Davidson, Ruth; Gross, Elizabeth; Harris, Pamela E.; Krone, Robert; Long, Colby; Stewart, Allen; Walker, Robert
2016-01-01
We introduce the package PhylogeneticTrees for Macaulay2 which allows users to compute phylogenetic invariants for group-based tree models. We provide some background information on phylogenetic algebraic geometry and show how the package PhylogeneticTrees can be used to calculate a generating set for a phylogenetic ideal as well as a lower bound for its dimension. Finally, we show how methods within the package can be used to compute a generating set for the join of any two ideals.
Liu, Tao; Im, Jungho; Quackenbush, Lindi J.
2015-12-01
This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.
Directory of Open Access Journals (Sweden)
Elias .
2011-03-01
Full Text Available The case study was conducted in the area of Acacia mangium plantation at BKPH Parung Panjang, KPH Bogor. The objective of the study was to formulate equation models of tree root carbon mass and root to shoot carbon mass ratio of the plantation. It was found that carbon content in the parts of tree biomass (stems, branches, twigs, leaves, and roots was different, in which the highest and the lowest carbon content was in the main stem of the tree and in the leaves, respectively. The main stem and leaves of tree accounted for 70% of tree biomass. The root-shoot ratio of root biomass to tree biomass above the ground and the root-shoot ratio of root biomass to main stem biomass was 0.1443 and 0.25771, respectively, in which 75% of tree carbon mass was in the main stem and roots of tree. It was also found that the root-shoot ratio of root carbon mass to tree carbon mass above the ground and the root-shoot ratio of root carbon mass to tree main stem carbon mass was 0.1442 and 0.2034, respectively. All allometric equation models of tree root carbon mass of A. mangium have a high goodness-of-fit as indicated by its high adjusted R2.Keywords: Acacia mangium, allometric, root-shoot ratio, biomass, carbon mass
Regional deposition of thoron progeny in models of the human tracheobronchial tree
International Nuclear Information System (INIS)
Smith, S.M.; Cheng, Yung-Sung; Yeh, Hsu-Chi.
1995-01-01
Models of the human tracheobronchial tree have been used to determine total and regional aerosol deposition of inhaled particles. Particle sizes measured in these studies have all been > 40 nm in diameter. The deposition of aerosols < 40 nm in diameter has not been measured. Particles in the ultrafine aerosol size range include some combustion aerosols and indoor radon progeny. Also, the influence of reduced lung size and airflow rates on particle deposition in young children has not been determined. With their smaller lung size and smaller minute volumes, children may be at increased risk from ultrafine pollutants. In order to accurately determine dose of inhaled aerosols, the effects of particle size, minute volume, and age at exposure must be quantified. The purpose of this study was to determine the deposition efficiency of ultrafine aerosols smaller than 40 nm in diameter in models of the human tracheobronchia tree. This study demonstrates that the deposition efficiency of aerosols in the model of the child's tracheobronchial tree may be slightly higher than in the adult models
Regional deposition of thoron progeny in models of the human tracheobronchial tree
Energy Technology Data Exchange (ETDEWEB)
Smith, S.M.; Cheng, Yung-Sung; Yeh, Hsu-Chi
1995-12-01
Models of the human tracheobronchial tree have been used to determine total and regional aerosol deposition of inhaled particles. Particle sizes measured in these studies have all been > 40 nm in diameter. The deposition of aerosols < 40 nm in diameter has not been measured. Particles in the ultrafine aerosol size range include some combustion aerosols and indoor radon progeny. Also, the influence of reduced lung size and airflow rates on particle deposition in young children has not been determined. With their smaller lung size and smaller minute volumes, children may be at increased risk from ultrafine pollutants. In order to accurately determine dose of inhaled aerosols, the effects of particle size, minute volume, and age at exposure must be quantified. The purpose of this study was to determine the deposition efficiency of ultrafine aerosols smaller than 40 nm in diameter in models of the human tracheobronchia tree. This study demonstrates that the deposition efficiency of aerosols in the model of the child`s tracheobronchial tree may be slightly higher than in the adult models.
San Onofre/Zion auxiliary feedwater system seismic fault tree modeling
International Nuclear Information System (INIS)
Najafi, B.; Eide, S.
1982-02-01
As part of the study for the seismic evaluation of the San Onofre Unit 1 Auxiliary Feedwater System (AFWS), a fault tree model was developed capable of handling the effect of structural failure of the plant (in the event of an earthquake) on the availability of the AFWS. A compatible fault tree model was developed for the Zion Unit 1 AFWS in order to compare the results of the two systems. It was concluded that if a single failure of the San Onofre Unit 1 AFWS is to be prevented, some weight existing, locally operated locked open manual valves have to be used for isolation of a rupture in specific parts of the AFWS pipings
Babanatsas, T.; Glăvan, D. O.; Babanatis Merce, R. M.; Maris, S. A.
2018-01-01
The purpose of this study is to bring as much as possible, close to real situation the 3D modelling for the olive trees in order to establish the necessary forces for automatic harvesting (harvesting robots). To fulfil our goal we have at our disposal different ways to do modelling very close to the real situation. One way is to use reality capture software (its results being photos) that are converted into a real 3D model, the disadvantage of the method being a mesh model that is not accurate enough. The reasonable alternative is to develop an experiment by measuring a sample orchard of olive trees (experiment who took place in Halkidiki, Greece, measuring over 120 trees). After establishing the real dimensions, we adopted as model the media that we have measured (the height of the tree, the thickness of branches, number of branches, etc.), model which we consider closer to the reality and therefor more suitable for our simulation.
Can phenological models predict tree phenology accurately under climate change conditions?
Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry
2014-05-01
The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay
Directory of Open Access Journals (Sweden)
T. Sghaier
2013-12-01
Full Text Available Aim of study: The aim of the work was to develop an individual tree diameter-increment model for Thuya (Tetraclinis articulata in Tunisia.Area of study: The natural Tetraclinis articulata stands at Jbel Lattrech in north-eastern of Tunisia.Material and methods: Data came from 200 trees located in 50 sample plots. The diameter at age t and the diameter increment for the last five years obtained from cores taken at breast height were measured for each tree. Four difference equations derived from the base functions of Richards, Lundqvist, Hossfeld IV and Weibull were tested using the age-independent formulations of the growth functions. Both numerical and graphical analyses were used to evaluate the performance of the candidate models.Main results: Based on the analysis, the age-independent difference equation derived from the base function Richards model was selected. Two of the three parameters (growth rate and shape parameter of the retained model were related to site quality, represented by a Growth Index, stand density and the basal area in larger trees divided by diameter of the subject tree expressing the inter-tree competition.Research highlights: The proposed model can be useful for predicting the diameter growth of Tetraclinis articulata in Tunisia when age is not available or for trees growing in uneven-aged stands.Keywords: Age-independent growth model; difference equations; Tetraclinis articulata; Tunisia.
A Model for the Detailed Analysis of Radio Links Involving Tree Canopies
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F. Perez-Fontan
2016-12-01
Full Text Available Detailed analysis of tree canopy interaction with incident radiowaves has mainly been limited to remote sensing for the purpose of forest classification among many other applications. This represents a monostatic configuration, unlike the case of communication links, which are bistatic. In general, link analyses have been limited to the application of simple, empirical formulas based on the use of specific attenuation values in dB/m and the traversed vegetated mass as, e.g., the model in Recommendation ITU-R P.833-8 [1]. In remote sensing, two main techniques are used: Multiple Scattering Theory (MST [2][5] and Radiative Transfer Theory (RT, [5] and [6]. We have paid attention in the past to MST [7][10]. It was shown that a full application of MST leads to very long computation times which are unacceptable in the case where we have to analyze a scenario with several trees. Extensive work using MST has been also presented by others in [11][16] showing the interest in this technique. We have proposed a simplified model for scattering from tree canopies based on a hybridization of MST and a modified physical optics (PO approach [16]. We assume that propagation through a canopy is accounted for by using the complex valued propagation constant obtained by MST. Unlike the case when the full MST is applied, the proposed approach offers significant benefits including a direct software implementation and acceptable computation times even for high frequencies and electrically large canopies. The proposed model thus replaces the coherent component in MST, significant in the forward direction, but keeps the incoherent or diffuse scattering component present in all directions. The incoherent component can be calculated within reasonable times. Here, we present tests of the proposed model against MST using an artificial single-tree scenario at 2 GHz and 10 GHz.
CFD modelling of the aerodynamic effect of trees on urban air pollution dispersion.
Amorim, J H; Rodrigues, V; Tavares, R; Valente, J; Borrego, C
2013-09-01
The current work evaluates the impact of urban trees over the dispersion of carbon monoxide (CO) emitted by road traffic, due to the induced modification of the wind flow characteristics. With this purpose, the standard flow equations with a kε closure for turbulence were extended with the capability to account for the aerodynamic effect of trees over the wind field. Two CFD models were used for testing this numerical approach. Air quality simulations were conducted for two periods of 31h in selected areas of Lisbon and Aveiro, in Portugal, for distinct relative wind directions: approximately 45° and nearly parallel to the main avenue, respectively. The statistical evaluation of modelling performance and uncertainty revealed a significant improvement of results with trees, as shown by the reduction of the NMSE from 0.14 to 0.10 in Lisbon, and from 0.14 to 0.04 in Aveiro, which is independent from the CFD model applied. The consideration of the plant canopy allowed to fulfil the data quality objectives for ambient air quality modelling established by the Directive 2008/50/EC, with an important decrease of the maximum deviation between site measurements and CFD results. In the non-aligned wind situation an average 12% increase of the CO concentrations in the domain was observed as a response to the aerodynamic action of trees over the vertical exchange rates of polluted air with the above roof-level atmosphere; while for the aligned configuration an average 16% decrease was registered due to the enhanced ventilation of the street canyon. These results show that urban air quality can be optimised based on knowledge-based planning of green spaces. Copyright © 2013 Elsevier B.V. All rights reserved.
Analysis of a Model for the Morphological Structure of Renal Arterial Tree: Fractal Structure
Directory of Open Access Journals (Sweden)
Aurora Espinoza-Valdez
2013-01-01
experimental data measurements of the rat kidneys. The fractal dimension depends on the probability of sprouting angiogenesis in the development of the arterial vascular tree of the kidney, that is, of the distribution of blood vessels in the morphology generated by the analytical model. The fractal dimension might determine whether a suitable renal vascular structure is capable of performing physiological functions under appropriate conditions. The analysis can describe the complex structures of the development vasculature in kidney.
Molecule formation and the Farey tree in the one-dimensional Falicov-Kimball model
International Nuclear Information System (INIS)
Gruber, C.; Ueltschi, D.; Jedrzejewski, J.
1994-01-01
The ground-state configurations of the one-dimensional Falicov-Kimball model are studied exactly with numerical calculations revealing unexpected effects for small interaction strength. In neutral systems we observe molecular formation, phase separation, and changes in the conducting properties; while in nonneutral systems the phase diagram exhibits Farey tree order (Aubry sequence) and a devil's staircase structure. Conjectures are presented for the boundary of the segregated domain and the general structure of the ground states
Yang, Ziheng; Zhu, Tianqi
2018-02-20
The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.
Directory of Open Access Journals (Sweden)
R. K. Tiwari
2011-08-01
Full Text Available A novel technique based on the Bayesian neural network (BNN theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226–2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then cross-validation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.
Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.
He, Jichao; Wanik, David W; Hartman, Brian M; Anagnostou, Emmanouil N; Astitha, Marina; Frediani, Maria E B
2017-03-01
This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources. © 2016 Society for Risk Analysis.
Correspondence between spanning trees and the Ising model on a square lattice
Viswanathan, G. M.
2017-06-01
An important problem in statistical physics concerns the fascinating connections between partition functions of lattice models studied in equilibrium statistical mechanics on the one hand and graph theoretical enumeration problems on the other hand. We investigate the nature of the relationship between the number of spanning trees and the partition function of the Ising model on the square lattice. The spanning tree generating function T (z ) gives the spanning tree constant when evaluated at z =1 , while giving the lattice green function when differentiated. It is known that for the infinite square lattice the partition function Z (K ) of the Ising model evaluated at the critical temperature K =Kc is related to T (1 ) . Here we show that this idea in fact generalizes to all real temperatures. We prove that [Z(K ) s e c h 2 K ] 2=k exp[T (k )] , where k =2 tanh(2 K )s e c h (2 K ) . The identical Mahler measure connects the two seemingly disparate quantities T (z ) and Z (K ) . In turn, the Mahler measure is determined by the random walk structure function. Finally, we show that the the above correspondence does not generalize in a straightforward manner to nonplanar lattices.
Perceived Organizational Support for Enhancing Welfare at Work: A Regression Tree Model
Giorgi, Gabriele; Dubin, David; Perez, Javier Fiz
2016-01-01
When trying to examine outcomes such as welfare and well-being, research tends to focus on main effects and take into account limited numbers of variables at a time. There are a number of techniques that may help address this problem. For example, many statistical packages available in R provide easy-to-use methods of modeling complicated analysis such as classification and tree regression (i.e., recursive partitioning). The present research illustrates the value of recursive partitioning in the prediction of perceived organizational support in a sample of more than 6000 Italian bankers. Utilizing the tree function party package in R, we estimated a regression tree model predicting perceived organizational support from a multitude of job characteristics including job demand, lack of job control, lack of supervisor support, training, etc. The resulting model appears particularly helpful in pointing out several interactions in the prediction of perceived organizational support. In particular, training is the dominant factor. Another dimension that seems to influence organizational support is reporting (perceived communication about safety and stress concerns). Results are discussed from a theoretical and methodological point of view. PMID:28082924
An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models
International Nuclear Information System (INIS)
Shin, Hoyoung; Jae, Moosung
2016-01-01
In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology
An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models
Energy Technology Data Exchange (ETDEWEB)
Shin, Hoyoung; Jae, Moosung [Hanyang University, Seoul (Korea, Republic of)
2016-10-15
In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology.
Fault-Tree Modeling of Safety-Critical Network Communication in a Digitalized Nuclear Power Plant
Energy Technology Data Exchange (ETDEWEB)
Lee, Sang Hun; Kang, Hyun Gook [KAIST, Daejeon (Korea, Republic of)
2015-10-15
To achieve technical self-reliance for nuclear I and C systems in Korea, the Advanced Power Reactor 1400 (APR-1400) man-machine interface system (MMIS) architecture was developed by the Korea Atomic Energy Research Institute (KAERI). As one of the systems in the developed MMIS architecture, the Engineered Safety Feature-Component Control System (ESF-CCS) employs a network communication system for the transmission of safety-critical information from group controllers (GCs) to loop controllers (LCs) to effectively accommodate the vast number of field controllers. The developed fault-tree model was then applied to several case studies. As an example of the development of a fault-tree model for ESF-CCS signal failure, the fault-tree model of ESF-CCS signal failure for CS pump PP01A in the CSAS condition was designed by considering the identified hazardous states of network failure that would result in a failure to provide input signals to the corresponding LC. The quantitative results for four case studies demonstrated that the probability of overall network communication failure, which was calculated as the sum of the failure probability associated with each failure cause, contributes up to 1.88% of the probability of ESF-CCS signal failure for the CS pump considered in the case studies.
Directory of Open Access Journals (Sweden)
Dan A. MacIsaac
2011-09-01
Full Text Available We investigated the relationship of stem diameter to tree, site and stand characteristics for six major tree species (trembling aspen, white birch, balsam fir, lodgepole pine, black spruce, and white spruce in Alberta (Canada with data from Alberta Sustainable Resource Development Permanent Sample Plots. Using non-linear mixed effects modeling techniques, we developed models to estimate diameter at breast height using height, crown and stand attributes. Mixed effects models (with plot as subject using height, crown area, and basal area of the larger trees explained on average 95% of the variation in diameter at breast height across the six species with a root mean square error of 2.0 cm (13.4% of mean diameter. Fixed effects models (without plot as subject including the Natural Sub-Region (NSR information explained on average 90% of the variation in diameter at breast height across the six species with a root mean square error equal to 2.8 cm (17.9% of mean diameter. Selected climate variables provided similar results to models with NSR information. The inclusion of nutrient regime and moisture regime did not significantly improve the predictive ability of these models.
International Nuclear Information System (INIS)
Wu, Qiong-Li; Cournède, Paul-Henry; Mathieu, Amélie
2012-01-01
Global sensitivity analysis has a key role to play in the design and parameterisation of functional–structural plant growth models which combine the description of plant structural development (organogenesis and geometry) and functional growth (biomass accumulation and allocation). We are particularly interested in this study in Sobol's method which decomposes the variance of the output of interest into terms due to individual parameters but also to interactions between parameters. Such information is crucial for systems with potentially high levels of non-linearity and interactions between processes, like plant growth. However, the computation of Sobol's indices relies on Monte Carlo sampling and re-sampling, whose costs can be very high, especially when model evaluation is also expensive, as for tree models. In this paper, we thus propose a new method to compute Sobol's indices inspired by Homma–Saltelli, which improves slightly their use of model evaluations, and then derive for this generic type of computational methods an estimator of the error estimation of sensitivity indices with respect to the sampling size. It allows the detailed control of the balance between accuracy and computing time. Numerical tests on a simple non-linear model are convincing and the method is finally applied to a functional–structural model of tree growth, GreenLab, whose particularity is the strong level of interaction between plant functioning and organogenesis. - Highlights: ► We study global sensitivity analysis in the context of functional–structural plant modelling. ► A new estimator based on Homma–Saltelli method is proposed to compute Sobol indices, based on a more balanced re-sampling strategy. ► The estimation accuracy of sensitivity indices for a class of Sobol's estimators can be controlled by error analysis. ► The proposed algorithm is implemented efficiently to compute Sobol indices for a complex tree growth model.
Directory of Open Access Journals (Sweden)
Roozbeh Hasanzadeh Nafari
2016-07-01
Full Text Available Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment.
Energy Technology Data Exchange (ETDEWEB)
Dongiovanni, Danilo Nicola, E-mail: danilo.dongiovanni@enea.it [ENEA, Nuclear Fusion and Safety Technologies Department, via Enrico Fermi 45, Frascati 00040 (Italy); Iesmantas, Tomas [LEI, Breslaujos str. 3 Kaunas (Lithuania)
2016-11-01
Highlights: • RAMI (Reliability, Availability, Maintainability and Inspectability) assessment of secondary heat transfer loop for a DEMO nuclear fusion plant. • Definition of a fault tree for a nuclear steam turbine operated in pulsed mode. • Turbine failure rate models update by mean of a Bayesian network reflecting the fault tree analysis in the considered scenario. • Sensitivity analysis on system availability performance. - Abstract: Availability will play an important role in the Demonstration Power Plant (DEMO) success from an economic and safety perspective. Availability performance is commonly assessed by Reliability Availability Maintainability Inspectability (RAMI) analysis, strongly relying on the accurate definition of system components failure modes (FM) and failure rates (FR). Little component experience is available in fusion application, therefore requiring the adaptation of literature FR to fusion plant operating conditions, which may differ in several aspects. As a possible solution to this problem, a new methodology to extrapolate/estimate components failure rate under different operating conditions is presented. The DEMO Balance of Plant nuclear steam turbine component operated in pulse mode is considered as study case. The methodology moves from the definition of a fault tree taking into account failure modes possibly enhanced by pulsed operation. The fault tree is then translated into a Bayesian network. A statistical model for the turbine system failure rate in terms of subcomponents’ FR is hence obtained, allowing for sensitivity analyses on the structured mixture of literature and unknown FR data for which plausible value intervals are investigated to assess their impact on the whole turbine system FR. Finally, the impact of resulting turbine system FR on plant availability is assessed exploiting a Reliability Block Diagram (RBD) model for a typical secondary cooling system implementing a Rankine cycle. Mean inherent availability
International Nuclear Information System (INIS)
Dongiovanni, Danilo Nicola; Iesmantas, Tomas
2016-01-01
Highlights: • RAMI (Reliability, Availability, Maintainability and Inspectability) assessment of secondary heat transfer loop for a DEMO nuclear fusion plant. • Definition of a fault tree for a nuclear steam turbine operated in pulsed mode. • Turbine failure rate models update by mean of a Bayesian network reflecting the fault tree analysis in the considered scenario. • Sensitivity analysis on system availability performance. - Abstract: Availability will play an important role in the Demonstration Power Plant (DEMO) success from an economic and safety perspective. Availability performance is commonly assessed by Reliability Availability Maintainability Inspectability (RAMI) analysis, strongly relying on the accurate definition of system components failure modes (FM) and failure rates (FR). Little component experience is available in fusion application, therefore requiring the adaptation of literature FR to fusion plant operating conditions, which may differ in several aspects. As a possible solution to this problem, a new methodology to extrapolate/estimate components failure rate under different operating conditions is presented. The DEMO Balance of Plant nuclear steam turbine component operated in pulse mode is considered as study case. The methodology moves from the definition of a fault tree taking into account failure modes possibly enhanced by pulsed operation. The fault tree is then translated into a Bayesian network. A statistical model for the turbine system failure rate in terms of subcomponents’ FR is hence obtained, allowing for sensitivity analyses on the structured mixture of literature and unknown FR data for which plausible value intervals are investigated to assess their impact on the whole turbine system FR. Finally, the impact of resulting turbine system FR on plant availability is assessed exploiting a Reliability Block Diagram (RBD) model for a typical secondary cooling system implementing a Rankine cycle. Mean inherent availability
Detecting Difference between Process Models Based on the Refined Process Structure Tree
Directory of Open Access Journals (Sweden)
Jing Fan
2017-01-01
Full Text Available The development of mobile workflow management systems (mWfMS leads to large number of business process models. In the meantime, the location restriction embedded in mWfMS may result in different process models for a single business process. In order to help users quickly locate the difference and rebuild the process model, detecting the difference between different process models is needed. Existing detection methods either provide a dissimilarity value to represent the difference or use predefined difference template to generate the result, which cannot reflect the entire composition of the difference. Hence, in this paper, we present a new approach to solve this problem. Firstly, we parse the process models to their corresponding refined process structure trees (PSTs, that is, decomposing a process model into a hierarchy of subprocess models. Then we design a method to convert the PST to its corresponding task based process structure tree (TPST. As a consequence, the problem of detecting difference between two process models is transformed to detect difference between their corresponding TPSTs. Finally, we obtain the difference between two TPSTs based on the divide and conquer strategy, where the difference is described by an edit script and we make the cost of the edit script close to minimum. The extensive experimental evaluation shows that our method can meet the real requirements in terms of precision and efficiency.
A Survey on Hidden Markov Model (HMM) Based Intention Prediction Techniques
Mrs. Manisha Bharati; Dr. Santosh Lomte
2016-01-01
The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. This paper discusses two approaches In the first three features namely ongoing attacks, autonomic prevention actions, and risk measure are Integrated to our Autonomic Cloud Intrus...
Modeled PM2.5 removal by trees in ten U.S. cities and associated health effects
International Nuclear Information System (INIS)
Nowak, David J.; Hirabayashi, Satoshi; Bodine, Allison; Hoehn, Robert
2013-01-01
Urban particulate air pollution is a serious health issue. Trees within cities can remove fine particles from the atmosphere and consequently improve air quality and human health. Tree effects on PM 2.5 concentrations and human health are modeled for 10 U.S. cities. The total amount of PM 2.5 removed annually by trees varied from 4.7 tonnes in Syracuse to 64.5 tonnes in Atlanta, with annual values varying from $1.1 million in Syracuse to $60.1 million in New York City. Most of these values were from the effects of reducing human mortality. Mortality reductions were typically around 1 person yr −1 per city, but were as high as 7.6 people yr −1 in New York City. Average annual percent air quality improvement ranged between 0.05% in San Francisco and 0.24% in Atlanta. Understanding the impact of urban trees on air quality can lead to improved urban forest management strategies to sustain human health in cities. -- Highlights: •Paper provides the first broad-scale estimates of city-wide tree impacts on PM 2.5 . •Trees improve overall air quality by intercepting particulate matter. •Particle resuspension can lead to short-term increases in pollutant concentrations. •Urban trees produce substantial health improvements and values. -- Air pollution modeling reveals broad-scale impacts of pollution removal by urban trees on PM 2.5 concentrations and human health
Osmanoglu, B.; Feliciano, E. A.; Armstrong, A. H.; Sun, G.; Montesano, P.; Ranson, K.
2017-12-01
Tree heights are one of the most commonly used remote sensing parameters to measure biomass of a forest. In this project, we investigate the relationship between remotely sensed tree heights (e.g. G-LiHT lidar and commercially available high resolution satellite imagery, HRSI) and the SIBBORK modeled tree heights. G-LiHT is a portable, airborne imaging system that simultaneously maps the composition, structure, and function of terrestrial ecosystems using lidar, imaging spectroscopy and thermal mapping. Ground elevation and canopy height models were generated using the lidar data acquired in 2012. A digital surface model was also generated using the HRSI technique from the commercially available WorldView data in 2016. The HRSI derived height and biomass products are available at the plot (10x10m) level. For this study, we parameterized the SIBBORK individual-based gap model for Howland forest, Maine. The parameterization was calibrated using field data for the study site and results show that the simulated forest reproduces the structural complexity of Howland old growth forest, based on comparisons of key variables including, aboveground biomass, forest height and basal area. Furthermore carbon cycle and ecosystem observational capabilities will be enhanced over the next 6 years via the launch of two LiDAR (NASA's GEDI and ICESAT 2) and two SAR (NASA's ISRO NiSAR and ESA's Biomass) systems. Our aim is to present the comparison of canopy height models obtained with SIBBORK forest model and remote sensing techniques, highlighting the synergy between individual-based forest modeling and high-resolution remote sensing.
Reconstruction of 3D tree stem models from low-cost terrestrial laser scanner data
Kelbe, Dave; Romanczyk, Paul; van Aardt, Jan; Cawse-Nicholson, Kerry
2013-05-01
With the development of increasingly advanced airborne sensing systems, there is a growing need to support sensor system design, modeling, and product-algorithm development with explicit 3D structural ground truth commensurate to the scale of acquisition. Terrestrial laser scanning is one such technique which could provide this structural information. Commercial instrumentation to suit this purpose has existed for some time now, but cost can be a prohibitive barrier for some applications. As such we recently developed a unique laser scanning system from readily-available components, supporting low cost, highly portable, and rapid measurement of below-canopy 3D forest structure. Tools were developed to automatically reconstruct tree stem models as an initial step towards virtual forest scene generation. The objective of this paper is to assess the potential of this hardware/algorithm suite to reconstruct 3D stem information for a single scan of a New England hardwood forest site. Detailed tree stem structure (e.g., taper, sweep, and lean) is recovered for trees of varying diameter, species, and range from the sensor. Absolute stem diameter retrieval accuracy is 12.5%, with a 4.5% overestimation bias likely due to the LiDAR beam divergence.
Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones
International Nuclear Information System (INIS)
Rosli, A D; Baharudin, R; Hashim, H; Khairuzzaman, N A; Mohd Sampian, A F; Abdullah, N E; Kamaru'zzaman, M; Sulaiman, M S
2015-01-01
This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water. (paper)
Post Fire Safe Shutdown Analysis Using a Fault Tree Logic Model
International Nuclear Information System (INIS)
Yim, Hyun Tae; Park, Jun Hyun
2005-01-01
Every nuclear power plant should have its own fire hazard analysis including the fire safe shutdown analysis. A safe shutdown (SSD) analysis is performed to demonstrate the capability of the plant to safely shut down for a fire in any given area. The basic assumption is that there will be fire damage to all cables and equipment located within a common fire area. When evaluating the SSD capabilities of the plant, based on a review of the systems, equipment and cables within each fire area, it should be determined which shutdown paths are either unaffected or least impacted by a postulated fire within the fire area. Instead of seeking a success path for safe shutdown given all cables and equipment damaged by a fire, there can be an alternative approach to determine the SSD capability: fault tree analysis. This paper introduces the methodology for fire SSD analysis using a fault tree logic model
A Bayesian Approach for Structural Learning with Hidden Markov Models
Directory of Open Access Journals (Sweden)
Cen Li
2002-01-01
Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.
Pan, Xing-Hua; Zhu, Lu; Yao, Xiang; Liu, Ju-Fen; Li, Zi-An; Yang, Jian-Yong; Pang, Rong-Qing; Ruan, Guang-Ping
2016-12-01
The aim of this study was to establish a tree shrew metabolic syndrome model and demonstrate the utility of MSCs in treating metabolic syndrome. We used tree shrew umbilical cord mesenchymal stem cell (TS-UC-MSC) transplantation for the treatment of metabolic syndrome to demonstrate the clinical application of these stem cells and to provide a theoretical basis and reference methods for this treatment. Tree shrew metabolic syndrome model showed significant insulin resistance, high blood sugar, lipid metabolism disorders, and hypertension, consistent with the diagnostic criteria. TS-UC-MSC transplantation at 16 weeks significantly reduced blood sugar and lipid levels, improved insulin resistance and the regulation of insulin secretion, and reduced the expression levels of the pro-inflammatory cytokines IL-1 and IL-6 (P metabolic syndrome model and showed that MSC migrate in diseased organs and can attenuate metabolic syndrome severity in a tree shrew model.
Segment-based acoustic models for continuous speech recognition
Ostendorf, Mari; Rohlicek, J. R.
1993-07-01
This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.
de Blas, J.; Criado, J. C.; Pérez-Victoria, M.; Santiago, J.
2018-03-01
We compute all the tree-level contributions to the Wilson coefficients of the dimension-six Standard-Model effective theory in ultraviolet completions with general scalar, spinor and vector field content and arbitrary interactions. No assumption about the renormalizability of the high-energy theory is made. This provides a complete ultraviolet/infrared dictionary at the classical level, which can be used to study the low-energy implications of any model of interest, and also to look for explicit completions consistent with low-energy data.
A New Method of Chinese Address Extraction Based on Address Tree Model
Directory of Open Access Journals (Sweden)
KANG Mengjun
2015-01-01
Full Text Available Address is a spatial location encoding method of individual geographical area. In China, address planning is relatively backward due to the rapid development of the city, resulting in the presence of large number of non-standard address. The space constrain relationship of standard address model is analyzed in this paper and a new method of standard address extraction based on the tree model is proposed, which regards topological relationship as consistent criteria of space constraints. With this method, standard address can be extracted and errors can be excluded from non-standard address. Results indicate that higher math rate can be obtained with this method.
Calculating Nuclear Power Plant Vulnerability Using Integrated Geometry and Event/Fault-Tree Models
International Nuclear Information System (INIS)
Peplow, Douglas E.; Sulfredge, C. David; Sanders, Robert L.; Morris, Robert H.; Hann, Todd A.
2004-01-01
Since the events of September 11, 2001, the vulnerability of nuclear power plants to terrorist attacks has become a national concern. The results of vulnerability analysis are greatly influenced by the computational approaches used. Standard approximations used in fault-tree analysis are not applicable for attacks, where high component failure probabilities are expected; two methods that do work with high failure probabilities are presented. Different blast modeling approaches can also affect the end results. Modeling the structural details of facility buildings and the geometric layout of components within the buildings is required to yield meaningful results
Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.
Chaubert-Pereira, Florence; Guédon, Yann; Lavergne, Christian; Trottier, Catherine
2010-09-01
Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent-in the corresponding growth phase-both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation-maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates. © 2009, The International Biometric Society.
Directory of Open Access Journals (Sweden)
Petras Rupšys
2015-01-01
Full Text Available A stochastic modeling approach based on the Bertalanffy law gained interest due to its ability to produce more accurate results than the deterministic approaches. We examine tree crown width dynamic with the Bertalanffy type stochastic differential equation (SDE and mixed-effects parameters. In this study, we demonstrate how this simple model can be used to calculate predictions of crown width. We propose a parameter estimation method and computational guidelines. The primary goal of the study was to estimate the parameters by considering discrete sampling of the diameter at breast height and crown width and by using maximum likelihood procedure. Performance statistics for the crown width equation include statistical indexes and analysis of residuals. We use data provided by the Lithuanian National Forest Inventory from Scots pine trees to illustrate issues of our modeling technique. Comparison of the predicted crown width values of mixed-effects parameters model with those obtained using fixed-effects parameters model demonstrates the predictive power of the stochastic differential equations model with mixed-effects parameters. All results were implemented in a symbolic algebra system MAPLE.
Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar
Chen, Qi
2015-08-01
Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors.
Forest, Loïc; Demongeot, Jacques; Demongeota, Jacques
2006-05-01
The radial growth of conifer trees proceeds from the dynamics of a merismatic tissue called vascular cambium or cambium. Cambium is a thin layer of active proliferating cells. The purpose of this paper was to model the main characteristics of cambial activity and its consecutive radial growth. Cell growth is under the control of the auxin hormone indole-3-acetic. The model is composed of a discrete part, which accounts for cellular proliferation, and a continuous part involving the transport of auxin. Cambium is modeled in a two-dimensional cross-section by a cellular automaton that describes the set of all its constitutive cells. Proliferation is defined as growth and division of cambial cells under neighbouring constraints, which can eliminate some cells from the cambium. The cell-growth rate is determined from auxin concentration, calculated with the continuous model. We studied the integration of each elementary cambial cell activity into the global coherent movement of macroscopic morphogenesis. Cases of normal and abnormal growth of Pinus radiata (D. Don) are modelled. Abnormal growth includes deformed trees where gravity influences auxin transport, producing heterogeneous radial growth. Cross-sectional microscopic views are also provided to validate the model's hypothesis and results.
Trimming a hazard logic tree with a new model-order-reduction technique
Porter, Keith; Field, Edward; Milner, Kevin R
2017-01-01
The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.
Van Looy, Kris; Piffady, Jérémy
2017-11-01
Floodplain landscapes are highly fragmented by river regulation resulting in habitat degradation and flood regime perturbation, posing risks to population persistence. Climate change is expected to pose supplementary risks in this context of fragmented landscapes, and especially for river systems adaptation management programs are developed. The association of habitat quality and quantity with the landscape dynamics and resilience to human-induced disturbances is still poorly understood in the context of species survival and colonization processes, but essential to prioritize conservation and restoration actions. We present a modelling approach that elucidates network connectivity and landscape dynamics in spatial and temporal context to identify vital corridors and conservation priorities in the Loire river and its tributaries. Alteration of flooding and flow regimes is believed to be critical to population dynamics in river ecosystems. Still, little is known of critical levels of alteration both spatially and temporally. We applied metapopulation modelling approaches for a dispersal-limited tree species, white elm; and a recruitment-limited tree species, black poplar. In different model steps the connectivity and natural dynamics of the river landscape are confronted with physical alterations (dams/dykes) to species survival and then future scenarios for climatic changes and potential adaptation measures are entered in the model and translated in population persistence over the river basin. For the two tree species we highlighted crucial network zones in relation to habitat quality and connectivity. Where the human impact model already shows currently restricted metapopulation development, climate change is projected to aggravate this persistence perspective substantially. For both species a significant drawback to the basin population is observed, with 1/3 for elm and ¼ for poplar after 25 years already. But proposed adaptation measures prove effective to even
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. © 2015 Society for Conservation Biology.
An effective fractal-tree closure model for simulating blood flow in large arterial networks.
Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em
2015-06-01
The aim of the present work is to address the closure problem for hemodynamic simulations by developing a flexible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure outflow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii [Formula: see text]). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to outflow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for outflow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 min. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels
Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling
Lau, Alvaro; Bentley, Lisa Patrick; Martius, Christopher; Shenkin, Alexander; Bartholomeus, Harm; Raumonen, Pasi; Malhi, Yadvinder; Jackson, Tobias; Herold, Martin
2018-01-01
Tree architecture is the three-dimensional arrangement of above ground parts of a tree. Ecologists hypothesize that the topology of tree branches represents optimized adaptations to tree’s environment. Thus, an accurate description of tree architecture leads to a better understanding of how form is
Zhou, Jianlong; Takatsuka, Masahiro
2009-01-01
Transfer functions facilitate the volumetric data visualization by assigning optical properties to various data features and scalar values. Automation of transfer function specifications still remains a challenge in volume rendering. This paper presents an approach for automating transfer function generations by utilizing topological attributes derived from the contour tree of a volume. The contour tree acts as a visual index to volume segments, and captures associated topological attributes involved in volumetric data. A residue flow model based on Darcy's Law is employed to control distributions of opacity between branches of the contour tree. Topological attributes are also used to control color selection in a perceptual color space and create harmonic color transfer functions. The generated transfer functions can depict inclusion relationship between structures and maximize opacity and color differences between them. The proposed approach allows efficient automation of transfer function generations, and exploration on the data to be carried out based on controlling of opacity residue flow rate instead of complex low-level transfer function parameter adjustments. Experiments on various data sets demonstrate the practical use of our approach in transfer function generations.
Numerical modeling of flow and pollutant dispersion in street canyons with tree planting
Energy Technology Data Exchange (ETDEWEB)
Balczo, Marton [Budapest Univ. of Technology and Economics (Hungary). Theodore von Karman Wind Tunnel Lab.; Gromke, Christof; Ruck, Bodo [Karlsruhe Univ. (Germany). Lab. of Building- and Environmental Aerodynamics
2009-04-15
Numerical simulations of the impact of tree planting on airflow and traffic pollutant dispersion in urban street canyons have been performed using the commercial CFD (Computational Fluid Dynamics) code MISKAM. A {kappa}-{epsilon} turbulence model including additional terms for the treatment of vegetation, has been employed to close the Reynolds-averaged-Navier-Stokes (RANS) equations. The numerical results were compared to wind tunnel data. In the case of the investigated wind direction perpendicular to the street axis, the presence of trees lead to increased pollutant concentrations inside the canyon. Concentrations increased strongly on the upstream side of the canyon, while on the downstream side a small concentration decrease could be observed. Lower flow velocities and higher pollutant concentrations were found in the numerical simulations when directly compared to the experimental results. However, the impact of tree planting on airflow and concentration fields when compared to the treeless street canyon as a reference configuration were simulated quite well, meaning that relative changes were similar in the wind tunnel investigations and numerical computations. This feature qualifies MISKAM for use as a tool for assessing the impacts of vegetation on local air quality. (orig.)
van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A
2018-02-01
This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.
Individual-Tree Diameter Growth Models for Mixed Nothofagus Second Growth Forests in Southern Chile
Directory of Open Access Journals (Sweden)
Paulo C. Moreno
2017-12-01
Full Text Available Second growth forests of Nothofagus obliqua (roble, N. alpina (raulí, and N. dombeyi (coihue, known locally as RORACO, are among the most important native mixed forests in Chile. To improve the sustainable management of these forests, managers need adequate information and models regarding not only existing forest conditions, but their future states with varying alternative silvicultural activities. In this study, an individual-tree diameter growth model was developed for the full geographical distribution of the RORACO forest type. This was achieved by fitting a complete model by comparing two variable selection procedures: cross-validation (CV, and least absolute shrinkage and selection operator (LASSO regression. A small set of predictors successfully explained a large portion of the annual increment in diameter at breast height (DBH growth, particularly variables associated with competition at both the tree- and stand-level. Goodness-of-fit statistics for this final model showed an empirical coefficient of correlation (R2emp of 0.56, relative root mean square error of 44.49% and relative bias of −1.96% for annual DBH growth predictions, and R2emp of 0.98 and 0.97 for DBH projection at 6 and 12 years, respectively. This model constitutes a simple and useful tool to support management plans for these forest ecosystems.
Directory of Open Access Journals (Sweden)
M. Saki
2013-03-01
Full Text Available The relationship between plant species and environmental factors has always been a central issue in plant ecology. With rising power of statistical techniques, geo-statistics and geographic information systems (GIS, the development of predictive habitat distribution models of organisms has rapidly increased in ecology. This study aimed to evaluate the ability of Logistic Regression Tree model to create potential habitat map of Astragalus verus. This species produces Tragacanth and has economic value. A stratified- random sampling was applied to 100 sites (50 presence- 50 absence of given species, and produced environmental and edaphic factors maps by using Kriging and Inverse Distance Weighting methods in the ArcGIS software for the whole study area. Relationships between species occurrence and environmental factors were determined by Logistic Regression Tree model and extended to the whole study area. The results indicated species occurrence has strong correlation with environmental factors such as mean daily temperature and clay, EC and organic carbon content of the soil. Species occurrence showed direct relationship with mean daily temperature and clay and organic carbon, and inverse relationship with EC. Model accuracy was evaluated both by Cohen’s kappa statistics (κ and by area under Receiver Operating Characteristics curve based on independent test data set. Their values (kappa=0.9, Auc of ROC=0.96 indicated the high power of LRT to create potential habitat map on local scales. This model, therefore, can be applied to recognize potential sites for rangeland reclamation projects.
The risk factors of laryngeal pathology in Korean adults using a decision tree model.
Byeon, Haewon
2015-01-01
The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Teolis, D.S.; Zarewczynski, S.A.; Detar, H.L.
2012-01-01
The reactor trip system (RTS) and engineered safety features actuation system (ESFAS) in nuclear power plants utilizes instrumentation and control (IC) to provide automatic protection against unsafe and improper reactor operation during steady-state and transient power operations. During normal operating conditions, various plant parameters are continuously monitored to assure that the plant is operating in a safe state. In response to deviations of these parameters from pre-determined set points, the protection system will initiate actions required to maintain the reactor in a safe state. These actions may include shutting down the reactor by opening the reactor trip breakers and actuation of safety equipment based on the situation. The RTS and ESFAS are represented in probabilistic risk assessments (PRAs) to reflect the impact of their contribution to core damage frequency (CDF). The reactor protection systems (RPS) in existing nuclear power plants are generally analog based and there is general consensus within the PRA community on fault tree modeling of these systems. In new plants, such as AP1000 plant, the RPS is based on digital technology. Digital systems are more complex combinations of hardware components and software. This combination of complex hardware and software can result in the presence of faults and failure modes unique to a digital RPS. The United States Nuclear Regulatory Commission (NRC) is currently performing research on the development of probabilistic models for digital systems for inclusion in PRAs; however, no consensus methodology exists at this time. Westinghouse is currently updating the AP1000 plant PRA to support initial operation of plants currently under construction in the United States. The digital RPS is modeled using fault tree methodology similar to that used for analog based systems. This paper presents high level descriptions of a typical analog based RPS and of the AP1000 plant digital RPS. Application of current fault
Shao, Q; Rowe, R C; York, P
2007-06-01
This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.
A systematic fault tree analysis based on multi-level flow modeling
International Nuclear Information System (INIS)
Gofuku, Akio; Ohara, Ai
2010-01-01
The fault tree analysis (FTA) is widely applied for the safety evaluation of a large-scale and mission-critical system. Because the potential of the FTA, however, strongly depends on human skill of analyzers, problems are pointed out in (1) education and training, (2) unreliable quality, (3) necessity of expertise knowledge, and (4) update of FTA results after the reconstruction of a target system. To get rid of these problems, many techniques to systematize FTA activities by applying computer technologies have been proposed. However, these techniques only use structural information of a target system and do not use functional information that is one of important properties of an artifact. The principle of FTA is to trace comprehensively cause-effect relations from a top undesirable effect to anomaly causes. The tracing is similar to the causality estimation technique that the authors proposed to find plausible counter actions to prevent or to mitigate the undesirable behavior of plants based on the model by a functional modeling technique, Multilevel Flow Modeling (MFM). The authors have extended this systematic technique to construct a fault tree (FT). This paper presents an algorithm of systematic construction of FT based on MFM models and demonstrates the applicability of the extended technique by the FT construction result of a cooling plant of nitric acid. (author)
The Development of a Fault Tree Model for Balance of Plant System
International Nuclear Information System (INIS)
Hwang, Mee Jeong; Park, Jin Hee; Lim, Ho Gon
2011-01-01
In this paper, we propose a fault tree modeling method for BOP (balance of plant) system to develop a combined risk model and trip model, and the application plans of the developed model. Where, the trip means the reactor trip and turbine and generator trip. We have usually modeled the safety-related systems and their supporting systems to assess the risk analysis of a nuclear power plant. However, the BOP system.s condition change induces the risk change. That is, the BOP system.s condition is relevant to plants. performance and affects to the plant risk. The existing model for BOP systems is a simplified system model or SPV (Single-point vulnerability) evaluation model. However, these models are not effective enough to use for the plant's performance evaluation. Also, lately an integrated decision-making framework is required for risk-informed applications. The methods for monitoring the performance of a nuclear power plant differ from the purpose. For example, MSPI (mitigating system performance index) and MR (maintenance rule) use different methods and indexes to monitor the performance. Therefore, for consistent decision-making, it is necessary to develop a risk assessment model including a systems model inducing reactor trip. The system.s model inducing reactor trip and turbine/generator trip is defined as the 'trip model'
T. Sghaier; M. Tome; J. Tome; M. Sanchez-Gonzalez; I. Cañellas; R. Calama
2013-01-01
Aim of study: The aim of the work was to develop an individual tree diameter-increment model for Thuya (Tetraclinis articulata) in Tunisia.Area of study: The natural Tetraclinis articulata stands at Jbel Lattrech in north-eastern of Tunisia.Material and methods: Data came from 200 trees located in 50 sample plots. The diameter at age t and the diameter increment for the last five years obtained from cores taken at breast height were measured for each tree. Four difference equations derived f...
Berdanier, Aaron B; Miniat, Chelcy F; Clark, James S
2016-08-01
Accurately scaling sap flux observations to tree or stand levels requires accounting for variation in sap flux between wood types and by depth into the tree. However, existing models for radial variation in axial sap flux are rarely used because they are difficult to implement, there is uncertainty about their predictive ability and calibration measurements are often unavailable. Here we compare different models with a diverse sap flux data set to test the hypotheses that radial profiles differ by wood type and tree size. We show that radial variation in sap flux is dependent on wood type but independent of tree size for a range of temperate trees. The best-fitting model predicted out-of-sample sap flux observations and independent estimates of sapwood area with small errors, suggesting robustness in the new settings. We develop a method for predicting whole-tree water use with this model and include computer code for simple implementation in other studies. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.
DEFF Research Database (Denmark)
Goussanou, Cédric A.; Guendehou, Sabin; Assogbadjo, Achille E.
2016-01-01
The quantification of the contribution of tropical forests to global carbon stocks and climate change mitigation requires availability of data and tools such as allometric equations. This study made available volume and biomass models for eighteen tree species in a semi-deciduous tropical forest...... in West Africa. Generic models were also developed for the forest ecosystem, and basic wood density determined for the tree species. Non-destructive sampling approach was carried out on five hundred and one sample trees to analyse stem volume and biomass. From the modelling of volume and biomass...... enabled to conclude that the non-destructive sampling was a good approach to determining reliable basic wood density. The comparative analysis of species-specific models in this study with selected generic models for tropical forests indicated low probability to identify effective generic models with good...
Kang, G.; Kim, J.
2017-12-01
This study investigated the tree's effect on wind comfort at pedestrian height in an urban area using a computational fluid dynamics (CFD) model. We implemented the tree's drag parameterization scheme to the CFD model and validated the simulated results against the wind-tunnel measurement data as well as LES data via several statistical methods. The CFD model underestimated (overestimated) the concentrations on the leeward (windward) walls inside the street canyon in the presence of trees, because the CFD model can't resolve the latticed cage and can't reflect the concentration increase and decrease caused by the latticed cage in the simulations. However, the scalar pollutants' dispersion simulated by the CFD model was quite similar to that in the wind-tunnel measurement in pattern and magnitude, on the whole. The CFD model overall satisfied the statistical validation indices (root normalized mean square error, geometric mean variance, correlation coefficient, and FAC2) but failed to satisfy the fractional bias and geometric mean bias due to the underestimation on the leeward wall and overestimation on the windward wall, showing that its performance was comparable to the LES's performance. We applied the CFD model to evaluation of the trees' effect on the pedestrian's wind-comfort in an urban area. To investigate sensory levels for human activities, the wind-comfort criteria based on Beaufort wind-force scales (BWSs) were used. In the tree-free scenario, BWS 4 and 5 (unpleasant condition for sitting long and sitting short, respectively) appeared in the narrow spaces between buildings, in the upwind side of buildings, and the unobstructed areas. In the tree scenario, BWSs decreased by 1 3 grade inside the campus of Pukyong National University located in the target area, which indicated that trees planted in the campus effectively improved pedestrian's wind comfort.
Genetic Algorithms Principles Towards Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-10-01
Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.
Sinha, T.; Gangodagamage, C.; Ale, S.; Frazier, A. G.; Giambelluca, T. W.; Kumagai, T.; Nakai, T.; Sato, H.
2017-12-01
Drought-related tree mortality at a regional scale causes drastic shifts in carbon and water cycling in Southeast Asian tropical rainforests, where severe droughts are projected to occur more frequently, especially under El Niño conditions. To provide a useful tool for projecting the tropical rainforest dynamics under climate change conditions, we developed the Spatially Explicit Individual-Based (SEIB) Dynamic Global Vegetation Model (DGVM) applicable to simulating mechanistic tree mortality induced by the climatic impacts via individual-tree-scale ecophysiology such as hydraulic failure and carbon starvation. In this study, we present the new model, SEIB-originated Terrestrial Ecosystem Dynamics (S-TEDy) model, and the computation results were compared with observations collected at a field site in a Bornean tropical rainforest. Furthermore, after validating the model's performance, numerical experiments addressing a future of the tropical rainforest were conducted using some global climate model (GCM) simulation outputs.
Development of a parametric containment event tree model for a severe BWR accident
Energy Technology Data Exchange (ETDEWEB)
Okkonen, T [OTO-Consulting Ay, Helsinki (Finland)
1995-04-01
A containment event tree (CET) is built for analysis of severe accidents at the TVO boiling water reactor (BWR) units. Parametric models of severe accident progression and fission product behaviour are developed and integrated in order to construct a compact and self-contained Level 2 PSA model. The model can be easily updated to correspond to new research results. The analyses of the study are limited to severe accidents starting from full-power operation and leading to core melting, and are focused mainly on the use and effects of the dedicated severe accident management (SAM) systems. Severe accident progression from eight plant damage states (PDS), involving different pre-core-damage accident evolution, is examined, but the inclusion of their relative or absolute probabilities, by integration with Level 1, is deferred to integral safety assessments. (33 refs., 5 figs., 7 tabs.).
Requeno, José Ignacio; Colom, José Manuel
2014-12-01
Model checking is a generic verification technique that allows the phylogeneticist to focus on models and specifications instead of on implementation issues. Phylogenetic trees are considered as transition systems over which we interrogate phylogenetic questions written as formulas of temporal logic. Nonetheless, standard logics become insufficient for certain practices of phylogenetic analysis since they do not allow the inclusion of explicit time and probabilities. The aim of this paper is to extend the application of model checking techniques beyond qualitative phylogenetic properties and adapt the existing logical extensions and tools to the field of phylogeny. The introduction of time and probabilities in phylogenetic specifications is motivated by the study of a real example: the analysis of the ratio of lactose intolerance in some populations and the date of appearance of this phenotype.
Martin, Y. E.; Johnson, E. A.; Gallaway, J.; Chaikina, O.
2011-12-01
Herein we conduct a followup investigation to an earlier research project in which we developed a numerical model of tree population dynamics, tree throw, and sediment transport associated with the formation of pit-mound features for Hawk Creek watershed, Canadian Rockies (Gallaway et al., 2009). We extend this earlier work by exploring the most appropriate transport relations to simulate the diffusion over time of newly-formed pit-pound features due to tree throw. We combine our earlier model with a landscape development model that can incorporate these diffusive transport relations. Using these combined models, changes in hillslope microtopography over time associated with the formation of pit-mound features and their decay will be investigated. The following ideas have motivated this particular study: (i) Rates of pit-mound degradation remain a source of almost complete speculation, as there is almost no long-term information on process rates. Therefore, we will attempt to tackle the issue of pit-mound degradation in a methodical way that can guide future field studies; (ii) The degree of visible pit-mound topography at any point in time on the landscape is a joint function of the rate of formation of new pit-mound features due to tree death/topple and their magnitude vs. the rate of decay of pit-mound features. An example of one interesting observation that arises is the following: it appears that pit-mound topography is often more pronounced in some eastern North American forests vs. field sites along the eastern slopes of the Canadian Rockies. Why is this the case? Our investigation begins by considering whether pit-mound decay might occur by linear or nonlinear diffusion. What differences might arise depending on which diffusive approach is adopted? What is the magnitude of transport rates associated with these possible forms of transport relations? We explore linear and nonlinear diffusion at varying rates and for different sizes of pit-mound pairs using a
Brims, Fraser J H; Meniawy, Tarek M; Duffus, Ian; de Fonseka, Duneesha; Segal, Amanda; Creaney, Jenette; Maskell, Nicholas; Lake, Richard A; de Klerk, Nick; Nowak, Anna K
2016-04-01
Malignant pleural mesothelioma (MPM) is a rare cancer with a heterogeneous prognosis. Prognostic models are not widely utilized clinically. Classification and regression tree (CART) analysis examines the interaction of multiple variables with a given outcome. Between 2005 and 2014, all cases with pathologically confirmed MPM had routinely available histological, clinical, and laboratory characteristics recorded. Classification and regression tree analysis was performed using 29 variables with 18-month survival as the dependent variable. Risk groups were refined according to survival and clinical characteristics. The model was then tested on an external international cohort. A total of 482 cases were included in the derivation cohort; the median survival was 12.6 months, and the median age was 69 years. The model defined four risk groups with clear survival differences (p loss. The group with the best survival at 18 months (86.7% alive, median survival 34.0 months, termed risk group 1) had no weight loss, a hemoglobin level greater than 153 g/L, and a serum albumin level greater than 43 g/L. The group with the worst survival (0% alive, median survival 7.5 months, termed risk group 4d) had weight loss, a performance score of 0 or 1, and sarcomatoid histological characteristics. The C-statistic for the model was 0.761, and the sensitivity was 94.5%. Validation on 174 external cases confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic 0.68). We have developed and validated a simple, clinically relevant model to reliably discriminate patients at high and lower risk of death using routinely available variables from the time of diagnosis in unselected populations of patients with MPM. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.
Toward the modeling of mucus draining from the human lung: role of the geometry of the airway tree
International Nuclear Information System (INIS)
Mauroy, Benjamin; Merckx, Jacques; Flaud, Patrice; Fausser, Christian; Pelca, Dominique
2011-01-01
Mucociliary clearance and cough are the two main natural mucus draining methods in the bronchial tree. If they are affected by a pathology, they can become insufficient or even ineffective, then therapeutic draining of mucus plays a critical role to keep mucus levels in the lungs acceptable. The manipulations of physical therapists are known to be very efficient clinically but they are mostly empirical since the biophysical mechanisms involved in these manipulations have never been studied. We develop in this work a model of mucus clearance in idealized rigid human bronchial trees and focus our study on the interaction between (1) tree geometry, (2) mucus physical properties and (3) amplitude of flow rate in the tree. The mucus is considered as a Bingham fluid (gel-like) which is moved upward in the tree thanks to its viscous interaction with air flow. Our studies point out the important roles played both by the geometry and by the physical properties of mucus (yield stress and viscosity). More particularly, the yield stress has to be overcome to make mucus flow. Air flow rate and yield stress determine the maximal possible mucus thickness in each branch of the tree at equilibrium. This forms a specific distribution of mucus in the tree whose characteristics are strongly related to the multi-scaled structure of the tree. The behavior of any mucus distribution is then dependent on this distribution. Finally, our results indicate that increasing air flow rates ought to be more efficient to drain mucus out of the bronchial tree while minimizing patient discomfort
Energy Technology Data Exchange (ETDEWEB)
Kim, Nam Yeong; Kim, Jin Tae; Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of); Jerng, Dong Wook [Chung-Ang University, Seoul (Korea, Republic of)
2016-05-15
The purpose of ASP (Accident Sequence Precursor) analysis is to evaluate operational accidents in full power and low power operation by using PRA (Probabilistic Risk Assessment) technologies. The awareness of the importance of ASP analysis has been on rise. The methodology for ASP analysis has been developed in Korea, KINS (Korea Institute of Nuclear Safety) has managed KINS-ASP program since it was developed. In this study, we applied ASP analysis into operational accidents in full power and low power operation to quantify CCDP (Conditional Core Damage Probability). To reflect these 2 cases into PRA model, we modified fault trees and event trees of the existing PRA model. Also, we suggest the ASP regulatory system in the conclusion. In this study, we reviewed previous studies for ASP analysis. Based on it, we applied it into operational accidents in full power and low power operation. CCDP of these 2 cases are 1.195E-06 and 2.261E-03. Unlike other countries, there is no regulatory basis of ASP analysis in Korea. ASP analysis could detect the risk by assessing the existing operational accidents. ASP analysis can improve the safety of nuclear power plant by detecting, reviewing the operational accidents, and finally removing potential risk. Operator have to notify regulatory institute of operational accident before operator takes recovery work for the accident. After follow-up accident, they have to check precursors in data base to find similar accident.
The standard lateral gene transfer model is statistically consistent for pectinate four-taxon trees
DEFF Research Database (Denmark)
Sand, Andreas; Steel, Mike
2013-01-01
Evolutionary events such as incomplete lineage sorting and lateral gene transfers constitute major problems for inferring species trees from gene trees, as they can sometimes lead to gene trees which conflict with the underlying species tree. One particularly simple and efficient way to infer...... species trees from gene trees under such conditions is to combine three-taxon analyses for several genes using a majority vote approach. For incomplete lineage sorting this method is known to be statistically consistent; however, for lateral gene transfers it was recently shown that a zone...... of inconsistency exists for a specific four-taxon tree topology, and it was posed as an open question whether inconsistencies could exist for other four-taxon tree topologies? In this letter we analyze all remaining four-taxon topologies and show that no other inconsistencies exist....
Martin, Y. E.; Johnson, E. A.; Chaikina, O.
2013-10-01
During the cycle of forest disturbance, regeneration, and maturity, tree mortality leading to topple is a regular occurrence. When tree topple occurs relatively soon after mortality and if the tree has attained some threshold diameter at breast height (dbh) at the time of death, then notable amounts of soil may be upheaved along with the root wad. This upheaval may result in sediment transfers and soil production. A combination of field evidence and numerical modeling is used herein to gain insights regarding the temporal dynamics of tree topple, associated root throw processes, and pit-mound microtopography. Results from our model of tree population dynamics demonstrate temporal patterns in root throw processes in subalpine forests of the Canadian Rockies, a region in which forests are affected largely by wildfire disturbance. As the forest regenerates after disturbance, the new cohort of trees has to reach a critical dbh before significant root plate upheaval can occur; in the subalpine forests of the Canadian Rockies, this may take up to ~ 102 years. Once trees begin to reach this critical dbh for root plate upheaval, a period of sporadic root throw arises that is caused by mortality of trees during competition. In due course, another wildfire will occur on the landscape and a period of much increased root throw activity then takes place for the next several decades; tree sizes and, therefore, the amount of sediment disturbance will be greater the longer the time period since the previous fire. Results of previous root throw studies covering a number of regional settings are used to guide an exercise in diffusion modeling with the aim of defining a range of reasonable diffusion coefficients for pit-mound degradation; the most appropriate values to fit the field data ranged from 0.01 m2 y- 1 to 0.1 m2 y- 1. A similar exercise is then undertaken that is guided by our field observations in subalpine forests of the Canadian Rockies. For these forests, the most
Fan, Yang; Qi, Yang; Bing, Gao; Rong, Xia; Yanjie, Le; Iroegbu, Paul Ikechukwu
2018-03-01
Water tree is the predominant defect in high-voltage crosslinked polyethylene cables. The microscopic mechanism in the discharge process is not fully understood; hence, a drawback is created towards an effective method to evaluate the insulation status. In order to investigate the growth of water tree, a plasma-chemical model is developed. The dynamic characteristics of the discharge process including voltage waveform, current waveform, electron density, electric potential, and electric field intensity are analyzed. Our results show that the distorted electric field is the predominant contributing factor of electron avalanche formation, which inevitably leads to the formation of pulse current. In addition, it is found that characteristic parameters such as the pulse width and pulse number have a great relevance to the length of water tree. Accordingly, the growth of water tree can be divided into the initial stage, development stage, and pre-breakdown stage, which provides a reference for evaluating the deteriorated stages of crosslinked polyethylene cables.
Individual tree crown modeling and change detection from airborne lidar data
Xiao, W.; Xu, Sudan; Oude Elberink, S.J.; Vosselman, G.
2016-01-01
Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne
Nóbrega, Cristina; Pereira, Fernando L.; Valente, Fernanda
2015-04-01
Water losses associated to the rainfall interception process by trees can be an important component of the local hydrologic balance and must be accounted for when implementing any sustainable water management programme. In many dry areas of the Mediterranean region where agro-forestry systems are common, those programmes are crucial to foster adequate water conservation measures. Recent studies have shown that the evaluation of interception loss in sparse forests or tree plantations should be made for individual trees, being the total value determined as the sum of the individual contributions. Following this approach, rainfall interception was measured and modelled over two years, in an isolated Olea europeaea L. tree, in a traditional low-density olive grove in Castelo Branco, central Portugal. Total interception loss over the experimental period was 243.5 mm, on a tree crown projected area basis, corresponding to 18.0% of gross rainfall (Pg). Modelling made for each rainfall event using the sparse version of the Gash model, slightly underestimated interception loss with a value of 240.5 mm, i.e., 17.8 % ofPg. Modelling quality, evaluated according to a number of criteria, was good, allowing the conclusion that the methodology used was adequate. Modelling was also made on a daily basis, i.e., assuming a single storm per rainday. In this case, interception loss was overestimated by 12%, mostly because 72% of all rainfall events lasted for more than a day.
Directory of Open Access Journals (Sweden)
Weizeng Ni
2014-01-01
Full Text Available Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.
Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad
2014-10-01
Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.
Taggart, T. P.; Endreny, T. A.; Nowak, D.
2014-12-01
Gray and green infrastructure in urban environments alters many natural hydrologic processes, creating an urban water balance unique to the developed environment. A common way to assess the consequences of impervious cover and grey infrastructure is by measuring runoff hydrographs. This focus on the watershed outlet masks the spatial variation of hydrologic process alterations across the urban environment in response to localized landscape characteristics. We attempt to represent this spatial variation in the urban environment using the statistically and spatially distributed i-Tree Hydro model, a scoping level urban forest effects water balance model. i-Tree Hydro has undergone expansion and modification to include the effect of green infrastructure processes, road network attributes, and urban pipe system leakages. These additions to the model are intended to increase the understanding of the altered urban hydrologic cycle by examining the effects of the location of these structures on the water balance. Specifically, the effect of these additional structures and functions on the spatially varying properties of interception, soil moisture and runoff generation. Differences in predicted properties and optimized parameter sets between the two models are examined and related to the recent landscape modifications. Datasets used in this study consist of watersheds and sewersheds within the Syracuse, NY metropolitan area, an urban area that has integrated green and gray infrastructure practices to alleviate stormwater problems.
OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
Magana-Mora, Arturo
2017-06-14
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.
OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models
Magana-Mora, Arturo; Bajic, Vladimir B.
2017-01-01
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.
Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu
2018-06-15
Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.
Candidate gene database and transcript map for peach, a model species for fruit trees.
Horn, Renate; Lecouls, Anne-Claire; Callahan, Ann; Dandekar, Abhaya; Garay, Lilibeth; McCord, Per; Howad, Werner; Chan, Helen; Verde, Ignazio; Main, Doreen; Jung, Sook; Georgi, Laura; Forrest, Sam; Mook, Jennifer; Zhebentyayeva, Tatyana; Yu, Yeisoo; Kim, Hye Ran; Jesudurai, Christopher; Sosinski, Bryon; Arús, Pere; Baird, Vance; Parfitt, Dan; Reighard, Gregory; Scorza, Ralph; Tomkins, Jeffrey; Wing, Rod; Abbott, Albert Glenn
2005-05-01
Peach (Prunus persica) is a model species for the Rosaceae, which includes a number of economically important fruit tree species. To develop an extensive Prunus expressed sequence tag (EST) database for identifying and cloning the genes important to fruit and tree development, we generated 9,984 high-quality ESTs from a peach cDNA library of developing fruit mesocarp. After assembly and annotation, a putative peach unigene set consisting of 3,842 ESTs was defined. Gene ontology (GO) classification was assigned based on the annotation of the single "best hit" match against the Swiss-Prot database. No significant homology could be found in the GenBank nr databases for 24.3% of the sequences. Using core markers from the general Prunus genetic map, we anchored bacterial artificial chromosome (BAC) clones on the genetic map, thereby providing a framework for the construction of a physical and transcript map. A transcript map was developed by hybridizing 1,236 ESTs from the putative peach unigene set and an additional 68 peach cDNA clones against the peach BAC library. Hybridizing ESTs to genetically anchored BACs immediately localized 11.2% of the ESTs on the genetic map. ESTs showed a clustering of expressed genes in defined regions of the linkage groups. [The data were built into a regularly updated Genome Database for Rosaceae (GDR), available at (http://www.genome.clemson.edu/gdr/).].
Directory of Open Access Journals (Sweden)
Zhixin Liu
2018-05-01
Full Text Available Urban trees can significantly improve the outdoor thermal environment, especially in subtropical zones. However, due to the lack of fundamental evaluations of numerical simulation models, design and modification strategies for optimizing the thermal environment in subtropical hot-humid climate zones cannot be proposed accurately. To resolve this issue, this study investigated the physiological parameters (leaf surface temperature and vapor flux and thermal effects (solar radiation, air temperature, and humidity of four common tree species (Michelia alba, Mangifera indica, Ficus microcarpa, and Bauhinia blakeana in both spring and summer in Guangzhou, China. A comprehensive comparison of the observed and modeled data from ENVI-met (v4.2 Science, a three-dimensional microclimate model was performed. The results show that the most fundamental weakness of ENVI-met is the limitation of input solar radiation, which cannot be input hourly in the current version and may impact the thermal environment in simulation. For the tree model, the discrepancy between modeled and observed microclimate parameters was acceptable. However, for the physiological parameters, ENVI-met tended to overestimate the leaf surface temperature and underestimate the vapor flux, especially at midday in summer. The simplified calculation of the tree model may be one of the main reasons. Furthermore, the thermal effect of trees, meaning the differences between nearby treeless sites and shaded areas, were all underestimated in ENVI-met for each microclimate variable. This study shows that the tree model is suitable in subtropical hot-humid climates, but also needs some improvement.
Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan
2006-01-01
We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...
Directory of Open Access Journals (Sweden)
Matthew Brolly
2014-07-01
Full Text Available This study describes the novel use of a macroecological plant and forest structure model in conjunction with a Radiative Transfer (RT model to better understand interactions between microwaves and forest canopies. Trends predicted by the RT model, resulting from interactions with mixed age, mono and multi species forests, are analysed in comparison to those predicted using a simplistic structure based scattering model. This model relates backscatter to scatterer cross sectional or volume specifications, dependent on the size. The Spatially Explicit Reiterative Algorithm (SERA model is used to provide a widely varied tree size distribution while maintaining allometric consistency to produce a natural-like forest representation. The RT model is parameterised using structural information from SERA and microwave backscatter simulations are used to analyse the impact of changes to the forest stand. Results show that the slope of the saturation curve observed in the Synthetic Aperture Radar (SAR backscatter-biomass relationship is sensitive to thinning and therefore forest basal area. Due to similarities displayed between the results of the RT and simplistic model, it is determined that forest SAR backscatter behaviour at long microwave wavelengths may be described generally using equations related to total stem volume and basal area. The nature of these equations is such that they describe saturating behaviour of forests in the absence of attenuation in comparable fashion to the trends exhibited using the RT model. Both modelled backscatter trends predict a relationship to forest basal area from an early age when forest volume is increasing. When this is not the case, it is assumed to be a result of attenuation of the dominant stem-ground interaction due to the presence of excessive numbers of stems. This work shows how forest growth models can be successfully incorporated into existing independent scattering models and reveals, through the RT
Maillard, Philippe; Gomes, Marília F.
2016-06-01
This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the "template matching" image processing approach, in which the template is based on a "geometricaloptical" model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications. The algorithm is tested on four images from different regions of the world and different crop types. These images all have the GoogleEarth application. Results show that the algorithm is very efficient at detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required parameters is under development.
Directory of Open Access Journals (Sweden)
Fang eShen
2014-09-01
Full Text Available Tree shrews represent a suitable animal model to study the pathogenesis of human diseases as they are phylogenetically close to primates and have a well-developed central nervous system that possesses many homologies with primates. Therefore, in our study, we investigated whether tree shrews can be used to explore the addictive behaviors induced by morphine. Firstly, to investigate the psychoactive effect of morphine on tree shrews’ behavior, the number of jumping and shuttling, which represent the vertical and horizontal locomotor activity respectively, was examined following the injection of different dosage of morphine. Our results showed intramuscular (IM injection of morphine (5 or 10 mg/kg significantly increased the locomotor activity of tree shrews 30-60 min post-injection. Then, using the conditioned place preference/aversion (CPP/CPA paradigm, we found morphine-conditioned tree shrews exhibited place preference in the morphine-paired chamber on the test day. In addition, naloxone-precipitated withdrawal induced place aversion in the chronic morphine-dependent tree shrews. We evaluated the craving for morphine drinking by assessing the break point that reflects the maximum effort animals will expend to get the drug. Our data showed the break point was significantly increased when compared to the baseline on the 1st, 7th and 14th day after the abstinence. Moreover, in the intravenous morphine self-administration experiment, tree shrews conditioned with morphine responded on the active lever significantly more frequently than on the inactive lever after training. These results suggest that tree shrew may be a potential candidate for study the addictive behaviors and the underling neurological mechanisms.
Restructuring of an Event Tree for a Loss of Coolant Accident in a PSA model
International Nuclear Information System (INIS)
Lim, Ho-Gon; Han, Sang-Hoon; Park, Jin-Hee; Jang, Seong-Chul
2015-01-01
Conventional risk model using PSA (probabilistic Safety Assessment) for a NPP considers two types of accident initiators for internal events, LOCA (Loss of Coolant Accident) and transient event such as Loss of electric power, Loss of cooling, and so on. Traditionally, a LOCA is divided into three initiating event (IE) categories depending on the break size, small, medium, and large LOCA. In each IE group, safety functions or systems modeled in the accident sequences are considered to be applicable regardless of the break size. However, since the safety system or functions are not designed based on a break size, there exist lots of mismatch between safety system/function and an IE, which may make the risk model conservative or in some case optimistic. Present paper proposes new methodology for accident sequence analysis for LOCA. We suggest an integrated single ET construction for LOCA by incorporating a safety system/function and its applicable break spectrum into the ET. Integrated accident sequence analysis in terms of ET for LOCA was proposed in the present paper. Safety function/system can be properly assigned if its applicable range is given by break set point. Also, using simple Boolean algebra with the subset of the break spectrum, final accident sequences are expressed properly in terms of the Boolean multiplication, the occurrence frequency and the success/failure of safety system. The accident sequence results show that the accident sequence is described more detailed compared with the conventional results. Unfortunately, the quantitative results in terms of MCS (minimal Cut-Set) was not given because system fault tree was not constructed for this analysis and the break set points for all 7 point were not given as a specified numerical quantity. Further study may be needed to fix the break set point and to develop system fault tree
Restructuring of an Event Tree for a Loss of Coolant Accident in a PSA model
Energy Technology Data Exchange (ETDEWEB)
Lim, Ho-Gon; Han, Sang-Hoon; Park, Jin-Hee; Jang, Seong-Chul [KAERI, Daejeon (Korea, Republic of)
2015-05-15
Conventional risk model using PSA (probabilistic Safety Assessment) for a NPP considers two types of accident initiators for internal events, LOCA (Loss of Coolant Accident) and transient event such as Loss of electric power, Loss of cooling, and so on. Traditionally, a LOCA is divided into three initiating event (IE) categories depending on the break size, small, medium, and large LOCA. In each IE group, safety functions or systems modeled in the accident sequences are considered to be applicable regardless of the break size. However, since the safety system or functions are not designed based on a break size, there exist lots of mismatch between safety system/function and an IE, which may make the risk model conservative or in some case optimistic. Present paper proposes new methodology for accident sequence analysis for LOCA. We suggest an integrated single ET construction for LOCA by incorporating a safety system/function and its applicable break spectrum into the ET. Integrated accident sequence analysis in terms of ET for LOCA was proposed in the present paper. Safety function/system can be properly assigned if its applicable range is given by break set point. Also, using simple Boolean algebra with the subset of the break spectrum, final accident sequences are expressed properly in terms of the Boolean multiplication, the occurrence frequency and the success/failure of safety system. The accident sequence results show that the accident sequence is described more detailed compared with the conventional results. Unfortunately, the quantitative results in terms of MCS (minimal Cut-Set) was not given because system fault tree was not constructed for this analysis and the break set points for all 7 point were not given as a specified numerical quantity. Further study may be needed to fix the break set point and to develop system fault tree.
Modelling and subject-specific validation of the heart-arterial tree system.
Guala, Andrea; Camporeale, Carlo; Tosello, Francesco; Canuto, Claudio; Ridolfi, Luca
2015-01-01
A modeling approach integrated with a novel subject-specific characterization is here proposed for the assessment of hemodynamic values of the arterial tree. A 1D model is adopted to characterize large-to-medium arteries, while the left ventricle, aortic valve and distal micro-circulation sectors are described by lumped submodels. A new velocity profile and a new formulation of the non-linear viscoelastic constitutive relation suitable for the {Q, A} modeling are also proposed. The model is firstly verified semi-quantitatively against literature data. A simple but effective procedure for obtaining subject-specific model characterization from non-invasive measurements is then designed. A detailed subject-specific validation against in vivo measurements from a population of six healthy young men is also performed. Several key quantities of heart dynamics-mean ejected flow, ejection fraction, and left-ventricular end-diastolic, end-systolic and stroke volumes-and the pressure waveforms (at the central, radial, brachial, femoral, and posterior tibial sites) are compared with measured data. Mean errors around 5 and 8%, obtained for the heart and arterial quantities, respectively, testify the effectiveness of the model and its subject-specific characterization.
The data-driven null models for information dissemination tree in social networks
Zhang, Zhiwei; Wang, Zhenyu
2017-10-01
For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.
Comparisons of patch-use models for wintering American tree sparrows
Tome, M.W.
1990-01-01
Optimal foraging theory has stimulated numerous theoretical and empirical studies of foraging behavior for >20 years. These models provide a valuable tool for studying the foraging behavior of an organism. As with any other tool, the models are most effective when properly used. For example, to obtain a robust test of a foraging model, Stephens and Krebs (1986) recommend experimental designs in which four questions are answered in the affirmative. First, do the foragers play the same "game" as the model? Sec- ond, are the assumptions of the model met? Third, does the test rule out alternative possibilities? Finally, are the appropriate variables measured? Negative an- swers to any of these questions could invalidate the model and lead to confusion over the usefulness of foraging theory in conducting ecological studies. Gaines (1989) attempted to determine whether American Tree Sparrows (Spizella arborea) foraged by a time (Krebs 1973) or number expectation rule (Gibb 1962), or in a manner consistent with the predictions of Charnov's (1976) marginal value theorem (MVT). Gaines (1989: 118) noted appropriately that field tests of foraging models frequently involve uncontrollable circumstances; thus, it is often difficult to meet the assumptions of the models. Gaines also states (1989: 118) that "violations of the assumptions are also in- formative but do not constitute robust tests of predicted hypotheses," and that "the problem can be avoided by experimental analyses which concurrently test mutually exclusive hypotheses so that alter- native predictions will be eliminated if falsified." There is a problem with this approach because, when major assumptions of models are not satisfied, it is not justifiable to compare a predator's foraging behavior with the model's predictions. I submit that failing to follow the advice offered by Stephens and Krebs (1986) can invalidate tests of foraging models.
Ruczyński, Ireneusz; Bartoń, Kamil A
2012-01-01
Sensory limitation plays an important role in the evolution of animal behaviour. Animals have to find objects of interest (e.g. food, shelters, predators). When sensory abilities are strongly limited, animals adjust their behaviour to maximize chances for success. Bats are nocturnal, live in complex environments, are capable of flight and must confront numerous perceptual challenges (e.g. limited sensory range, interfering clutter echoes). This makes them an excellent model for studying the role of compensating behaviours to decrease costs of finding resources. Cavity roosting bats are especially interesting because the availability of tree cavities is often limited, and their quality is vital for bats during the breeding season. From a bat's sensory point of view, cavities are difficult to detect and finding them requires time and energy. However, tree cavities are also long lasting, allowing information transfer among conspecifics. Here, we use a simple simulation model to explore the benefits of tree selection, memory and eavesdropping (compensation behaviours) to searches for tree cavities by bats with short and long perception range. Our model suggests that memory and correct discrimination of tree suitability are the basic strategies decreasing the cost of roost finding, whereas perceptual range plays a minor role in this process. Additionally, eavesdropping constitutes a buffer that reduces the costs of finding new resources (such as roosts), especially when they occur in low density. We conclude that natural selection may promote different strategies of roost finding in relation to habitat conditions and cognitive skills of animals.
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Ireneusz Ruczyński
Full Text Available Sensory limitation plays an important role in the evolution of animal behaviour. Animals have to find objects of interest (e.g. food, shelters, predators. When sensory abilities are strongly limited, animals adjust their behaviour to maximize chances for success. Bats are nocturnal, live in complex environments, are capable of flight and must confront numerous perceptual challenges (e.g. limited sensory range, interfering clutter echoes. This makes them an excellent model for studying the role of compensating behaviours to decrease costs of finding resources. Cavity roosting bats are especially interesting because the availability of tree cavities is often limited, and their quality is vital for bats during the breeding season. From a bat's sensory point of view, cavities are difficult to detect and finding them requires time and energy. However, tree cavities are also long lasting, allowing information transfer among conspecifics. Here, we use a simple simulation model to explore the benefits of tree selection, memory and eavesdropping (compensation behaviours to searches for tree cavities by bats with short and long perception range. Our model suggests that memory and correct discrimination of tree suitability are the basic strategies decreasing the cost of roost finding, whereas perceptual range plays a minor role in this process. Additionally, eavesdropping constitutes a buffer that reduces the costs of finding new resources (such as roosts, especially when they occur in low density. We conclude that natural selection may promote different strategies of roost finding in relation to habitat conditions and cognitive skills of animals.
Pricing Options and Equity-Indexed Annuities in a Regime-switching Model by Trinomial Tree Method
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Fei Lung Yuen
2011-12-01
Full Text Available In this paper we summarize the main idea and results of Yuen and Yang (2009, 2010a, 2010b and provide some results on pricing of Parisian options under the Markov regime-switching model (MRSM. The MRSM allows the parameters of the market model depending on a Markovian process, and the model can reflect the information of the market environment which cannot be modeled solely by linear Gaussian process. However, when the parameters of the stock price model are not constant but governed by a Markovian process, the pricing of the options becomes complex. We present a fast and simple trinomial tree model to price options in MRSM. In recent years, the pricing of modern insurance products, such as Equity-Indexed annuity (EIA and variable annuities (VAs, has become a popular topic. We show here that our trinomial tree model can been used to price EIA with strong path dependent exotic options in the regime switching model.
International Nuclear Information System (INIS)
Bouttier, J; Francesco, P Di; Guitter, E
2007-01-01
We introduce Eulerian maps with blocked edges as a general way to implement statistical matter models on random maps by a modification of intrinsic distances. We show how to code these dressed maps by means of mobiles, i.e. decorated trees with labelled vertices, leading to a closed system of recursion relations for their generating functions. We discuss particular solvable cases in detail, as well as various applications of our method to several statistical systems such as spanning trees on quadrangulations, mutually excluding particles on Eulerian triangulations or the Ising model on quadrangulations
Herguido, Estela; Pulido, Manuel; Francisco Lavado Contador, Joaquín; Schnabel, Susanne
2017-04-01
In Iberian dehesas and montados, the lack of tree recruitment compromises its long-term sustainability. However, in marginal areas of dehesas shrub encroachment facilitates tree recruitment while altering the distinctive physiognomic and cultural characteristics of the system. These are ongoing processes that should be considered when designing afforestation measures and policies. Based on spatial variables, we modeled the proneness of a piece of land to undergo tree recruitment and the results were related with the afforestation measures carried out under the UE First Afforestation Agricultural Land Program between 1992 and 2008. We analyzed the temporal tree population dynamics in 800 randomly selected plots of 100 m radius (2,510 ha in total) in dehesas and treeless pasturelands of Extremadura (hereafter rangelands). Tree changes were revealed by comparing aerial images taken in 1956 with orthophotographs and infrared ones from 2012. Spatial models that predict the areas prone either to lack tree recruitment or with recruitment were developed and based on three data mining algorithms: MARS (Multivariate Adaptive Regression Splines), Random Forest (RF) and Stochastic Gradient Boosting (Tree-Net, TN). Recruited-tree locations (1) vs. locations of places with no recruitment (0) (randomly selected from the study areas) were used as the binary dependent variable. A 5% of the data were used as test data set. As candidate explanatory variables we used 51 different topographic, climatic, bioclimatic, land cover-related and edaphic ones. The statistical models developed were extrapolated to the spatial context of the afforested areas in the region and also to the whole Extremenian rangelands, and the percentage of area modelled as prone to tree recruitment was calculated for each case. A total of 46,674.63 ha were afforested with holm oak (Quercus ilex) or cork oak (Quercus suber) in the studied rangelands under the UE First Afforestation Agricultural Land Program. In
International Nuclear Information System (INIS)
Regentova, E.; Zhang, L.; Veni, G.; Zheng, J.
2007-01-01
A system is designed for detecting microcalcification clusters (MCC) in digital mammograms. The system is intended for computer-aided diagnostic prompting. Further discrimination of MCC as benign or malignant is assumed to be performed by radiologists. Processing of mammograms is based on the statistical modeling by means of wavelet domain hidden markov trees (WHMT). Segmentation is performed by the weighted likelihood evaluation followed by the classification based on spatial filters for a single microcalcification (MC) and a cluster of MC detection. The analysis is carried out on FROC curves for 40 mammograms from the mini-MIAS database and for 100 mammograms with 50 cancerous and 50 benign cases from DDSM database. The designed system is capable to detect 100% of true positive cases in these sets. The rate of false positives is 2.9 per case for mini-MIAS dataset; and 0.01 for the DDSM images. (orig.)
Measuring and modelling above-ground carbon and tree allometry along a tropical elevation gradient
DEFF Research Database (Denmark)
Marshall, A.R.; Willcock, S.; Platts, P.J.
2012-01-01
of physical, climatic and edaphic predictors of AGC and tree stature. AGC estimates using stem diameter, height and wood density, gave a mean value of 174.6 t ha−1, compared with 229.6 t ha−1 when height was excluded. Regression models revealed that stems were tallest for a given diameter at mid......:benefit of different measurements and recommend a tiered approach to AGC monitoring, depending on available resources. AGC assessments in African forests could exclude small stems, but should aim to record disturbance, topography and species. Stem height is vital for AGC estimation and valuation; when excluding height...... our 55 t ha−1 over-estimation of AGC would have over-valued the carbon resource by 24% (US$3300 ha−1)....
Guo, Shu Hai; Wu, Bo
2017-12-01
Aquatic ecological regionalization and aquatic ecological function regionalization are the basis of water environmental management of a river basin and rational utilization of an aquatic ecosystem, and have been studied in China for more than ten years. Regarding the common problems in this field, the relationship between aquatic ecological regionalization and aquatic ecological function regionalization was discussed in this study by systematic analysis of the aquatic ecological zoning and the types of aquatic ecological function. Based on the dual tree structure, we put forward the RFCH process and the diamond conceptual model. Taking Liaohe River basin as an example and referring to the results of existing regionalization studies, we classified the aquatic ecological function regions based on three-class aquatic ecological regionalization. This study provided a process framework for aquatic ecological function regionalization of a river basin.
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Isabel C. Pérez Hoyos
2016-04-01
Full Text Available Groundwater Dependent Ecosystems (GDEs are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT and random forests (RF. Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found.
Modeling the compensatory response of an invasive tree to specialist insect herbivory
Zhang, Bo; Liu, Xin; DeAngelis, Donald L.; Zhai, Lu; Rayamajhi, Min B.; Ju, Shu
2018-01-01
The severity of the effects of herbivory on plant fitness can be moderated by the ability of plants to compensate for biomass loss. Compensation is an important component of the ecological fitness in many plants, and has been shown to reduce the effects of pests on agricultural plant yields. It can also reduce the effectiveness of biocontrol through introduced herbivores in controlling weedy invasive plants. This study used a modeling approach to predict the effect of different levels of foliage herbivory by biological control agents introduced to control the invasive tree Melaleuca quinquennervia (melaleuca) in Florida. It is assumed in the model that melaleuca can optimally change its carbon and nitrogen allocation strategies in order to compensate for the effects of herbivory. The model includes reallocation of more resources to production and maintenance of photosynthetic tissues at the expense of roots. This compensation is shown to buffer the severity of the defoliation effect, but the model predicts a limit on the maximum herbivory that melaleuca can tolerate and survive. The model also shows that the level of available limiting nutrient (e.g., soil nitrogen) may play an important role in a melaleuca’s ability to compensate for herbivory. This study has management implications for the best ways to maximize the level of damage using biological control or other means of defoliation.
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F. Gennaretti
2017-11-01
Full Text Available A better understanding of the coupling between photosynthesis and carbon allocation in the boreal forest, together with its associated environmental factors and mechanistic rules, is crucial to accurately predict boreal forest carbon stocks and fluxes, which are significant components of the global carbon budget. Here, we adapted the MAIDEN ecophysiological forest model to consider important processes for boreal tree species, such as nonlinear acclimation of photosynthesis to temperature changes, canopy development as a function of previous-year climate variables influencing bud formation and the temperature dependence of carbon partition in summer. We tested these modifications in the eastern Canadian taiga using black spruce (Picea mariana (Mill. B.S.P. gross primary production and ring width data. MAIDEN explains 90 % of the observed daily gross primary production variability, 73 % of the annual ring width variability and 20–30 % of its high-frequency component (i.e., when decadal trends are removed. The positive effect on stem growth due to climate warming over the last several decades is well captured by the model. In addition, we illustrate how we improve the model with each introduced model adaptation and compare the model results with those of linear response functions. Our results demonstrate that MAIDEN simulates robust relationships with the most important climate variables (those detected by classical response-function analysis and is a powerful tool for understanding how environmental factors interact with black spruce ecophysiology to influence present-day and future boreal forest carbon fluxes.
Gennaretti, Fabio; Gea-Izquierdo, Guillermo; Boucher, Etienne; Berninger, Frank; Arseneault, Dominique; Guiot, Joel
2017-11-01
A better understanding of the coupling between photosynthesis and carbon allocation in the boreal forest, together with its associated environmental factors and mechanistic rules, is crucial to accurately predict boreal forest carbon stocks and fluxes, which are significant components of the global carbon budget. Here, we adapted the MAIDEN ecophysiological forest model to consider important processes for boreal tree species, such as nonlinear acclimation of photosynthesis to temperature changes, canopy development as a function of previous-year climate variables influencing bud formation and the temperature dependence of carbon partition in summer. We tested these modifications in the eastern Canadian taiga using black spruce (Picea mariana (Mill.) B.S.P.) gross primary production and ring width data. MAIDEN explains 90 % of the observed daily gross primary production variability, 73 % of the annual ring width variability and 20-30 % of its high-frequency component (i.e., when decadal trends are removed). The positive effect on stem growth due to climate warming over the last several decades is well captured by the model. In addition, we illustrate how we improve the model with each introduced model adaptation and compare the model results with those of linear response functions. Our results demonstrate that MAIDEN simulates robust relationships with the most important climate variables (those detected by classical response-function analysis) and is a powerful tool for understanding how environmental factors interact with black spruce ecophysiology to influence present-day and future boreal forest carbon fluxes.
Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate
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Yu-sheng Cheng
2014-01-01
Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.
International Nuclear Information System (INIS)
Makela, A.
2003-01-01
A generally accepted method has not emerged for managing the different temporal and spatial scales in a forest ecosystem. This paper reviews a hierarchical-modular modelling tradition, with the main focus on individual tree growth throughout the rotation. At this scale, model performance requires (i) realistic long-term dynamic properties, (ii) realistic responses of growth and mortality of competing individuals, and (iii) realistic responses to ecophysio-logical inputs. Model development and validation are illustrated through allocation patterns, height growth, and size-related feedbacks. Empirical work to test the approach is reviewed. In this approach, finer scale effects are embedded in parameters calculated using more detailed, interacting modules. This is exemplified by (i) the within-year effect of weather on annual photosynthesis, (ii) the effects of fast soil processes on carbon allocation and photosynthesis, and (iii) the utilization of detailed stem structure to predict wood quality. Prevailing management paradigms are reflected in growth modelling. A shift of emphasis has occurred from productivity in homogeneous canopies towards, e.g., wood quality versus total yield, spatially more explicit models, and growth decline in old-growth forests. The new problems emphasize the hierarchy of the system and interscale interactions, suggesting that the hierarchical-modular approach could prove constructive. (author)
Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk
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Khaled Halteh
2018-05-01
Full Text Available Credit risk is a critical issue that affects banks and companies on a global scale. Possessing the ability to accurately predict the level of credit risk has the potential to help the lender and borrower. This is achieved by alleviating the number of loans provided to borrowers with poor financial health, thereby reducing the number of failed businesses, and, in effect, preventing economies from collapsing. This paper uses state-of-the-art stochastic models, namely: Decision trees, random forests, and stochastic gradient boosting to add to the current literature on credit-risk modelling. The Australian mining industry has been selected to test our methodology. Mining in Australia generates around $138 billion annually, making up more than half of the total goods and services. This paper uses publicly-available financial data from 750 risky and not risky Australian mining companies as variables in our models. Our results indicate that stochastic gradient boosting was the superior model at correctly classifying the good and bad credit-rated companies within the mining sector. Our model showed that ‘Property, Plant, & Equipment (PPE turnover’, ‘Invested Capital Turnover’, and ‘Price over Earnings Ratio (PER’ were the variables with the best explanatory power pertaining to predicting credit risk in the Australian mining sector.
Defining modeling parameters for juniper trees assuming pleistocene-like conditions at the NTS
International Nuclear Information System (INIS)
Tarbox, S.R.; Cochran, J.R.
1994-01-01
This paper addresses part of Sandia National Laboratories' (SNL) efforts to assess the long-term performance of the Greater Confinement Disposal (GCD) facility located on the Nevada Test Site (NTS). Of issue is whether the GCD site complies with 40 CFR 191 standards set for transuranic (TRU) waste burial. SNL has developed a radionuclide transport model which can be used to assess TRU radionuclide movement away from the GCD facility. An earlier iteration of the model found that radionuclide uptake and release by plants is an important aspect of the system to consider. Currently, the shallow-rooted plants at the NTS do not pose a threat to the integrity of the GCD facility. However, the threat increases substantially it deeper-rooted woodland species migrate to the GCD facility, given a shift to a wetter climate. The model parameters discussed here will be included in the next model iteration which assumes a climate shift will provide for the growth of juniper trees at the GCD facility. Model parameters were developed using published data and wherever possible, data were taken from juniper and pinon-juniper studies that mirrored as many aspects of the GCD facility as possible
DO3SE modelling of soil moisture to determine ozone flux to forest trees
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M. Schaub
2012-06-01
Full Text Available The DO3SE (Deposition of O3 for Stomatal Exchange model is an established tool for estimating ozone (O3 deposition, stomatal flux and impacts to a variety of vegetation types across Europe. It has been embedded within the EMEP (European Monitoring and Evaluation Programme photochemical model to provide a policy tool capable of relating the flux-based risk of vegetation damage to O3 precursor emission scenarios for use in policy formulation. A key limitation of regional flux-based risk assessments has been the assumption that soil water deficits are not limiting O3 flux due to the unavailability of evaluated methods for modelling soil water deficits and their influence on stomatal conductance (gsto, and subsequent O3 flux. This paper describes the development and evaluation of a method to estimate soil moisture status and its influence on gsto for a variety of forest tree species. This DO3SE soil moisture module uses the Penman-Monteith energy balance method to drive water cycling through the soil-plant-atmosphere system and empirical data describing gsto relationships with pre-dawn leaf water status to estimate the biological control of transpiration. We trial four different methods to estimate this biological control of the transpiration stream, which vary from simple methods that relate soil water content or potential directly to gsto, to more complex methods that incorporate hydraulic resistance and plant capacitance that control water flow through the plant system. These methods are evaluated against field data describing a variety of soil water variables, gsto and transpiration data for Norway spruce (Picea abies, Scots pine (Pinus sylvestris, birch (Betula pendula, aspen (Populus tremuloides, beech (Fagus sylvatica and holm oak (Quercus ilex collected from ten sites across Europe and North America. Modelled estimates of these variables show consistency with observed data when applying the simple empirical methods, with the timing and
Directory of Open Access Journals (Sweden)
Kajungu Dan K
2012-09-01
Full Text Available Abstract Background Drug prescription practices depend on several factors related to the patient, health worker and health facilities. A better understanding of the factors influencing prescription patterns is essential to develop strategies to mitigate the negative consequences associated with poor practices in both the public and private sectors. Methods A cross-sectional study was conducted in rural Tanzania among patients attending health facilities, and health workers. Patients, health workers and health facilities-related factors with the potential to influence drug prescription patterns were used to build a model of key predictors. Standard data mining methodology of classification tree analysis was used to define the importance of the different factors on prescription patterns. Results This analysis included 1,470 patients and 71 health workers practicing in 30 health facilities. Patients were mostly treated in dispensaries. Twenty two variables were used to construct two classification tree models: one for polypharmacy (prescription of ≥3 drugs on a single clinic visit and one for co-prescription of artemether-lumefantrine (AL with antibiotics. The most important predictor of polypharmacy was the diagnosis of several illnesses. Polypharmacy was also associated with little or no supervision of the health workers, administration of AL and private facilities. Co-prescription of AL with antibiotics was more frequent in children under five years of age and the other important predictors were transmission season, mode of diagnosis and the location of the health facility. Conclusion Standard data mining methodology is an easy-to-implement analytical approach that can be useful for decision-making. Polypharmacy is mainly due to the diagnosis of multiple illnesses.
Models to estimate volume of individual trees by morphometry of crowns obtained with lidar
Directory of Open Access Journals (Sweden)
Evandro Orfanó Figueiredo
2014-12-01
Full Text Available The volumetric estimate from digital scanning of the forests through the use of LIDAR increases the precision of forest management techniques in planning tropical forest logging operations. The use of this remote detection technology allows the incorporation of crown morphometric variables which are still little known and little used due to the difficulty of collecting field data for volume equations. The objective of this study was to build equations capable of estimating the stem volume of dominant and codominant individual trees from the crown's morphometry obtained by airborne LIDAR, considering two forest inventory situations: a with the collection of diameter at breast height (DBH, and crown morphometric variables obtained from LIDAR data and b using only the crown morphometry variables. For the selection of models the factors considered were: the correlation matrix of predictor variables and the combination of variables that generates the best results by statistical criteria Syx, Syx(% and Pressp, and that were homoscedastic and had a normal and independent distribution of errors. The influence analysis was performed for the best equations. The results for the statistical fit of the equations to the two situations allowed the selection of models with and without DBH, with R2aj.( % values of a 92.92 and b 79.44, Syx(% values of a 16.73 and b 27.47, and, Pressp criterion values of a 201.15 m6 and b 537.47 m6, respectively. Through morphometric variables it was possible to develop equations capable of accurately estimating the stem volume of dominant and codominant trees in tropical forests.
DEFF Research Database (Denmark)
Rasmussen, Mads Olander; Goettsche, Frank-M.; Diop, Doudou
2011-01-01
A tree survey and an analysis of high resolution satellite data were performed to characterise the woody vegetation within a 10 x 10 km(2) area around a site located close to the town of Dahra in the semiarid northern part of Senegal. The surveyed parameters were tree species, height, tree crown...
A global analysis of the comparability of winter chill models for fruit and nut trees.
Luedeling, Eike; Brown, Patrick H
2011-05-01
Many fruit and nut trees must fulfill a chilling requirement to break their winter dormancy and resume normal growth in spring. Several models exist for quantifying winter chill, and growers and researchers often tacitly assume that the choice of model is not important and estimates of species chilling requirements are valid across growing regions. To test this assumption, Safe Winter Chill (the amount of winter chill that is exceeded in 90% of years) was calculated for 5,078 weather stations around the world, using the Dynamic Model [in Chill Portions (CP)], the Chilling Hours (CH) Model and the Utah Model [Utah Chill Units (UCU)]. Distributions of the ratios between different winter chill metrics were mapped on a global scale. These ratios should be constant if the models were strictly proportional. Ratios between winter chill metrics varied substantially, with the CH/CP ratio ranging between 0 and 34, the UCU/CP ratio between -155 and +20 and the UCU/CH ratio between -10 and +5. The models are thus not proportional, and chilling requirements determined in a given location may not be valid elsewhere. The Utah Model produced negative winter chill totals in many Subtropical regions, where it does not seem to be useful. Mean annual temperature and daily temperature range influenced all winter chill ratios, but explained only between 12 and 27% of the variation. Data on chilling requirements should always be amended with information on the location and experimental conditions of the study in which they were determined, ideally including site-specific conversion factors between winter chill models. This would greatly facilitate the transfer of such information across growing regions, and help prepare growers for the impact of climate change.
Discrete dynamic event tree modeling and analysis of nuclear power plant crews for safety assessment
International Nuclear Information System (INIS)
Mercurio, D.
2011-01-01
Current Probabilistic Risk Assessment (PRA) and Human Reliability Analysis (HRA) methodologies model the evolution of accident sequences in Nuclear Power Plants (NPPs) mainly based on Logic Trees. The evolution of these sequences is a result of the interactions between the crew and plant; in current PRA methodologies, simplified models of these complex interactions are used. In this study, the Accident Dynamic Simulator (ADS), a modeling framework based on the Discrete Dynamic Event Tree (DDET), has been used for the simulation of crew-plant interactions during potential accident scenarios in NPPs. In addition, an operator/crew model has been developed to treat the response of the crew to the plant. The 'crew model' is made up of three operators whose behavior is guided by a set of rules-of-behavior (which represents the knowledge and training of the operators) coupled with written and mental procedures. In addition, an approach for addressing the crew timing variability in DDETs has been developed and implemented based on a set of HRA data from a simulator study. Finally, grouping techniques were developed and applied to the analysis of the scenarios generated by the crew-plant simulation. These techniques support the post-simulation analysis by grouping similar accident sequences, identifying the key contributing events, and quantifying the conditional probability of the groups. These techniques are used to characterize the context of the crew actions in order to obtain insights for HRA. The model has been applied for the analysis of a Small Loss Of Coolant Accident (SLOCA) event for a Pressurized Water Reactor (PWR). The simulation results support an improved characterization of the performance conditions or context of operator actions, which can be used in an HRA, in the analysis of the reliability of the actions. By providing information on the evolution of system indications, dynamic of cues, crew timing in performing procedure steps, situation
Paleo Data Assimilation of Pseudo-Tree-Ring-Width Chronologies in a Climate Model
Fallah Hassanabadi, B.; Acevedo, W.; Reich, S.; Cubasch, U.
2016-12-01
Using the Time-Averaged Ensemble Kalman Filter (EnKF) and a forward model, we assimilate the pseudo Tree-Ring-Width (TRW) chronologies into an Atmospheric Global Circulation model. This study investigates several aspects of Paleo-Data Assimilation (PDA) within a perfect-model set-up: (i) we test the performance of several forward operators in the framework of a PDA-based climate reconstruction, (ii) compare the PDA-based simulations' skill against the free ensemble runs and (iii) inverstigate the skill of the "online" (with cycling) DA and the "off-line" (no-cycling) DA. In our experiments, the "online" (with cycling) PDA approach did not outperform the "off-line" (no-cycling) one, despite its considerable additional implementation complexity. On the other hand, it was observed that the error reduction achieved by assimilating a particular pseudo-TRW chronology is modulated by the strength of the yearly internal variability of the model at the chronology site. This result might help the dendrochronology community to optimize their sampling efforts.
Analyzing dynamic fault trees derived from model-based system architectures
International Nuclear Information System (INIS)
Dehlinger, Josh; Dugan, Joanne Bechta
2008-01-01
Dependability-critical systems, such as digital instrumentation and control systems in nuclear power plants, necessitate engineering techniques and tools to provide assurances of their safety and reliability. Determining system reliability at the architectural design phase is important since it may guide design decisions and provide crucial information for trade-off analysis and estimating system cost. Despite this, reliability and system engineering remain separate disciplines and engineering processes by which the dependability analysis results may not represent the designed system. In this article we provide an overview and application of our approach to build architecture-based, dynamic system models for dependability-critical systems and then automatically generate Dynamic Fault Trees (DFT) for comprehensive, toolsupported reliability analysis. Specifically, we use the Architectural Analysis and Design Language (AADL) to model the structural, behavioral and failure aspects of the system in a composite architecture model. From the AADL model, we seek to derive the DFT(s) and use Galileo's automated reliability analyses to estimate system reliability. This approach alleviates the dependability engineering - systems engineering knowledge expertise gap, integrates the dependability and system engineering design and development processes and enables a more formal, automated and consistent DFT construction. We illustrate this work using an example based on a dynamic digital feed-water control system for a nuclear reactor
SCREENING OF WILD FRUIT TREES WITH GASTROPROTECTIVE ACTIVITY IN DIFFERENT EXPERIMENTAL MODELS.
Nesello, Luciane Angela Nottar; Campos, Adriana; Rosa, Roseane Leandra da; Andrade, Sérgio Faloni de; Cechinel, Valdir
2017-01-01
Given the increase of people with gastrointestinal disorders, the search for alternative treatments with fewer side effects is vital, as well as the demand for food or plants that can help protect the stomach. The aim of this study was to evaluate the gastroprotective action of the extracts of wild fruit trees of Myrcianthes pungens (guabiju); Inga vera Willd. (ingá-banana) and Marlierea tomentosa Cambess. (guarapuruna) in in vivo pharmacological models. The different parts of the fruits were separately subjected to a process of extraction by methanol. Two experimental pharmacological models were conducted in mice; the gastric ulcer model induced by non-steroidal anti-inflammatory (indomethacin), and the gastric ulcer model induced by ethanol/HCl, which allowed us to evaluate the gastroprotective activity of the extracts at a dose of 250 mg/kg. Subsequently, the total lesion area (mm2) and relative lesion area (%) were determined. The results showed significant gastroprotective activity against the aggressive agents used - ethanol and indomethacin - for all the extracts tested. It is assumed that the fruits have bioactive compounds such as antioxidant substances that act on the prostaglandin levels, protecting them from the damage caused by ethanol and indomethacin. These results prompt further studies to isolate and identify the active properties.
Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant
2015-07-01
Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.
Modeling a two-layer flow system at the subarctic, subalpine tree line during snowmelt
Leenders, Erica E.; Woo, Ming-Ko
2002-10-01
In the subarctic it is common to encounter a two-layer flow system consisting of a porous organic cover overlying frozen or unfrozen mineral soils with much lower hydraulic conductivities. The "simple lumped reservoir parametric," or "semidistributed land-use-based runoff processes" (SLURP), model was adapted to simulate runoff generated by such a flow system from an upland shrub land to an open woodland downslope. A subalpine site in Wolf Creek, Yukon, Canada, was subdivided into two aggregated simulation areas (ASA), each being a unit characterized by a set of parameters. The model computes the vertical water balance and flow generation from several storages, and then routes the water out of the ASA. When applied to the 1999 snowmelt season, the model simulated the very low lateral flow and a large increase in storage in the mineral soil, as was observed in the field. The model was used to assess the sensitivity of the two-layer flow system under a range of temperature, snow cover, and frost conditions. Results show that within the range of possible climatic conditions, the hydrologic system is unlikely to yield significant runoff across the subalpine tree line, but if ground ice is abundant in the soil pores, percolation will be limited and fast flow from the surface layer is enhanced.
Regression trees modeling and forecasting of PM10 air pollution in urban areas
Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.
2017-10-01
Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.
[A prediction model for internet game addiction in adolescents: using a decision tree analysis].
Kim, Ki Sook; Kim, Kyung Hee
2010-06-01
This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.
Aguiar, Fabio S; Almeida, Luciana L; Ruffino-Netto, Antonio; Kritski, Afranio Lineu; Mello, Fernanda Cq; Werneck, Guilherme L
2012-08-07
Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in
Couvreur, Valentin; Ledder, Glenn; Manzoni, Stefano; Way, Danielle A; Muller, Erik B; Russo, Sabrina E
2018-05-08
Trees grow by vertically extending their stems, so accurate stem hydraulic models are fundamental to understanding the hydraulic challenges faced by tall trees. Using a literature survey, we showed that many tree species exhibit continuous vertical variation in hydraulic traits. To examine the effects of this variation on hydraulic function, we developed a spatially-explicit, analytical water transport model for stems. Our model allows Huber ratio, stem-saturated conductivity, pressure at 50% loss of conductivity, leaf area, and transpiration rate to vary continuously along the hydraulic path. Predictions from our model differ from a matric flux potential model parameterized with uniform traits. Analyses show that cavitation is a whole-stem emergent property resulting from nonlinear pressure-conductivity feedbacks that, with gravity, cause impaired water transport to accumulate along the path. Because of the compounding effects of vertical trait variation on hydraulic function, growing proportionally more sapwood and building tapered xylem with height, as well as reducing xylem vulnerability only at branch tips while maintaining transport capacity at the stem base, can compensate for these effects. We therefore conclude that the adaptive significance of vertical variation in stem hydraulic traits is to allow trees to grow tall and tolerate operating near their hydraulic limits. This article is protected by copyright. All rights reserved.
Categorizing ideas about trees: a tree of trees.
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.
Directory of Open Access Journals (Sweden)
Thiago Augusto da Cunha
2013-01-01
Full Text Available Reliable growth data from trees are important to establish a rational forest management. Characteristics from trees, like the size, crown architecture and competition indices have been used to mathematically describe the increment efficiently when associated with them. However, the precise role of these effects in the growth-modeling destined to tropical trees needs to be further studied. Here it is reconstructed the basal area increment (BAI of individual Cedrela odorata trees, sampled at Amazon forest, to develop a growth- model using potential-predictors like: (1 classical tree size; (2 morphometric data; (3 competition and (4 social position including liana loads. Despite the large variation in tree size and growth, we observed that these kinds of predictor variables described well the BAI in level of individual tree. The fitted mixed model achieve a high efficiency (R2=92.7 % and predicted 3-years BAI over bark for trees of Cedrela odorata ranging from 10 to 110 cm at diameter at breast height. Tree height, steam slenderness and crown formal demonstrated high influence in the BAI growth model and explaining most of the growth variance (Partial R2=87.2%. Competition variables had negative influence on the BAI, however, explained about 7% of the total variation. The introduction of a random parameter on the regressions model (mixed modelprocedure has demonstrated a better significance approach to the data observed and showed more realistic predictions than the fixed model.
Optimisation of Hidden Markov Model using Baum–Welch algorithm ...
Indian Academy of Sciences (India)
The present work is a part of development of Hidden Markov Model. (HMM) based ... the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum ..... data collection teams of Snow and Avalanche Study.
Ballinas, Mónica; Barradas, Víctor L
2016-01-01
The urban heat island (UHI) is mainly a nocturnal phenomenon, but it also appears during the day in Mexico City. The UHI may affect human thermal comfort, which can influence human productivity and morbidity in the spring/summer period. A simple phenomenological model based on the energy balance was developed to generate theoretical support of UHI mitigation in Mexico City focused on the latent heat flux change by increasing tree coverage to reduce sensible heat flux and air temperature. Half-hourly data of the urban energy balance components were generated in a typical residential/commercial neighborhood of Mexico City and then parameterized using easily measured variables (air temperature, humidity, pressure, and visibility). Canopy conductance was estimated every hour in four tree species, and transpiration was estimated using sap flow technique and parameterized by the envelope function method. Averaged values of net radiation, energy storage, and sensible and latent heat flux were around 449, 224, 153, and 72 W m, respectively. Daily tree transpiration ranged from 3.64 to 4.35 Ld. To reduce air temperature by 1°C in the studied area, 63 large would be required per hectare, whereas to reduce the air temperature by 2°C only 24 large trees would be required. This study suggests increasing tree canopy cover in the city cannot mitigate UHI adequately but requires choosing the most appropriate tree species to solve this problem. It is imperative to include these types of studies in tree selection and urban development planning to adequately mitigate UHI. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Bayesian Models for Streamflow and River Network Reconstruction using Tree Rings
Ravindranath, A.; Devineni, N.
2016-12-01
Water systems face non-stationary, dynamically shifting risks due to shifting societal conditions and systematic long-term variations in climate manifesting as quasi-periodic behavior on multi-decadal time scales. Water systems are thus vulnerable to long periods of wet or dry hydroclimatic conditions. Streamflow is a major component of water systems and a primary means by which water is transported to serve ecosystems' and human needs. Thus, our concern is in understanding streamflow variability. Climate variability and impacts on water resources are crucial factors affecting streamflow, and multi-scale variability increases risk to water sustainability and systems. Dam operations are necessary for collecting water brought by streamflow while maintaining downstream ecological health. Rules governing dam operations are based on streamflow records that are woefully short compared to periods of systematic variation present in the climatic factors driving streamflow variability and non-stationarity. We use hierarchical Bayesian regression methods in order to reconstruct paleo-streamflow records for dams within a basin using paleoclimate proxies (e.g. tree rings) to guide the reconstructions. The riverine flow network for the entire basin is subsequently modeled hierarchically using feeder stream and tributary flows. This is a starting point in analyzing streamflow variability and risks to water systems, and developing a scientifically-informed dynamic risk management framework for formulating dam operations and water policies to best hedge such risks. We will apply this work to the Missouri and Delaware River Basins (DRB). Preliminary results of streamflow reconstructions for eight dams in the upper DRB using standard Gaussian regression with regional tree ring chronologies give streamflow records that now span two to two and a half centuries, and modestly smoothed versions of these reconstructed flows indicate physically-justifiable trends in the time series.
A decision-tree-based model for evaluating the thermal comfort of horses
Directory of Open Access Journals (Sweden)
Ana Paula de Assis Maia
2013-12-01
Full Text Available Thermal comfort is of great importance in preserving body temperature homeostasis during thermal stress conditions. Although the thermal comfort of horses has been widely studied, there is no report of its relationship with surface temperature (T S. This study aimed to assess the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. T S was obtained using infrared thermography image processing. Physiological and environmental variables were used to define the predicted class, which classified thermal comfort as "comfort" and "discomfort". The variables of armpit, croup, breast and groin T S of horses and the predicted classes were then subjected to a machine learning process. All variables in the dataset were considered relevant for the classification problem and the decision-tree model yielded an accuracy rate of 74 %. The feature selection methods used to reduce computational cost and simplify predictive learning decreased model accuracy to 70 %; however, the model became simpler with easily interpretable rules. For both these selection methods and for the classification using all attributes, armpit and breast T S had a higher power rating for predicting thermal comfort. Data mining techniques show promise in the discovery of new variables associated with the thermal comfort of horses.
A simulation model of distributions of radiational flux at leaf surfaces in crowns of fruit trees
International Nuclear Information System (INIS)
Yamamoto, T.
1988-01-01
A computer-model was constructed for estimating distributions with time of radiational fluxes at leaf surfaces throughout fruit tree canopies in which leaves did not distribute uniformely in three dimensional space. Several assumptions were set up to construct the model for approximation of using solid geometry. For irregular distribution of leaf area in three dimensional space data were used in the simulation as number of leaves per internal cubic bloc of a cubic grid (n-divided per side). Several main parameters used were peculiar to fruit species which contain parameters (λ, ν) of Beta function to calculate both probability density function of leaf area distribution with respect to inclination angle and leaf extinction coefficient for parallel beam by leaves parameters (A, R i ) to calculate stem extinction coefficient for parallel beam, and parameters (D i ) to calculate leaf extinction coefficient of downward transmission and downward reflection. With these data and parameters solid geometry and Lambert-Beer's law constituted this model
Estimating carbon and showing impacts of drought using satellite data in regression-tree models
Boyte, Stephen; Wylie, Bruce K.; Howard, Danny; Dahal, Devendra; Gilmanov, Tagir G.
2018-01-01
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.
On factor associated with the unordered phase of λ-model on a Cayley tree
International Nuclear Information System (INIS)
Mukhamedov, Farruh
2002-11-01
In this paper we consider nearest neighbors models where the spin takes values in the set Φ={η 1 , η 2 ,..., η q } and is assigned to the vertices of the Cayley tree Γ k . A configuration σ on Γ k is then defined as a function x is element of Γ k →σ(x) is element of Φ; the set of all configurations coincides with Ω=Φ Γ k . The Hamiltonian is of an λ-model form: H λ (σ)=Σ λ(σ(x), σ(y); J), where J is element of R n is a coupling constant and the sum is taken over all pairs of neighboring vertices , σ is element of Ω. Here λ:ΦxΦxR n →R is some given function. We find a condition for the function λ to determine a type of von Neumann algebra generated by the GNS-construction associated with the unordered phase of the λ-model. (author)
DAG-based attack and defense modeling: don’t miss the forest for the attack trees
Kordy, Barbara; Piètre-Cambacédès, Ludovic; Schweitzer, Patrick
2015-01-01
This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and
Energy Technology Data Exchange (ETDEWEB)
Mukhamedov, Farrukh, E-mail: far75m@yandex.ru, E-mail: farrukh.m@uaeu.ac.ae [International Islamic University Malaysia, Department of Computational and Theoretical Sciences, Faculty of Science (Malaysia); Barhoumi, Abdessatar, E-mail: abdessatar.barhoumi@ipein.rnu.tn [Carthage University, Department of Mathematics, Nabeul Preparatory Engineering Institute (Tunisia); Souissi, Abdessatar, E-mail: s.abdessatar@hotmail.fr [Carthage University, Department of Mathematics, Marsa Preparatory Institute for Scientific and Technical Studies (Tunisia)
2016-12-15
It is known that the disordered phase of the classical Ising model on the Caley tree is extreme in some region of the temperature. If one considers the Ising model with competing interactions on the same tree, then about the extremity of the disordered phase there is no any information. In the present paper, we first aiming to analyze the correspondence between Gibbs measures and QMC’s on trees. Namely, we establish that states associated with translation invariant Gibbs measures of the model can be seen as diagonal quantum Markov chains on some quasi local algebra. Then as an application of the established correspondence, we study some algebraic property of the disordered phase of the Ising model with competing interactions on the Cayley tree of order two. More exactly, we prove that a state corresponding to the disordered phase is not quasi-equivalent to other states associated with translation invariant Gibbs measures. This result shows how the translation invariant states relate to each other, which is even a new phenomena in the classical setting. To establish the main result we basically employ methods of quantum Markov chains.
Moonen, P.; Gromke, C.B.; Dorer, V.
2013-01-01
The potential of a Large Eddy Simulation (LES) model to reliably predict near-field pollutant dispersion is assessed. To that extent, detailed time-resolved numerical simulations of coupled flow and dispersion are conducted for a street canyon with tree planting. Different crown porosities are
Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools
Ruijters, Enno Jozef Johannes; Stoelinga, Mariëlle Ida Antoinette
2015-01-01
Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software
Fault Tree Analysis: A survey of the state-of-the-art in modeling, analysis and tools
Ruijters, Enno Jozef Johannes; Stoelinga, Mariëlle Ida Antoinette
2014-01-01
Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software
Fei Chen; Guo Zhang; Michael Barlage; Ying Zhang; Jeffrey A. Hicke; Arjan Meddens; Guangsheng Zhou; William J. Massman; John Frank
2015-01-01
Bark beetle outbreaks have killed billions of trees and affected millions of hectares of forest during recent decades. The objective of this study was to quantify responses of surface energy and hydrologic fluxes 2-3 yr following a spruce beetle outbreak using measurements and modeling. The authors used observations at the Rocky Mountains Glacier Lakes Ecosystem...
Attack Trees with Sequential Conjunction
Jhawar, Ravi; Kordy, Barbara; Mauw, Sjouke; Radomirović, Sasa; Trujillo-Rasua, Rolando
2015-01-01
We provide the first formal foundation of SAND attack trees which are a popular extension of the well-known attack trees. The SAND at- tack tree formalism increases the expressivity of attack trees by intro- ducing the sequential conjunctive operator SAND. This operator enables the modeling of
Heddam, Salim; Kisi, Ozgur
2018-04-01
In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three models. The water quality data selected consisted of daily measured of water temperature (TE, °C), pH (std. unit), specific conductance (SC, μS/cm) and discharge (DI cfs), are used as inputs to the LSSVM, MARS and M5T models. The three models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three models and (ii) the best model among all others differs from one station to another.
Evaluating bacterial gene-finding HMM structures as probabilistic logic programs
DEFF Research Database (Denmark)
Mørk, Søren; Holmes, Ian
2012-01-01
, a probabilistic dialect of Prolog. Results: We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length...
Alexander, Cici; Korstjens, Amanda H.; Hill, Ross A.
2018-03-01
Tree or canopy height is an important attribute for carbon stock estimation, forest management and habitat quality assessment. Airborne Laser Scanning (ALS) based on Light Detection and Ranging (LiDAR) has advantages over other remote sensing techniques for describing the structure of forests. However, sloped terrain can be challenging for accurate estimation of tree locations and heights based on a Canopy Height Model (CHM) generated from ALS data; a CHM is a height-normalised Digital Surface Model (DSM) obtained by subtracting a Digital Terrain Model (DTM) from a DSM. On sloped terrain, points at the same elevation on a tree crown appear to increase in height in the downhill direction, based on the ground elevations at these points. A point will be incorrectly identified as the treetop by individual tree crown (ITC) recognition algorithms if its height is greater than that of the actual treetop in the CHM, which will be recorded as the tree height. In this study, the influence of terrain slope and crown characteristics on the detection of treetops and estimation of tree heights is assessed using ALS data in a tropical forest with complex terrain (i.e. micro-topography) and tree crown characteristics. Locations and heights of 11,442 trees based on a DSM are compared with those based on a CHM. The horizontal (DH) and vertical displacements (DV) increase with terrain slope (r = 0.47 and r = 0.54 respectively, p tree height are up to 16.6 m on slopes greater than 50° in our study area in Sumatra. The errors in locations (DH) and tree heights (DV) are modelled for trees with conical and spherical tree crowns. For a spherical tree crown, DH can be modelled as R sin θ, and DV as R (sec θ - 1). In this study, a model is developed for an idealised conical tree crown, DV = R (tan θ - tan ψ), where R is the crown radius, and θ and ψ are terrain and crown angles respectively. It is shown that errors occur only when terrain angle exceeds the crown angle, with the
DEFF Research Database (Denmark)
Thuiller, Wilfried; Lavorel, Sandra; Sykes, Martin T.
2006-01-01
Rapid anthropogenic climate change is already affecting species distributions and ecosystem functioning worldwide. We applied niche-based models to analyse the impact of climate change on tree species and functional diversity in Europe. Present-day climate was used to predict the distributions...... of 122 tree species from different functional types (FT). We then explored projections of future distributions under one climate scenario for 2080, considering two alternative dispersal assumptions: no dispersal and unlimited dispersal. The species-rich broadleaved deciduous group appeared to play a key...... role in the future of different European regions. Temperate areas were projected to lose both species richness and functional diversity due to the loss of broadleaved deciduous trees. These were projected to migrate to boreal forests, thereby increasing their species richness and functional diversity...
International Nuclear Information System (INIS)
Charlot, Philippe; Marimoutou, Vêlayoudom
2014-01-01
This study examines the volatility and correlation and their relationships among the euro/US dollar exchange rates, the S and P500 equity indices, and the prices of WTI crude oil and the precious metals (gold, silver, and platinum) over the period 2005 to 2012. Our model links the univariate volatilities with the correlations via a hidden stochastic decision tree. The ensuing Hidden Markov Decision Tree (HMDT) model is in fact an extension of the Hidden Markov Model (HMM) introduced by Jordan et al. (1997). The architecture of this model is the opposite that of the classical deterministic approach based on a binary decision tree and, it allows a probabilistic vision of the relationship between univariate volatility and correlation. Our results are categorized into three groups, namely (1) exchange rates and oil, (2) S and P500 indices, and (3) precious metals. A switching dynamics is seen to characterize the volatilities, while, in the case of the correlations, the series switch from one regime to another, this movement touching a peak during the period of the Subprime crisis in the US, and again during the days following the Tohoku earthquake in Japan. Our findings show that the relationships between volatility and correlation are dependent upon the nature of the series considered, sometimes corresponding to those found in econometric studies, according to which correlation increases in bear markets, at other times differing from them. - Highlights: • This study examines the volatility and correlation and their relationships of precious metals and crude oil. • Our model links the univariate volatilities with the correlations via a hidden stochastic decision tree. • This model allows a probabilistic point of view of the relationship between univariate volatility and correlation. • Results show the relationships between volatility and correlation are dependent upon the nature of the series considered
Directory of Open Access Journals (Sweden)
Irwan Budi Santoso
2013-10-01
Full Text Available Langkah pertama dalam membangun model pengenalan Tree-Augmented Network (TAN dengan mengukur besarnya hubungan diantara pasangan fitur objek. Salah satu metode yang dapat digunakan mengukur besarnya keeratan hubungan secara linier diantara pasangan fitur adalah Korelasi Pearson. Aplikasi Korelasi Pearson dalam membangun model Tree-Augmented Network (TAN dalam penelitian ini, akan diujicobakan pada kasus membangun model pengenalan karakter tulisan tangan. Data fitur karakter tulisan tangan untuk kasus ini, diasumsikan mengikuti distribusi gaussian karena estimasi parameter model pengenalannya menggunakan estimator Maximum Likelihood (ML. Hasil eksperimen dengan menggunakan data training yang terdiri dari 5 jenis karakter tulisan tangan, menunjukkan untuk dimensi fitur karakter tulisan tangan 10x30 (30 fitur, akurasi sistem Korelasi Pearson dalam membangun model TAN untuk mengenali karakter tulisan tangan sebesar 88 %.
Aubry-Kientz, Mélaine; Rossi, Vivien; Boreux, Jean-Jacques; Hérault, Bruno
2015-06-01
Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20-year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait-based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.
Sarah Wilkinson; Jerome Ogee; Jean-Christophe Domec; Mark Rayment; Lisa Wingate
2015-01-01
Process-based models that link seasonally varying environmental signals to morphological features within tree rings are essential tools to predict tree growth response and commercially important wood quality traits under future climate scenarios. This study evaluated model portrayal of radial growth and wood anatomy observations within a mature maritime pine (Pinus...
Johnson, D. J.; Needham, J.; Xu, C.; Davies, S. J.; Bunyavejchewin, S.; Giardina, C. P.; Condit, R.; Cordell, S.; Litton, C. M.; Hubbell, S.; Kassim, A. R. B.; Shawn, L. K. Y.; Nasardin, M. B.; Ong, P.; Ostertag, R.; Sack, L.; Tan, S. K. S.; Yap, S.; McDowell, N. G.; McMahon, S.
2016-12-01
Terrestrial carbon cycling is a function of the growth and survival of trees. Current model representations of tree growth and survival at a global scale rely on coarse plant functional traits that are parameterized very generally. In view of the large biodiversity in the tropical forests, it is important that we account for the functional diversity in order to better predict tropical forest responses to future climate changes. Several next generation Earth System Models are moving towards a size-structured, trait-based approach to modelling vegetation globally, but the challenge of which and how many traits are necessary to capture forest complexity remains. Additionally, the challenge of collecting sufficient trait data to describe the vast species richness of tropical forests is enormous. We propose a more fundamental approach to these problems by characterizing forests by their patterns of survival. We expect our approach to distill real-world tree survival into a reasonable number of functional types. Using 10 large-area tropical forest plots that span geographic, edaphic and climatic gradients, we model tree survival as a function of tree size for hundreds of species. We found surprisingly few categories of size-survival functions emerge. This indicates some fundamental strategies at play across diverse forests to constrain the range of possible size-survival functions. Initial cluster analysis indicates that four to eight functional forms are necessary to describe variation in size-survival relations. Temporal variation in size-survival functions can be related to local environmental variation, allowing us to parameterize how demographically similar groups of species respond to perturbations in the ecosystem. We believe this methodology will yield a synthetic approach to classifying forest systems that will greatly reduce uncertainty and complexity in global vegetation models.
Directory of Open Access Journals (Sweden)
Andrew T Tredennick
Full Text Available Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of 'universal' scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and 'global' (i.e. interspecific scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST, Geometric Similarity, and Stress Similarity in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and
Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.
Mørk, Søren; Holmes, Ian
2012-03-01
Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.
Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.
Yaghouby, Farid; Modur, Pradeep; Sunderam, Sridhar
2014-01-01
Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).
Energy Technology Data Exchange (ETDEWEB)
Cardil Forradellas, A.; Molina Terrén, D.M.; Oliveres, J.; Castellnou, M.
2016-07-01
Aim of study: In this study we compare the accuracy of three bivariate distributions: Johnson’s SBB, Weibull-2P and LL-2P functions for characterizing the joint distribution of tree diameters and heights. Area of study: North-West of Spain. Material and methods: Diameter and height measurements of 128 plots of pure and even-aged Tasmanian blue gum (Eucalyptus globulus Labill.) stands located in the North-west of Spain were considered in the present study. The SBB bivariate distribution was obtained from SB marginal distributions using a Normal Copula based on a four-parameter logistic transformation. The Plackett Copula was used to obtain the bivariate models from the Weibull and Logit-logistic univariate marginal distributions. The negative logarithm of the maximum likelihood function was used to compare the results and the Wilcoxon signed-rank test was used to compare the related samples of these logarithms calculated for each sample plot and each distribution. Main results: The best results were obtained by using the Plackett copula and the best marginal distribution was the Logit-logistic. Research highlights: The copulas used in this study have shown a good performance for modeling the joint distribution of tree diameters and heights. They could be easily extended for modelling multivariate distributions involving other tree variables, such as tree volume or biomass. (Author)
International Nuclear Information System (INIS)
Bou Kheir, Rania; Greve, Mogens H.; Abdallah, Chadi; Dalgaard, Tommy
2010-01-01
Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. - GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.
Energy Technology Data Exchange (ETDEWEB)
Bou Kheir, Rania, E-mail: rania.boukheir@agrsci.d [Lebanese University, Faculty of Letters and Human Sciences, Department of Geography, GIS Research Laboratory, P.O. Box 90-1065, Fanar (Lebanon); Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark); Greve, Mogens H. [Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark); Abdallah, Chadi [National Council for Scientific Research, Remote Sensing Center, P.O. Box 11-8281, Beirut (Lebanon); Dalgaard, Tommy [Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele (Denmark)
2010-02-15
Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. - GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.
Directory of Open Access Journals (Sweden)
CHEN Liyan
2018-03-01
Full Text Available Change analysis and detection plays important role in the updating of multi-scale databases.When overlap an updated larger-scale dataset and a to-be-updated smaller-scale dataset,people usually focus on temporal changes caused by the evolution of spatial entities.Little attention is paid to the representation changes influenced by map generalization.Using polygonal building data as an example,this study examines the changes from different perspectives,such as the reasons for their occurrence,their performance format.Based on this knowledge,we employ decision tree in field of machine learning to establish a change detection model.The aim of the proposed model is to distinguish temporal changes that need to be applied as updates to the smaller-scale dataset from representation changes.The proposed method is validated through tests using real-world building data from Guangzhou city.The experimental results show the overall precision of change detection is more than 90%,which indicates our method is effective to identify changed objects.
From the trees to the forest: a review of radiative neutrino mass models
Cai, Yi; Herrero García, Juan; Schmidt, Michael A.; Vicente, Avelino; Volkas, Raymond R.
2017-12-01
A plausible explanation for the lightness of neutrino masses is that neutrinos are massless at tree level, with their mass (typically Majorana) being generated radiatively at one or more loops. The new couplings, together with the suppression coming from the loop factors, imply that the new degrees of freedom cannot be too heavy (they are typically at the TeV scale). Therefore, in these models there are no large mass hierarchies and they can be tested using different searches, making their detailed phenomenological study very appealing. In particular, the new particles can be searched for at colliders and generically induce signals in lepton-flavor and lepton-number violating processes (in the case of Majorana neutrinos), which are not independent from reproducing correctly the neutrino masses and mixings. The main focus of the review is on Majorana neutrinos. We order the allowed theory space from three different perspectives: (i) using an effective operator approach to lepton number violation, (ii) by the number of loops at which the Weinberg operator is generated, (iii) within a given loop order, by the possible irreducible topologies. We also discuss in more detail some popular radiative models which involve qualitatively different features, revisiting their most important phenomenological implications. Finally, we list some promising avenues to pursue.
Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi
2017-09-01
It has been a common idea to produce multiscale segmentations to represent the various geographic objects in high-spatial resolution remote sensing (HR) images. However, it remains a great challenge to automatically select the proper segmentation scale(s) just according to the image information. In this study, we propose a novel way of information fusion at object level by combining hierarchical multiscale segmentations with existed thematic information produced by classification or recognition. The tree Markov random field (T-MRF) model is designed for the multiscale combination framework, through which the object type is determined as close as the existed thematic information. At the same time, the object boundary is jointly determined by the thematic labels and the multiscale segments through the minimization of the energy function. The benefits of the proposed T-MRF combination model include: (1) reducing the dependence of segmentation scale selection when utilizing multiscale segmentations; (2) exploring the hierarchical context naturally imbedded in the multiscale segmentations. The HR images in both urban and rural areas are used in the experiments to show the effectiveness of the proposed combination framework on these two aspects.
Membrane voltage changes in passive dendritic trees: a tapering equivalent cylinder model.
Poznański, R R
1988-01-01
An exponentially tapering equivalent cylinder model is employed in order to approximate the loss of the dendritic trunk parameter observed from anatomical data on apical and basilar dendrites of CA1 and CA3 hippocampal pyramidal neurons. This model allows dendritic trees with a relative paucity of branching to be treated. In particular, terminal branches are not required to end at the same electrotonic distance. The Laplace transform method is used to obtain analytic expressions for the Green's function corresponding to an instantaneous pulse of current injected at a single point along a tapering equivalent cylinder with sealed ends. The time course of the voltage in response to an arbitrary input is computed using the Green's function in a convolution integral. Examples of current input considered are (1) an infinitesimally brief (Dirac delta function) pulse and (2) a step pulse. It is demonstrated that inputs located on a tapering equivalent cylinder are more effective at the soma than identically placed inputs on a nontapering equivalent cylinder. Asymptotic solutions are derived to enable the voltage response behaviour over both relatively short and long time periods to be analysed. Semilogarithmic plots of these solutions provide a basis for estimating the membrane time constant tau m from experimental transients. Transient voltage decrement from a clamped soma reveals that tapering tends to reduce the error associated with inadequate voltage clamping of the dendritic membrane. A formula is derived which shows that tapering tends to increase the estimate of the electrotonic length parameter L.
An observation-based progression modeling approach to spring and autumn deciduous tree phenology
Yu, Rong; Schwartz, Mark D.; Donnelly, Alison; Liang, Liang
2016-03-01
It is important to accurately determine the response of spring and autumn phenology to climate change in forest ecosystems, as phenological variations affect carbon balance, forest productivity, and biodiversity. We observed phenology intensively throughout spring and autumn in a temperate deciduous woodlot at Milwaukee, WI, USA, during 2007-2012. Twenty-four phenophase levels in spring and eight in autumn were recorded for 106 trees, including white ash, basswood, white oak, boxelder, red oak, and hophornbeam. Our phenological progression models revealed that accumulated degree-days and day length explained 87.9-93.4 % of the variation in spring canopy development and 75.8-89.1 % of the variation in autumn senescence. In addition, the timing of community-level spring and autumn phenophases and the length of the growing season from 1871 to 2012 were reconstructed with the models developed. All simulated spring phenophases significantly advanced at a rate from 0.24 to 0.48 days/decade ( p ≤ 0.001) during the 1871-2012 period and from 1.58 to 2.00 days/decade ( p coloration) and 0.50 (full-leaf coloration) days/decade ( p coloration and leaf fall, and suggested accelerating simulated ecosystem responses to climate warming over the last four decades in comparison to the past 142 years.
Directory of Open Access Journals (Sweden)
YU Jintao
2016-09-01
Full Text Available After the characteristics of geodesic active contour model (GAC, Chan-Vese model(CV and local binary fitting model(LBF are analyzed, and the active contour model based on regions and edges is combined with image segmentation method based on quad-tree, a waterline extraction method based on quad-tree and multiple active contour model is proposed in this paper. Firstly, the method provides an initial contour according to quad-tree segmentation. Secondly, a new signed pressure force(SPF function based on global image statistics information of CV model and local image statistics information of LBF model has been defined, and then ,the edge stopping function(ESF is replaced by the proposed SPF function, which solves the problem such as evolution stopped in advance and excessive evolution. Finally, the selective binary and Gaussian filtering level set method is used to avoid reinitializing and regularization to improve the evolution efficiency. The experimental results show that this method can effectively extract the weak edges and serious concave edges, and owns some properties such as sub-pixel accuracy, high efficiency and reliability for waterline extraction.
Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne
2018-01-01
Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology—light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limite...
Ay, Jean-Sauveur; Guillemot, Joannès; Martin-StPaul, Nicolas K.; Doyen, Luc; Leadley, Paul
2015-04-01
Species distribution models (SDMs) are widely used to study and predict the outcome of global change on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of application on forested trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8 km). We also compared the output of the SSDM with outputs of a classical SDM in term of bioclimatic response curves and potential distribution under current climate. According to the species and the spatial resolution of the calibration dataset, shapes of bioclimatic response curves the modelled species distribution maps differed markedly between the SSDM and classical SDMs. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents a crucial step to account for economic constraints on tree
Evans, M. E.; Merow, C.; Record, S.; Menlove, J.; Gray, A.; Cundiff, J.; McMahon, S.; Enquist, B. J.
2013-12-01
Current attempts to forecast how species' distributions will change in response to climate change suffer under a fundamental trade-off: between modeling many species superficially vs. few species in detail (between correlative vs. mechanistic models). The goals of this talk are two-fold: first, we present a Bayesian multilevel modeling framework, dynamic range modeling (DRM), for building process-based forecasts of many species' distributions at a time, designed to address the trade-off between detail and number of distribution forecasts. In contrast to 'species distribution modeling' or 'niche modeling', which uses only species' occurrence data and environmental data, DRMs draw upon demographic data, abundance data, trait data, occurrence data, and GIS layers of climate in a single framework to account for two processes known to influence range dynamics - demography and dispersal. The vision is to use extensive databases on plant demography, distributions, and traits - in the Botanical Information and Ecology Network, the Forest Inventory and Analysis database (FIA), and the International Tree Ring Data Bank - to develop DRMs for North American trees. Second, we present preliminary results from building the core submodel of a DRM - an integral projection model (IPM) - for a sample of dominant tree species in western North America. IPMs are used to infer demographic niches - i.e., the set of environmental conditions under which population growth rate is positive - and project population dynamics through time. Based on >550,000 data points derived from FIA for nine tree species in western North America, we show IPM-based models of their current and future distributions, and discuss how IPMs can be used to forecast future forest productivity, mortality patterns, and inform efforts at assisted migration.
FireStem2D A two-dimensional heat transfer model for simulating tree stem injury in fires
Efthalia K. Chatziefstratiou; Gil Bohrer; Anthony S. Bova; Ravishankar Subramanian; Renato P.M. Frasson; Amy Scherzer; Bret W. Butler; Matthew B. Dickinson
2013-01-01
FireStem2D, a software tool for predicting tree stem heating and injury in forest fires, is a physically-based, two-dimensional model of stem thermodynamics that results from heating at the bark surface. It builds on an earlier one-dimensional model (FireStem) and provides improved capabilities for predicting fire-induced mortality and injury before a fire occurs by...
Dean, Christopher; Kirkpatrick, Jamie B; Osborn, Jon; Doyle, Richard B; Fitzgerald, Nicholas B; Roxburgh, Stephen H
2018-03-01
There is high uncertainty in the contribution of land-use change to anthropogenic climate change, especially pertaining to below-ground carbon loss resulting from conversion of primary-to-secondary forest. Soil organic carbon (SOC) and coarse roots are concentrated close to tree trunks, a region usually unmeasured during soil carbon sampling. Soil carbon estimates and their variation with land-use change have not been correspondingly adjusted. Our aim was to deduce allometric equations that will allow improvement of SOC estimates and tree trunk carbon estimates, for primary forest stands that include large trees in rugged terrain. Terrestrial digital photography, photogrammetry and GIS software were used to produce 3D models of the buttresses, roots and humus mounds of large trees in primary forests dominated by Eucalyptus regnans in Tasmania. Models of 29, in situ eucalypts were made and analysed. 3D models of example eucalypt roots, logging debris, rainforest tree species, fallen trees, branches, root and trunk slices, and soil profiles were also derived. Measurements in 2D, from earlier work, of three buttress 'logs' were added to the data set. The 3D models had high spatial resolution. The modelling allowed checking and correction of field measurements. Tree anatomical detail was formulated, such as buttress shape, humus volume, root volume in the under-sampled zone and trunk hollow area. The allometric relationships developed link diameter at breast height and ground slope, to SOC and tree trunk carbon, the latter including a correction for senescence. These formulae can be applied to stand-level carbon accounting. The formulae allow the typically measured, inter-tree SOC to be corrected for not sampling near large trees. The 3D models developed are irreplaceable, being for increasingly rare, large trees, and they could be useful to other scientific endeavours.
Directory of Open Access Journals (Sweden)
Aguiar Fabio S
2012-08-01
Full Text Available Abstract Background Tuberculosis (TB remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART model was generated and validated. The area under the ROC curve (AUC, sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with
Jeffrey E. Schneiderman; Hong S. He; Frank R. Thompson; William D. Dijak; Jacob S. Fraser
2015-01-01
Tree species distribution and abundance are affected by forces operating across a hierarchy of ecological scales. Process and species distribution models have been developed emphasizing forces at different scales. Understanding model agreement across hierarchical scales provides perspective on prediction uncertainty and ultimately enables policy makers and managers to...
Refining discordant gene trees.
Górecki, Pawel; Eulenstein, Oliver
2014-01-01
Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism. We propose a natural extension of the well-established costs for comparing unrooted and non-binary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the Robinson-Foulds costs. Our binary refinement of Robinson-Foulds, gene duplication, and deep coalescence costs for unrooted and non-binary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.
Ishida, Takuya; Tayasu, Ichiro; Takenaka, Chisato
2015-11-01
Quantification of sulfur (S) deposition is critical to deciphering the environmental archive of S in terrestrial ecosystems. Here we propose a mixing model that quantifies S deposition based on the S isotope ratio (δS) in tree rings. We collected samples from Japanese cedar ( D. Don) stumps from two sites: one near Yokkaichi City (YOK), which is well known for having the heaviest S air pollution in the world, and one at Inabu-cho (INA) in central Japan, which has been much less affected by air pollution. The δS profiles at both sites are consistent with S air pollution and contributions of anthropogenic S. The minimum value in YOK is lower than the δS values of anthropogenic S or any other possible source. Because the δS in the tree rings is affected by fractionation in the forest ecosystems, we used a mixing model to account for the isotope effects and to distinguish the sources of S. Based on the model results, we infer that the peak of S emissions at YOK occurred sometime between the late 1960s and early 1970s (489 mmol m yr). This estimated value is comparable with the highest reported values in Europe. This is the first quantitative estimate of anthropogenic input of S in forest systems based on δS in tree rings. Our results suggest that tree ring data can be used when monitoring stations of atmospheric S are lacking and that estimates of S deposition using δS in tree rings will advance our understanding of the local-scale S dynamics and the effect of human activities on it. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
McTavish, Emily Jane; Steel, Mike; Holder, Mark T
2015-12-01
Statistically consistent estimation of phylogenetic trees or gene trees is possible if pairwise sequence dissimilarities can be converted to a set of distances that are proportional to the true evolutionary distances. Susko et al. (2004) reported some strikingly broad results about the forms of inconsistency in tree estimation that can arise if corrected distances are not proportional to the true distances. They showed that if the corrected distance is a concave function of the true distance, then inconsistency due to long branch attraction will occur. If these functions are convex, then two "long branch repulsion" trees will be preferred over the true tree - though these two incorrect trees are expected to be tied as the preferred true. Here we extend their results, and demonstrate the existence of a tree shape (which we refer to as a "twisted Farris-zone" tree) for which a single incorrect tree topology will be guaranteed to be preferred if the corrected distance function is convex. We also report that the standard practice of treating gaps in sequence alignments as missing data is sufficient to produce non-linear corrected distance functions if the substitution process is not independent of the insertion/deletion process. Taken together, these results imply inconsistent tree inference under mild conditions. For example, if some positions in a sequence are constrained to be free of substitutions and insertion/deletion events while the remaining sites evolve with independent substitutions and insertion/deletion events, then the distances obtained by treating gaps as missing data can support an incorrect tree topology even given an unlimited amount of data. Copyright © 2015 Elsevier Inc. All rights reserved.
Study and ranking of determinants of Taenia solium infections by classification tree models.
Mwape, Kabemba E; Phiri, Isaac K; Praet, Nicolas; Dorny, Pierre; Muma, John B; Zulu, Gideon; Speybroeck, Niko; Gabriël, Sarah
2015-01-01
Taenia solium taeniasis/cysticercosis is an important public health problem occurring mainly in developing countries. This work aimed to study the determinants of human T. solium infections in the Eastern province of Zambia and rank them in order of importance. A household (HH)-level questionnaire was administered to 680 HHs from 53 villages in two rural districts and the taeniasis and cysticercosis status determined. A classification tree model (CART) was used to define the relative importance and interactions between different predictor variables in their effect on taeniasis and cysticercosis. The Katete study area had a significantly higher taeniasis and cysticercosis prevalence than the Petauke area. The CART analysis for Katete showed that the most important determinant for cysticercosis infections was the number of HH inhabitants (6 to 10) and for taeniasis was the number of HH inhabitants > 6. The most important determinant in Petauke for cysticercosis was the age of head of household > 32 years and for taeniasis it was age taeniasis and cysticercosis infections was the number of HH inhabitants (6 to 10) in Katete district and age in Petauke. The results suggest that control measures should target HHs with a high number of inhabitants and older individuals. © The American Society of Tropical Medicine and Hygiene.
L. Linsen; B.J. Karis; E.G. McPherson; B. Hamann
2005-01-01
In computer graphics, models describing the fractal branching structure of trees typically exploit the modularity of tree structures. The models are based on local production rules, which are applied iteratively and simultaneously to create a complex branching system. The objective is to generate three-dimensional scenes of often many realistic- looking and non-...
Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica
2017-09-01
Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.
A kingdom-specific protein domain HMM library for improved annotation of fungal genomes
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Oliver Stephen G
2007-04-01
Full Text Available Abstract Background Pfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs, which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models. Results Here we present a general approach to create domain libraries of HMMs for sub-taxa of a kingdom. Taking fungal species as an example, we construct a domain library of HMMs (called Fungal Pfam or FPfam using sequences from 30 genomes, consisting of 24 species from the ascomycetes group and two basidiomycetes, Ustilago maydis, a fungal pathogen of maize, and the white rot fungus Phanerochaete chrysosporium. In addition, we include the Microsporidion Encephalitozoon cuniculi, an obligate intracellular parasite, and two non-fungal species, the oomycetes Phytophthora sojae and Phytophthora ramorum, both plant pathogens. We evaluate the performance in terms of coverage against the original 30 genomes used in training FPfam and against five more recently sequenced fungal genomes that can be considered as an independent test set. We show that kingdom-specific models such as FPfam can find instances of both novel and well characterized domains, increases overall coverage and detects more domains per sequence with typically higher bitscores than Pfam for the same domain families. An evaluation of the effect of changing E-values on the coverage shows that the performance of FPfam is consistent over the range of E-values applied. Conclusion Kingdom-specific models are shown to provide improved coverage. However, as the models become more specific, some sequences found by Pfam may be missed by the models in FPfam and some of the families represented in the test set are not present in FPfam
Cook, E. R.
2007-05-01
The North American Drought Atlas describes a detailed reconstruction of drought variability from tree rings over most of North America for the past 500-1000 years. The first version of it, produced over three years ago, was based on a network of 835 tree-ring chronologies and a 286-point grid of instrumental Palmer Drought Severity Indices (PDSI). These gridded PDSI reconstructions have been used in numerous published studies now that range from modeling fire in the American West, to the impact of drought on palaeo-Indian societies, and to the determination of the primary causes of drought over North America through climate modeling experiments. Some examples of these applications will be described to illustrate the scientific value of these large-scale reconstructions of drought. Since the development and free public release of Version 1 of the North American Drought Atlas (see http:iridl.ldeo.columbia.edu/SOURCES/.LDEO/.TRL/.NADA2004/.pdsi-atlas.html), great improvements have been made in the critical tree-ring network used to reconstruct PDSI at each grid point. This network has now been enlarged to 1743 annual tree-ring chronologies, which greatly improves the density of tree-ring records in certain parts of the grid, especially in Canada and Mexico. In addition, the number of tree-ring records that extend back before AD 1400 has been substantially increased. These developments justify the creation of Version 2 of the North American Drought Atlas. In this talk I will describe this new version of the drought atlas and some of its properties that make it a significant improvement over the previous version. The new product provides enhanced resolution of the spatial and temporal variability of prolonged drought such as the late 16th century event that impacted regions of both Mexico and the United States. I will also argue for the North American Drought Atlas being used as a template for the development of large-scale drought reconstructions in other land areas of
Xue, Yang; Yang, Zhongyang; Wang, Xiaoyan; Lin, Zhipan; Li, Dunxi; Su, Shaofeng
2016-01-01
Casuarina equisetifolia is commonly planted and used in the construction of coastal shelterbelt protection in Hainan Island. Thus, it is critical to accurately estimate the tree biomass of Casuarina equisetifolia L. for forest managers to evaluate the biomass stock in Hainan. The data for this work consisted of 72 trees, which were divided into three age groups: young forest, middle-aged forest, and mature forest. The proportion of biomass from the trunk significantly increased with age (Pbiomass of the branch and leaf decreased, and the biomass of the root did not change. To test whether the crown radius (CR) can improve biomass estimates of C. equisetifolia, we introduced CR into the biomass models. Here, six models were used to estimate the biomass of each component, including the trunk, the branch, the leaf, and the root. In each group, we selected one model among these six models for each component. The results showed that including the CR greatly improved the model performance and reduced the error, especially for the young and mature forests. In addition, to ensure biomass additivity, the selected equation for each component was fitted as a system of equations using seemingly unrelated regression (SUR). The SUR method not only gave efficient and accurate estimates but also achieved the logical additivity. The results in this study provide a robust estimation of tree biomass components and total biomass over three groups of C. equisetifolia.
Xue, Yang; Yang, Zhongyang; Wang, Xiaoyan; Lin, Zhipan; Li, Dunxi; Su, Shaofeng
2016-01-01
Casuarina equisetifolia is commonly planted and used in the construction of coastal shelterbelt protection in Hainan Island. Thus, it is critical to accurately estimate the tree biomass of Casuarina equisetifolia L. for forest managers to evaluate the biomass stock in Hainan. The data for this work consisted of 72 trees, which were divided into three age groups: young forest, middle-aged forest, and mature forest. The proportion of biomass from the trunk significantly increased with age (Pbiomass of the branch and leaf decreased, and the biomass of the root did not change. To test whether the crown radius (CR) can improve biomass estimates of C. equisetifolia, we introduced CR into the biomass models. Here, six models were used to estimate the biomass of each component, including the trunk, the branch, the leaf, and the root. In each group, we selected one model among these six models for each component. The results showed that including the CR greatly improved the model performance and reduced the error, especially for the young and mature forests. In addition, to ensure biomass additivity, the selected equation for each component was fitted as a system of equations using seemingly unrelated regression (SUR). The SUR method not only gave efficient and accurate estimates but also achieved the logical additivity. The results in this study provide a robust estimation of tree biomass components and total biomass over three groups of C. equisetifolia. PMID:27002822
An introduction to tree-structured modeling with application to quality of life data.
Su, Xiaogang; Azuero, Andres; Cho, June; Kvale, Elizabeth; Meneses, Karen M; McNees, M Patrick
2011-01-01
Investigators addressing nursing research are faced increasingly with the need to analyze data that involve variables of mixed types and are characterized by complex nonlinearity and interactions. Tree-based methods, also called recursive partitioning, are gaining popularity in various fields. In addition to efficiency and flexibility in handling multifaceted data, tree-based methods offer ease of interpretation. The aims of this study were to introduce tree-based methods, discuss their advantages and pitfalls in application, and describe their potential use in nursing research. In this article, (a) an introduction to tree-structured methods is presented, (b) the technique is illustrated via quality of life (QOL) data collected in the Breast Cancer Education Intervention study, and (c) implications for their potential use in nursing research are discussed. As illustrated by the QOL analysis example, tree methods generate interesting and easily understood findings that cannot be uncovered via traditional linear regression analysis. The expanding breadth and complexity of nursing research may entail the use of new tools to improve efficiency and gain new insights. In certain situations, tree-based methods offer an attractive approach that help address such needs.
Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu
2015-01-01
A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.
Letsch, Harald O; Kjer, Karl M
2011-05-27
Failure to account for covariation patterns in helical regions of ribosomal RNA (rRNA) genes has the potential to misdirect the estimation of the phylogenetic signal of the data. Furthermore, the extremes of length variation among taxa, combined with regional substitution rate variation can mislead the alignment of rRNA sequences and thus distort subsequent tree reconstructions. However, recent developments in phylogenetic methodology now allow a comprehensive integration of secondary structures in alignment and tree reconstruction analyses based on rRNA sequences, which has been shown to correct some of these problems. Here, we explore the potentials of RNA substitution models and the interactions of specific model setups with the inherent pattern of covariation in rRNA stems and substitution rate variation among loop regions. We found an explicit impact of RNA substitution models on tree reconstruction analyses. The application of specific RNA models in tree reconstructions is hampered by interaction between the appropriate modelling of covarying sites in stem regions, and excessive homoplasy in some loop regions. RNA models often failed to recover reasonable trees when single-stranded regions are excessively homoplastic, because these regions contribute a greater proportion of the data when covarying sites are essentially downweighted. In this context, the RNA6A model outperformed all other models, including the more parametrized RNA7 and RNA16 models. Our results depict a trade-off between increased accuracy in estimation of interdependencies in helical regions with the risk of magnifying positions lacking phylogenetic signal. We can therefore conclude that caution is warranted when applying rRNA covariation models, and suggest that loop regions be independently screened for phylogenetic signal, and eliminated when they are indistinguishable from random noise. In addition to covariation and homoplasy, other factors, like non-stationarity of substitution rates
Mexican drought: an observational modeling and tree ring study of variability and climate change
Energy Technology Data Exchange (ETDEWEB)
Seager, R.; Ting, M. [Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY (United States)]. E-mail: seager@ldeo.columbia.edu; Davis, M. [Department of History, University of California at Irvine, CA (United States); Cane, M.; Naik, N.; Nakamura, J.; Li, C.; Cook, E. [Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY (United States); Stahle, D.W. [Tree Ring Laboratory, University of Arkansas, Fayetteville, Arkansas (United States)
2009-01-15
Variability of Mexican hydroclimate, with special attention to persistent drought, is examined using observations, model simulations forced by historical sea surface temperature (SST), tree ring reconstructions of past climate and model simulations and projections of naturally and anthropogenically forced climate change. During the winter half year, hydroclimate across Mexico is influenced by the state of the tropical Pacific Ocean with the Atlantic playing little role. Mexican winters tend to be wetter during El Nino conditions. In the summer half year northern Mexico is also wetter when El Nino conditions prevail, but southern Mexico is drier. A warm tropical North Atlantic Ocean makes northern Mexico dry and southern Mexico wet. These relationships are reasonably well reproduced in ensembles of atmosphere model simulations forced by historical SST for the period from 1856 to 2002. Large ensembles of 100 day long integrations are used to examine the day to day evolution of the atmospheric circulation and precipitation in response to a sudden imposition of a El Nino SST anomaly in the summer half year. Kelvin waves propagate east and immediately cause increased column-integrated moisture divergence and reduced precipitation over the tropical Americas and Intra-America Seas. Within a few days a low level high pressure anomaly develops over the Gulf of Mexico. A forced nonlinear model is used to demonstrate that this low is forced by the reduced atmospheric heating over the tropical Atlantic-Intra-America Seas area. Tree ring reconstructions that extend back before the period of instrumental precipitation data coverage are used to verify long model simulations forced by historical SST. The early to mid 1950s drought in northern Mexico appears to have been the most severe since the mid nineteenth century and likely arose as a response to both a multiyear La Nina and a warm tropical North Atlantic. A drought in the 1890s was also severe and appears driven by a
Directory of Open Access Journals (Sweden)
Ser Javier Del
2005-01-01
Full Text Available We consider the case of two correlated sources, and . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source over an additive white Gaussian noise (AWGN channel when the output of the other source is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting.
Wöllmer, Martin; Marchi, Erik; Squartini, Stefano; Schuller, Björn
2011-09-01
Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database-a corpus containing emotionally colored conversations with a cognitive system for "Sensitive Artificial Listening".
histoneHMM: Differential analysis of histone modifications with broad genomic footprints
Czech Academy of Sciences Publication Activity Database
Heinig, M.; Colomé-Tatché, M.; Taudt, A.; Rintisch, C.; Schafer, S.; Pravenec, Michal; Hubner, N.; Vingron, M.; Johannes, F.
2015-01-01
Roč. 16, Feb 22 (2015), s. 60 ISSN 1471-2105 R&D Projects: GA MŠk(CZ) 7E10067; GA ČR(CZ) GA13-04420S Institutional support: RVO:67985823 Keywords : ChIP - seq * histone modifications * Hidden Markov model * computational biology * differential analysis Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.435, year: 2015
Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong
2014-01-01
Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.
One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.
Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios
2016-05-10
This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.
Modeling the long-term effects of introduced herbivores on the spread of an invasive tree
Zhang, Bo; DeAngelis, Donald L.; Rayamajhi, Min B.; Botkin, Daniel B.
2017-01-01
ContextMelaleuca quinquenervia (Cav.) Blake (hereafter melaleuca) is an invasive tree from Australia that has spread over the freshwater ecosystems of southern Florida, displacing native vegetation, thus threatening native biodiversity. Suppression of melaleuca appears to be progressing through the introduction of insect species, the weevil, Oxiops vitiosa, and the psyllid, Boreioglycaspis melaleucae.ObjectiveTo improve understanding of the possible effects of herbivory on the landscape dynamics of melaleuca in native southern Florida plant communities.MethodsWe projected likely future changes in plant communities using the individual based modeling platform, JABOWA-II, by simulating successional processes occurring in two types of southern Florida habitat, cypress swamp and bay swamp, occupied by native species and melaleuca, with the impact of insect herbivores.ResultsComputer simulations show melaleuca invasion leads to decreases in density and basal area of native species, but herbivory would effectively control melaleuca to low levels, resulting in a recovery of native species. When herbivory was modeled on pure melaleuca stands, it was more effective in stands with initially larger-sized melaleuca. Although the simulated herbivory did not eliminate melaleuca, it decreased its presence dramatically in all cases, supporting the long-term effectiveness of herbivory in controlling melaleuca invasion.ConclusionsThe results provide three conclusions relevant to management: (1) The introduction of insect herbivory that has been applied to melaleuca appears sufficient to suppress melaleuca over the long term, (2) dominant native species may recover in about 50 years, and (3) regrowth of native species will further suppress melaleuca through competition.
Application Of Decision Tree Approach To Student Selection Model- A Case Study
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.
Directory of Open Access Journals (Sweden)
Rohulla Kosari Langari
2014-02-01
Full Text Available Change the world through information technology and Internet development, has created competitive knowledge in the field of electronic commerce, lead to increasing in competitive potential among organizations. In this condition The increasing rate of commercial deals developing guaranteed with speed and light quality is due to provide dynamic system of electronic banking until by using modern technology to facilitate electronic business process. Internet banking is enumerate as a potential opportunity the fundamental pillars and determinates of e-banking that in cyber space has been faced with various obstacles and threats. One of this challenge is complete uncertainty in security guarantee of financial transactions also exist of suspicious and unusual behavior with mail fraud for financial abuse. Now various systems because of intelligence mechanical methods and data mining technique has been designed for fraud detection in users’ behaviors and applied in various industrial such as insurance, medicine and banking. Main of article has been recognizing of unusual users behaviors in e-banking system. Therefore, detection behavior user and categories of emerged patterns to paper the conditions for predicting unauthorized penetration and detection of suspicious behavior. Since detection behavior user in internet system has been uncertainty and records of transactions can be useful to understand these movement and therefore among machine method, decision tree technique is considered common tool for classification and prediction, therefore in this research at first has determinate banking effective variable and weight of everything in internet behaviors production and in continuation combining of various behaviors manner draw out such as the model of inductive rules to provide ability recognizing of different behaviors. At least trend of four algorithm Chaid, ex_Chaid, C4.5, C5.0 has compared and evaluated for classification and detection of exist
A classification model of Hyperion image base on SAM combined decision tree
Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin
2009-10-01
Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model
International Nuclear Information System (INIS)
Shi Chunsheng; Meng Dapeng
2011-01-01
The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)
Ogée, J.; Barbour, M. M.; Wingate, L.; Bert, D.; Bosc, A.; Stievenard, M.; Lambrot, C.; Pierre, M.; Bariac, T.; Dewar, R. C.
2009-04-01
High-resolution intra-annual measurements of the carbon and oxygen stable isotope composition of cellulose in annual tree rings (δ13Ccellulose and δ18Ocellulose, respectively) reveal well-defined seasonal patterns that could contain valuable records of past climate and tree function. Interpreting these signals is nonetheless complex because they not only record the signature of current assimilates, but also depend on carbon allocation dynamics within the trees. Here, we present a simple, single-substrate model for wood growth containing only 12 main parameters. The model is used to interpret an isotopic intra-annual chronology collected in an even-aged maritime pine plantation growing in the South-West of France, where climate, soil and flux variables were also monitored. The empirical δ13Ccellulose and δ18Ocellulose exhibit dynamic seasonal patterns, with clear differences between years and individuals, that are mostly captured by the model. In particular, the amplitude of both signals is reproduced satisfactorily as well as the sharp 18O enrichment at the beginning of 1997 and the less pronounced 13C and 18O depletion observed at the end of the latewood. Our results suggest that the single-substrate hypothesis is a good approximation for tree ring studies on Pinus pinaster, at least for the environmental conditions covered by this study. A sensitivity analysis revealed that, in the early wood, the model was particularly sensitive to the date when cell wall thickening begins (twt). We therefore propose to use the model to reconstruct time series of twt and explore how climate influences this key parameter of xylogenesis.
A model analysis of climate and CO2 controls on tree growth in a semi-arid woodland
Li, G.; Harrison, S. P.; Prentice, I. C.
2015-03-01
We used a light-use efficiency model of photosynthesis coupled with a dynamic carbon allocation and tree-growth model to simulate annual growth of the gymnosperm Callitris columellaris in the semi-arid Great Western Woodlands, Western Australia, over the past 100 years. Parameter values were derived from independent observations except for sapwood specific respiration rate, fine-root turnover time, fine-root specific respiration rate and the ratio of fine-root mass to foliage area, which were estimated by Bayesian optimization. The model reproduced the general pattern of interannual variability in radial growth (tree-ring width), including the response to the shift in precipitation regimes that occurred in the 1960s. Simulated and observed responses to climate were consistent. Both showed a significant positive response of tree-ring width to total photosynthetically active radiation received and to the ratio of modeled actual to equilibrium evapotranspiration, and a significant negative response to vapour pressure deficit. However, the simulations showed an enhancement of radial growth in response to increasing atmospheric CO2 concentration (ppm) ([CO2]) during recent decades that is not present in the observations. The discrepancy disappeared when the model was recalibrated on successive 30-year windows. Then the ratio of fine-root mass to foliage area increases by 14% (from 0.127 to 0.144 kg C m-2) as [CO2] increased while the other three estimated parameters remained constant. The absence of a signal of increasing [CO2] has been noted in many tree-ring records, despite the enhancement of photosynthetic rates and water-use efficiency resulting from increasing [CO2]. Our simulations suggest that this behaviour could be explained as a consequence of a shift towards below-ground carbon allocation.
Tree-based flood damage modeling of companies: Damage processes and model performance
Sieg, Tobias; Vogel, Kristin; Merz, Bruno; Kreibich, Heidi
2017-07-01
Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.
CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information
DEFF Research Database (Denmark)
Bartoli, Lisa; Fariselli, Piero; Krogh, Anders
2009-01-01
tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods. RESULTS: In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence...... alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches...
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
H. Othman
2007-02-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
Othman H
2007-01-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
International Nuclear Information System (INIS)
Das, Shiva K.; Zhou Sumin; Zhang, Junan; Yin, F.-F.; Dewhirst, Mark W.; Marks, Lawrence B.
2007-01-01
Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials: The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a 'boosting' process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results: The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation
Zhang, Ling Yu; Liu, Zhao Gang
2017-12-01
Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stabi