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Journal articles on the topic 'TreeNet'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Changpetch, Pannapa. "Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset." Symmetry 13, no. 2 (February 8, 2021): 287. http://dx.doi.org/10.3390/sym13020287.

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A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.

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Lawry, D., G. M. Moore, and D. Peacock. "The TREENET Avenues of Honour Project 1915-2015." Acta Horticulturae, no. 1108 (February 2016): 31–40. http://dx.doi.org/10.17660/actahortic.2016.1108.4.

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3

Abdi, Omid. "Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis." Sensors 19, no. 18 (September 14, 2019): 3965. http://dx.doi.org/10.3390/s19183965.

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Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

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Changpetch, Pannapa. "Gambling Consumers in Thailand." Asian Social Science 13, no. 5 (April 19, 2017): 136. http://dx.doi.org/10.5539/ass.v13n5p136.

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This paper presents a study of household gambling consumption in Thailand in 2011. We investigate the nonlinear relationships between this behavior and household alcohol expenditure, household gambling expenditure, and demographic factors. We use Treenet to analyze datasets drawn from a socio-economic survey of 42,083 Thai households conducted in 2011. The results show that the five most significant variables in order of importance for predicting the likelihood of household gambling consumption are household income, household region, work status of the household head, religion of the household head, and age of the household head. In summary, the Treenet results suggest that the likelihood of gambling consumption was higher for households with an income of more than 25,000 Bahts per year, a location in the North, a Buddhist head of household, a head with active work status, a head between 35 and 55 years old, with household expenditure spent on alcohol consumed at home of more than 500 Bahts, with household expenditure spent on tobacco of more than 100 Bahts, and a head of household with less education.

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Padmaja, B., V. V. Rama Prasad, and K. V. N. Sunitha. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 446. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp446-452.

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Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.

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6

Padmaja, B., V. V. Rama Prasad, and K. V. N. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 148. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp148-154.

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<p>Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.<em></em></p>

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7

Steinberg, Dan, Nicholas C. Cardell, John Ries, and Mykhaylyo Golovnya. "Using TreeNet to Cross-sell Home Loans to Credit Card Holders." International Journal of Data Warehousing and Mining 4, no. 2 (April 2008): 32–45. http://dx.doi.org/10.4018/jdwm.2008040105.

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8

Mohanty, Ramakanta, V. Ravi, and M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability." International Journal of Applied Evolutionary Computation 1, no. 3 (July 2010): 70–86. http://dx.doi.org/10.4018/jaec.2010070104.

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In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

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9

Tang, Yanchao, Tong Zhao, Nian Huang, Wanfu Lin, Zhiying Luo, and Changquan Ling. "Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach." Evidence-Based Complementary and Alternative Medicine 2019 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/1686205.

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Objective. In order to find the predictive indexes for metabolic syndrome (MS), a data mining method was used to identify significant physiological indexes and traditional Chinese medicine (TCM) constitutions. Methods. The annual health check-up data including physical examination data; biochemical tests and Constitution in Chinese Medicine Questionnaire (CCMQ) measurement data from 2014 to 2016 were screened according to the inclusion and exclusion criteria. A predictive matrix was established by the longitudinal data of three consecutive years. TreeNet machine learning algorithm was applied to build prediction model to uncover the dependence relationship between physiological indexes, TCM constitutions, and MS. Results. By model testing, the overall accuracy rate for prediction model by TreeNet was 73.23%. Top 12.31% individuals in test group (n=325) that have higher probability of having MS covered 23.68% MS patients, showing 0.92 times more risk of having MS than the general population. Importance of ranked top 15 was listed in descending order . The top 5 variables of great importance in MS prediction were TBIL difference between 2014 and 2015 (D_TBIL), TBIL in 2014 (TBIL 2014), LDL-C difference between 2014 and 2015 (D_LDL-C), CCMQ scores for balanced constitution in 2015 (balanced constitution 2015), and TCH in 2015 (TCH 2015). When D_TBIL was between 0 and 2, TBIL 2014 was between 10 and 15, D_LDL-C was above 19, balanced constitution 2015 was below 60, or TCH 2015 was above 5.7, the incidence of MS was higher. Furthermore, there were interactions between balanced constitution 2015 score and TBIL 2014 or D_LDL-C in MS prediction. Conclusion. Balanced constitution, TBIL, LDL-C, and TCH level can act as predictors for MS. The combination of TCM constitution and physiological indexes can give early warning to MS.

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Muchira, James M., Philimon N. Gona, Suzanne Leveille, and Laura L. Hayman. "A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening." Journal of Nursing Measurement 27, no. 3 (December 1, 2019): 433–57. http://dx.doi.org/10.1891/1061-3749.27.3.433.

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Background and PurposeDiagnostic algorithms are invaluable tools for screening diabetes. This review aimed to evaluate and identify the most robust methodological approaches for developing diagnostic algorithms for screening diabetes.MethodsFollowing a literature search, methodological quality of algorithm development studies was evaluated using the TRIPOD guidelines (Collins, Reitsma, Altman, & Moons, 2015).ResultsMethods used for developing the algorithms included logistic regression models, classification and regression trees, Random Forest and TreeNet, Artificial Neural Networks, and Naïve Bayes. Methodological issues for algorithm development studies were related to handling of missing values, reporting recruitment methods, categorization of continuous variables, and statistical controls.ConclusionsMost studies exhibited critical methodological flaws and poor adherence to reporting standards. Diabetes screening algorithms can easily be availed electronically and utilized by nurses at minimal cost even in underserved areas.

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Shapovalova, Svitlana, and Yurii Moskalenko. "METHODS FOR INCREASING THE CLASSIFICATION ACCURACY BASED ON MODIFICATIONS OF THE BASIC ARCHITECTURE OF CONVOLUTIONAL NEURAL NETWORKS." ScienceRise, no. 6 (December 30, 2020): 10–16. http://dx.doi.org/10.21303/2313-8416.2020.001550.

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Object of research: basic architectures of deep learning neural networks. Investigated problem: insufficient accuracy of solving the classification problem based on the basic architectures of deep learning neural networks. An increase in accuracy requires a significant complication of the architecture, which, in turn, leads to an increase in the required computing resources, as well as the consumption of video memory and the cost of learning/output time. Therefore, the problem arises of determining such methods for modifying basic architectures that improve the classification accuracy and require insignificant additional computing resources. Main scientific results: based on the analysis of existing methods for improving the classification accuracy on the convolutional networks of basic architectures, it is determined what is most effective: scaling the ScanNet architecture, learning the ensemble of TreeNet models, integrating several CBNet backbone networks. For computational experiments, these modifications of the basic architectures are implemented, as well as their combinations: ScanNet + TreeNet, ScanNet + CBNet. The effectiveness of these methods in comparison with basic architectures has been proven when solving the problem of recognizing malignant tumors with diagnostic images – SIIM-ISIC Melanoma Classification, the train/test set of which is presented on the Kaggle platform. The accuracy value for the area under the ROC curve metric has increased from 0.94489 (basic architecture network) to 0.96317 (network with ScanNet + CBNet modifications). At the same time, the output compared to the basic architecture (EfficientNet-b5) increased from 440 to 490 seconds, and the consumption of video memory increased from 8 to 9.2 gigabytes, which is acceptable. Innovative technological product: methods for achieving high recognition accuracy from a diagnostic signal based on deep learning neural networks of basic architectures. Scope of application of the innovative technological product: automatic diagnostics systems in the following areas: medicine, seismology, astronomy (classification by images) onboard control systems and systems for monitoring transport and vehicle flows or visitors (recognition of scenes with camera frames).

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12

Johnson, T., D. Lawry, and H. Sapdhare. "The Council verge as the next wetland: TREENET and the cities of Mitcham and Salisbury investigate." Acta Horticulturae, no. 1108 (February 2016): 63–70. http://dx.doi.org/10.17660/actahortic.2016.1108.8.

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Ravi, V., NIKUNJ J. CHAUHAN, and N. RAJ KIRAN. "SOFTWARE RELIABILITY PREDICTION USING INTELLIGENT TECHNIQUES: APPLICATION TO OPERATIONAL RISK PREDICTION IN FIRMS." International Journal of Computational Intelligence and Applications 08, no. 02 (June 2009): 181–94. http://dx.doi.org/10.1142/s1469026809002588.

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In this paper, operational risk arising from the technological dimension is effectively modeled by efficiently forecasting software reliability. We propose the use of wavelet neural networks (WNN) to predict software reliability. Two kinds of wavelets were employed in WNN as transfer functions, viz. Morlet wavelet and Gaussian wavelet, thus giving rise to two variants of WNN. The effectiveness of WNN is demonstrated on a data set taken from literature. Its performance is compared with that of multiple linear regression, multivariate adaptive regression splines, back propagation trained neural network, threshold-accepting trained neural network, threshold accepting trained wavelet neural network, pi-sigma network, general regression neural network, dynamic evolving neuro-fuzzy inference system and TreeNet in terms of normalized root mean square error obtained on test data. Based on the experiments performed, it is observed that the WNN-based models outperformed all the other techniques.

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14

Changpetch, Pannapa, and Dominique Haughton. "Alcohol Consumption and Age in Thailand from 2006 to 2011: An Exploration of Non-Linear Relationships." Open Public Health Journal 10, no. 1 (March 31, 2017): 17–24. http://dx.doi.org/10.2174/1874944501610010017.

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Background: In this paper, we investigate how household alcohol consumption in Thailand relates to the age of the head of household. Methods: We use datasets drawn from socio-economic surveys of Thai households conducted during the period of 2006–2011, and we use Treenet, a data-mining technique, to investigate nonlinear relationships between response and predictors. Our study shows that households with a head of household aged between 25 and 30 years old were the most likely to consume alcohol at home and that this likelihood decreased after the age of 30. Results: Our results also reveal that the proportion of total household expenditure allocated to alcohol decreased over time, but in different ways for different age groups. Moreover, we find that the gaps in alcohol spending across the different age groups tend to become smaller over time. Our results suggest that Thai government policies seem to have been accompanied by a relative decrease in alcohol consumption by younger groups.

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FARROÑAY, FRANCISCO, MARISABEL U. ADRIANZÉN, RICARDO DE OLIVEIRA PERDIZ, and ALBERTO VICENTINI. "A new species of Macrolobium (Fabaceae, Detarioideae) endemic on a Tepui of the Guyana Shield in Brazil." Phytotaxa 361, no. 1 (July 18, 2018): 97. http://dx.doi.org/10.11646/phytotaxa.361.1.8.

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Macrolobium aracaense (Fabaceae), a new endemic treelet species from Brazil, is here described and illustrated. Morphologically it is similar to M. longipes: both are treelets, the leaflets have the same shape and are covered by papillary epidermis on the abaxial surface, and the sepals apex are minutely ciliate. Macrolobium aracaense occurs in sympatry with M. discolor var. discolor and M. gracile var. confertum in Serra do Aracá State Park, Amazonas, Brazil, and these four species can be easily differentiated by morphological characters, and their similarities and differences are here discussed.

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Bu, Wensheng, Cancan Zhang, Jihong Huang, Runguo Zang, Yi Ding, Han Xu, Mingxian Lin, and Jinsong Wang. "The Influences of Disturbance Histories and Soil Properties on Aboveground Biomass through Plant Functional Traits in a Tropical Rainforest." Forests 10, no. 9 (September 6, 2019): 774. http://dx.doi.org/10.3390/f10090774.

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Research Highlights: We try to evaluate the relative contribution of environmental factors and functional traits on aboveground biomass in a species rich tropical forest ecosystem after a 40-years natural recovery. Background and Objectives: Functional traits have a potential to incorporate community dynamics into the impacts of disturbance histories or environmental conditions on ecosystem functioning, but few studies have been conducted to understand these processes. Materials and Methods: We measured plant functional traits and soil properties in the tropical montane rainforests on Hainan Island, China, which had experienced different disturbance histories (clear cutting, selective logging, and old-growth) 40 years ago. A structural equation model was used to elucidate how disturbance histories and soil factors influence aboveground biomass (AGB) across different size classes (saplings, treelets, and adult trees) through plant functional traits. Results: The results demonstrated logging stimulated seedling establishment but decreased AGB of adult trees and wood density at community-level (CWM_WD) of sapling and adult tree. Generally, CWM_WD of sapling, treelet, and adult tree decreased linearly with the increasing of specific leaf area at community-level (CWM_SLA) in old-growth forest and these two disturbed forests. Moreover, CWM_SLA explained more variation of CWM_WD with increasing intensity of logging within sapling, treelet, and adult tree. CWM_SLA and CWM_WD not only responded to environmental conditions and disturbance intensity but also affected AGB in all size classes; meanwhile, CWM_SLA was a major driver of AGB. CWM_SLA had a stronger effect on AGB in sapling and treelet classes than on the adult tree class. Conclusions: Our results suggested that disturbance history and environmental factors could directly or indirectly affect ecosystem functioning through plant functional traits. Functional traits always had a stronger effect on AGB than environmental conditions. Moreover, CWM_SLA is a key trait that can be used to link the relationship between environmental conditions and AGB.

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Golovnya, M. "Advanced Data Mining Techniques and How to Build and Interpret Treenet/Mart and Random Forests Models: The Evolution of Data Mining from Cart to Ensembles of Trees." American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S25. http://dx.doi.org/10.1093/aje/163.suppl_11.s25-d.

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Mi, Chunrong, Huettmann Falk, and Yumin Guo. "Climate envelope predictions indicate an enlarged suitable wintering distribution for Great Bustards (Otis tarda dybowskii) in China for the 21st century." PeerJ 4 (February 1, 2016): e1630. http://dx.doi.org/10.7717/peerj.1630.

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The rapidly changing climate makes humans realize that there is a critical need to incorporate climate change adaptation into conservation planning. Whether the wintering habitats of Great Bustards (Otis tarda dybowskii), a globally endangered migratory subspecies whose population is approximately 1,500–2,200 individuals in China, would be still suitable in a changing climate environment, and where this could be found, is an important protection issue. In this study, we selected the most suitable species distribution model for bustards using climate envelopes from four machine learning models, combining two modelling approaches (TreeNet and Random Forest) with two sets of variables (correlated variables removed or not). We used common evaluation methods area under the receiver operating characteristic curves (AUC) and the True Skill Statistic (TSS) as well as independent test data to identify the most suitable model. As often found elsewhere, we found Random Forest with all environmental variables outperformed in all assessment methods. When we projected the best model to the latest IPCC-CMIP5 climate scenarios (Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 in three Global Circulation Models (GCMs)), and averaged the project results of the three models, we found that suitable wintering habitats in the current bustard distribution would increase during the 21st century. The Northeast Plain and the south of North China were projected to become two major wintering areas for bustards. However, the models suggest that some currently suitable habitats will experience a reduction, such as Dongting Lake and Poyang Lake in the Middle and Lower Yangtze River Basin. Although our results suggested that suitable habitats in China would widen with climate change, greater efforts should be undertaken to assess and mitigate unstudied human disturbance, such as pollution, hunting, agricultural development, infrastructure construction, habitat fragmentation, and oil and mine exploitation. All of these are negatively and intensely linked with global change.

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Mi, Chunrong, Falk Huettmann, Yumin Guo, Xuesong Han, and Lijia Wen. "Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence." PeerJ 5 (January 12, 2017): e2849. http://dx.doi.org/10.7717/peerj.2849.

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Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha,n = 33), White-naped Crane (Grus vipio,n = 40), and Black-necked Crane (Grus nigricollis,n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

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Kahaer, Yasenjiang, Nigara Tashpolat, Qingdong Shi, and Suhong Liu. "Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices." Water 12, no. 12 (November 30, 2020): 3360. http://dx.doi.org/10.3390/w12123360.

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The possibility of quantitative inversion of salinized soil moisture content (SMC) from Zhuhai-1 hyperspectral imagery and the application effect of fractional order differentially optimized spectral indices were discussed, which provided new research ideas for improving the accuracy of hyperspectral remote sensing inversion. The hyperspectral data from indoor and Zhuhai-1 remote sensing imagery were resampled to the same spectral scale. The soil hyperspectral data were processed by fractional order differential preprocessing method and optimized spectral indices method, and the Pearson correlation coefficient (PCC/r) analysis was made with SMC data. The sensitive optimized spectral indices were used to establish the ground hyperspectral estimation model, and a variety of modeling methods were used to select the best SMC inversion model. The results were as follows: the maximum one-dimensional r between SMC and the 466–938 nm band was −0.635, the maximum one-dimensional r with the 0.5-order absorbance spectrum was 0.665, and the maximum two-dimensional r with the difference index (DI) calculated by the 0.5-order absorbance spectrum was ±0.72. The maximum three-dimensional r with the triangle vegetation index (TVI) calculated from the 0.5-order absorbance spectrum reached 0.755, which exceeded the one-dimensional r extreme value of 400–2400 nm. The TreeNet gradient boosting machine (TGBM) regression model had the highest modeling accuracy, with a calibration coefficient of determination (R2C) = 0.887, calibration root mean square error (RMSEC) = 2.488%, standard deviation (SD) = 6.733%, and r = 0.942. However, the partial least squares regression (PLSR) model had the strongest predictive ability, with validation coefficient of determination (R2V) = 0.787, validation root mean square error (RMSEV) = 3.247%, and relative prediction deviation (RPD) = 2.071. The variable importance in projection (VIP) method could not only improve model efficiency but also increased model accuracy. R2C of the optimal PLSR model was 0.733, RMSEC was 3.028%, R2V was 0.805, RMSEV was 3.100%, RPD was 1.976, and Akaike information criterion (AIC) was 151.050. The three-band optimized spectral indices with fractional differential pretreatment could to a certain extent break through the limitation of visible near-infrared spectrum in SMC estimation due to the lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery.

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Gorst-Rasmussen, Anders. "tt: Treelet Transform with Stata." Stata Journal: Promoting communications on statistics and Stata 12, no. 1 (March 2012): 130–46. http://dx.doi.org/10.1177/1536867x1201200108.

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Zhu, Yuhao, Zeyu Fu, Zhuang Fu, Xi Chen, and Qi Wu. "Multi-Features Fusion for Fault Diagnosis of Pedal Robot Using Time-Speed Signals." Sensors 19, no. 1 (January 4, 2019): 163. http://dx.doi.org/10.3390/s19010163.

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In order to realize automation of the pollutant emission tests of vehicles, a pedal robot is designed instead of a human-driven vehicle. Sometimes, the actual time-speed curve of the vehicle will deviate from the upper or lower limit of the worldwide light-duty test cycle (WLTC) target curve, which will cause a fault. In this paper, a new fault diagnosis method is proposed and applied to the pedal robot. Since principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Autoencoder cannot extract feature information adequately when they are used alone, three types of feature components extracted by PCA, t-SNE, and Autoencoder are fused to form a nine-dimensional feature set. Then, the feature set is reduced into three-dimensional space via Treelet Transform. Finally, the fault samples are classified by Gaussian process classifier. Compared with the methods using only one algorithm to extract features, the proposed method has the minimum standard deviation, 0.0078, and almost the maximum accuracy, 98.17%. The accuracy of the proposed method is only 0.24% lower than that without Treelet Transform, but the processing time is 6.73% less than that without Treelet Transform. These indicate that the multi-features fusion model and Treelet Transform method is quite effective. Therefore, the proposed method is quite helpful for fault diagnosis of the pedal robot.

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Xia, Hedi, and Hector D. Ceniceros. "Kernel Treelets." Advances in Data Science and Adaptive Analysis 11, no. 03n04 (July 2019): 1950006. http://dx.doi.org/10.1142/s2424922x19500062.

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A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.

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Polunin, Nicholas. "Balcony Treelet Observations and Some Hunches." Environmental Conservation 20, no. 2 (1993): 163–64. http://dx.doi.org/10.1017/s037689290003767x.

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Ozay, Guner, Ferda Seyhan, Aysun Yilmaz, Thomas B. Whitaker, Andrew B. Slate, and Francis G. Giesbrecht. "Sampling Hazelnuts for Aflatoxin: Effect of Sample Size and Accept/Reject Limit on Reducing the Risk of Misclassifying Lots." Journal of AOAC INTERNATIONAL 90, no. 4 (July 1, 2007): 1028–35. http://dx.doi.org/10.1093/jaoac/90.4.1028.

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Abstract About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin sampling plans can be predicted, they can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. A method was developed to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16 aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers' risk) and accepting bad lots (buyers' risk) was demonstrated for various sampling plan designs.

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Thetford, Mack, Debbie Miller, Kathryn Smith, and Mica Schneider. "Container Size and Planting Zone Influence on Transplant Survival and Growth of Two Coastal Plants." HortTechnology 15, no. 3 (January 2005): 554–59. http://dx.doi.org/10.21273/horttech.15.3.0554.

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Survival and subsequent growth of two beach species produced in containers of differing volume and depth were evaluated following transplant on Eglin Air Force Base, Santa Rosa Island, Fla. Rooted cuttings of gulf bluestem (Schizachyrium maritimum) were produced in four container types: 1-gal (gallon), 0.75-gal treepot, 1-qt (quart), or 164-mL Ray leach tube (RLT) containers. Root and shoot biomass of gulf bluestem harvested after 12 weeks in container production were greatest for plants grown in treepot containers and root: shoot ratio decreased as container size increased. Regardless of container size, survival of beach-planted gulf bluestem was 100%. Basal area of plants from standard gallon and treepot containers was similar 11 months after transplant and basal area for plants from treepot containers remained greater than plants from quart or RLT containers. Effect of planting zone [92, 124, 170, and 200 m landward of the Gulf of Mexico (Gulf)] on transplant survival was also evaluated for inkberry (Ilex glabra). Seedling liners of inkberry were produced in 3-gal treepot or gallon containers. Inkberry was taller when grown in 3-gal treepot containers than when grown in gallon containers. Regardless of container size, all inkberry planted 92 m from the Gulf died. Inkberry survival (>75%) when grown in 3-gal treepot containers was two to six times greater than plants grown in gallon containers (15%, 50%, 40%; 124, 170, and 200 m from Gulf, respectively). After 15 months, inkberry grown in 3-gal treepot containers remained larger with 1.5 times the mean maximum height and twice the mean canopy area compared to those grown in gallon containers.

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Goldberg, Michael R., Michael Y. Appel, Liat Nachshon, Mor Carmel, Michael B. Levy, and Arnon Elizur. "The Utility of BAT in Diagnosing Treenut Allergy." Journal of Allergy and Clinical Immunology 139, no. 2 (February 2017): AB276. http://dx.doi.org/10.1016/j.jaci.2016.12.887.

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Gorst-Rasmussen, A., C. C. Dahm, C. Dethlefsen, T. Scheike, and K. Overvad. "Exploring Dietary Patterns By Using the Treelet Transform." American Journal of Epidemiology 173, no. 10 (April 7, 2011): 1097–104. http://dx.doi.org/10.1093/aje/kwr060.

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Carro, A., M. Tress, D. de Juan, F. Pazos, P. Lopez-Romero, A. del Sol, A. Valencia, and A. M. Rojas. "TreeDet: a web server to explore sequence space." Nucleic Acids Research 34, Web Server (July 1, 2006): W110—W115. http://dx.doi.org/10.1093/nar/gkl203.

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Crossett, Andrew, Ann B. Lee, Lambertus Klei, Bernie Devlin, and Kathryn Roeder. "Refining genetically inferred relationships using treelet covariance smoothing." Annals of Applied Statistics 7, no. 2 (June 2013): 669–90. http://dx.doi.org/10.1214/12-aoas598.

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Wright, David M., Gerry McKenna, Anne Nugent, Lewis Winning, Gerard J. Linden, and Jayne V. Woodside. "Association between diet and periodontitis: a cross-sectional study of 10,000 NHANES participants." American Journal of Clinical Nutrition 112, no. 6 (October 23, 2020): 1485–91. http://dx.doi.org/10.1093/ajcn/nqaa266.

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ABSTRACT Background Periodontitis is a major cause of tooth loss globally. Risk factors include age, smoking, and diabetes. Intake of specific nutrients has been associated with periodontitis risk but there has been little research into the influence of overall diet, potentially more relevant when formulating dietary recommendations. Objectives We aimed to investigate potential associations between diet and periodontitis using novel statistical techniques for dietary pattern analysis. Methods Two 24-h dietary recalls and periodontal examination data from the cross-sectional US NHANES, 2009–2014 (n = 10,010), were used. Dietary patterns were extracted using treelet transformation, a data-driven hierarchical clustering and dimension reduction technique. Associations between each pattern [treelet component (TC)] and extent of periodontitis [proportion of sites with clinical attachment loss (CAL) ≥ 3 mm] were estimated using robust logistic quantile regression, adjusting for age, sex, ethnicity, education level, smoking, BMI, and diabetes. Results Eight TCs explained 21% of the variation in diet, 1 of which (TC1) was associated with CAL extent. High TC1 scores represented a diet rich in salad, fruit, vegetables, poultry and seafood, and plain water or tea to drink. There was a substantial negative gradient in CAL extent from the lowest to the highest decile of TC1 (median proportion of sites with CAL ≥ 3 mm: decile 1 = 19.1%, decile 10 = 8.1%; OR, decile 10 compared with decile 1: 0.67; 95% CI: 0.46, 0.99). Conclusions Most dietary patterns identified were not associated with periodontitis extent. One pattern, however, rich in salad, fruit, and vegetables and with plain water or tea to drink, was associated with lower CAL extent. Treelet transformation may be a useful approach for calculating dietary patterns in nutrition research.

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Koptur, Suzanne, William A. Haber, Gordon W. Frankie, and Herbert G. Baker. "Phenological studies of shrub and treelet species in tropical cloud forests of Costa Rica." Journal of Tropical Ecology 4, no. 4 (November 1988): 323–46. http://dx.doi.org/10.1017/s0266467400002984.

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ABSTRACT(1) During 1978–1981, marked individuals of 107 species of treelets and shrubs in three forest types between 1300–1650 m elevation at Monteverde, Costa Rica, were monitored at monthly intervals for behaviour of leafing, flowering, and fruiting.(2) Although there was not a pronounced seasonal pattern of leafing activity, more species produced new leaves in the dry season. Species that flush large quantities of new leaves do so more commonly in the drier months. Leaf loss was gradual and unobtrusive in species observed.(3) Flowering activity was greatest in the late dry season and early wet season. Most species exhibited extended flowering; only 15% of the species were massively flowering. Massive flowerers showed less seasonality than extended flowerers.(4) Of the species studied, the majority had relatively unspecialized flowers which were visited by a variety of insects; small bee-pollination was the next most common, followed by hummingbird, beetle, settling moth, sphingid, butterfly, large bee and fly pollination (the pollination system of 18 species was unknown). Hummingbird pollinated species showed little seasonality of flowering when compared with species exhibiting small moth, and beetle pollination syndromes, as well as those with unspecialized flowers.(5) The vast majority of species studied have fleshy fruits (sarcochores). Fruiting activity was less markedly seasonal than flowering. Species with fruit are more numerous in the second half of the year (the wet season and early dry season). The second year of the study saw substantially fewer species in fruit than the first year; this is attributed to the greater than usual rainfall and inclement weather during the peak flowering season.(6) Cloud forest shrub and treelet phenology is compared with patterns of other forests that have been studied. In general, the greater the rainfall, the less seasonality of flowering and fruiting is seen. Although Monteverde is very wet, rainfall is intermediate between that of lowland dry and lowland wet forest in Costa Rica. Seasonality of flowering and fruiting at Monteverde is more pronounced than at La Selva (wet) and less obvious than in Guanacaste (dry).

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Sun, Zeyu, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. "TreeGen: A Tree-Based Transformer Architecture for Code Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8984–91. http://dx.doi.org/10.1609/aaai.v34i05.6430.

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A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model.

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Mareček, David, and Zdeněk Žabokrtský. "Gibbs Sampling Segmentation of Parallel Dependency Trees for Tree-Based Machine Translation." Prague Bulletin of Mathematical Linguistics 105, no. 1 (April 1, 2016): 101–10. http://dx.doi.org/10.1515/pralin-2016-0005.

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Abstract We present a work in progress aimed at extracting translation pairs of source and target dependency treelets to be used in a dependency-based machine translation system. We introduce a novel unsupervised method for parallel tree segmentation based on Gibbs sampling. Using the data from a Czech-English parallel treebank, we show that the procedure converges to a dictionary containing reasonably sized treelets; in some cases, the segmentation seems to have interesting linguistic interpretations.

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ARANTES, MARIANA OLIVEIRA. "ENTREVISTA COM DAVID TREECE." História e Cultura 2, no. 1 (August 19, 2013): 183. http://dx.doi.org/10.18223/hiscult.v2i1.947.

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Zhao, Cheng, Wuxiong Zhang, Yang Yang, and Sha Yao. "Treelet-Based Clustered Compressive Data Aggregation for Wireless Sensor Networks." IEEE Transactions on Vehicular Technology 64, no. 9 (September 2015): 4257–67. http://dx.doi.org/10.1109/tvt.2014.2361250.

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Gaüzère, Benoit, Pierre-Anthony Grenier, Luc Brun, and Didier Villemin. "Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics." Pattern Recognition 48, no. 2 (February 2015): 356–67. http://dx.doi.org/10.1016/j.patcog.2014.07.029.

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38

Valois-Cuesta, Hamleth, Pascual J. Soriano, and Juan Francisco Ornelas. "Gender specialization in Palicourea demissa (Rubiaceae), a distylous, hummingbird-pollinated treelet." Plant Systematics and Evolution 298, no. 5 (March 6, 2012): 975–84. http://dx.doi.org/10.1007/s00606-012-0607-7.

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Yue, Kai, Lei Yang, Ruirui Li, Wei Hu, Fan Zhang, and Wei Li. "TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation." ISPRS Journal of Photogrammetry and Remote Sensing 156 (October 2019): 1–13. http://dx.doi.org/10.1016/j.isprsjprs.2019.07.007.

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Wu, Edmond Qi, Jin Wang, Xian-Yong Peng, Peidong Zhang, Rob Law, Xi Chen, and Jin-xing Lin. "Fault diagnosis of rotating machinery using Gaussian process and EEMD-treelet." International Journal of Adaptive Control and Signal Processing 33, no. 1 (November 28, 2018): 52–73. http://dx.doi.org/10.1002/acs.2952.

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41

Baia, Silvano Fernandes. "Brazilian jive, de David Treece." Música Popular em Revista 3, no. 1 (September 22, 2014): 142–50. http://dx.doi.org/10.20396/muspop.v3i1.12963.

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A resenha analisa o livro Brazilian jive, do professor e pesquisador inglês David Treece, que estuda um conjunto de gêneros musicais que se consolidaram ao longo do século XX no Brasil, com destaque para o samba, a bossa nova e o rap.

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42

Lukes, Eileen. "Isabella Treece, NIWI Grant Recipient." AAOHN Journal 49, no. 2 (February 2001): 66–68. http://dx.doi.org/10.1177/216507990104900201.

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Terborgh, John, and Jeffrey Mathews. "Partitioning of the understorey light environment by two Amazonian treelets." Journal of Tropical Ecology 15, no. 6 (November 1999): 751–63. http://dx.doi.org/10.1017/s0266467499001157.

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Primary tropical forests comprise a mosaic of mature, gap and building phase patches, resulting in great spatial variation in the distribution of foliage. Light may consequently penetrate into the forest interior over a wide range of angles. It thus seems possible that understorey tree species might be adapted for distinct understorey light conditions. At the Cocha Cashu Biological Station in Madre de Dios, Peru, there are two understorey treelets distinguished by contrasting crown architectures. One, Neea chlorantha (Nyctaginaceae), possesses a much-branched superstructure and displays a smoothly contoured shell of drooping elliptical leaves. The other, Rinorea viridifolia (Violaceae), displays planar arrays of horizontally-oriented obovate leaves held on whorls of stiffly radiating horizontal branches.With the aid of hemispherical photography and the program CANOPY, the light environment at large within a 2.25-ha permanent tree plot, and above and below the crowns of Neea and Rinorea treelets, was investigated. Available light (measured as uncorrected indirect site factor (ISFU) in CANOPY) at control points showed a log-linear increase with height from 2.1 to 14.2 m. The relative amount of lateral illumination also increased with height. Photographs were taken just above and below the crowns of 50 Neea and 50 Rinorea treelets. Neea crowns were more effective at intercepting light from overhead sources, whereas Rinorea crowns were more effective at intercepting light from lateral sources. Adult Neea and Rinorea treelets occurred at locations in the forest where they were exposed to differing angular distributions of incident light, suggesting that the two species were engaged in a form of resource partitioning, a conclusion that is in conflict with the non-equilibrium model of tropical forest tree species diversity. It is suggested that tropical tree species diversity is, at least in part, a product of adaptive specialization to a spatially heterogeneous light environment.

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Meirelles, Julia, and Renato Goldenberg. "A new species of Miconia (Miconieae, Melastomataceae) from the Brazilian Amazon." Phytotaxa 173, no. 4 (July 2, 2014): 278. http://dx.doi.org/10.11646/phytotaxa.173.4.2.

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A new species of Miconia sect. Miconia subsection Seriatiflorae. was collected in Igapó Forest from the Purus-Madeira interfluve, in the state of Amazonas, Brazil. This new species, Miconia suberosa, can be distinguished from other species in the subsection by a variety of morphological features, namely: (i) treelet habit; (ii) deeply fissured bark with a thick cork; (iii) long dendritic hairs with short arms on young branches, inflorescences and leaves; (iv) petiolated leaves with rounded to obtuse bases, and ciliate margins, congested at the branch apices; and, (v) truncated stigmas.

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WÜTHERICH, DIRK, AURA AZÓCAR, CARLOS GARCÍA-NUÑEZ, and JUAN F. SILVA. "Seed dispersal in Palicourea rigida, a common treelet species from neotropical savannas." Journal of Tropical Ecology 17, no. 3 (April 27, 2001): 449–58. http://dx.doi.org/10.1017/s0266467401001304.

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The dispersal system of Palicourea rigida (Rubiaceae), a common woody species of neotropical savannas, was studied in a seasonal Venezuelan savanna. Production and consumption of fruits, dispersal agents, feeding behaviour, transport and effects on seed viability, were investigated by field observations, cage experiments and viability tests. To compare different dispersers, quantitative and qualitative dispersal parameters were calculated. About 59% of the fruit crop was dispersed successfully by birds. Although 10 different bird species consumed the fruits of Palicourea rigida, three bird species dispersed 79% of the fruits (Tyrannus melancholicus, Molothrus bonariensis and Elaenia chiriquensis). T. melancholicus (Tyrannidae) was the most important dispersal agent, with more than 39% of dispersed seeds. In contrast to the predictions of the specialization vs. generalization paradigm, this system achieves high effectiveness by means of predominantly unspecialized traits in both the plant and the dispersers.Se estudió el sistema de dispersión de Palicourea rigida (Rubiaceae), una leñosa común de las sabanas neotropicales, en una sabana estacional de Venezuela. Mediante observaciones de campo y experimentos en jaulas y en el laboratorio, se estudiaron paramétros tales como producción, transporte y consumo de frutos, los agentes dispersores y su comportamiento y los efectos sobre la viabilidad de las semillas. Para comparar los agentes de dispersión se estimaron paramétros cualitativos y cuantitativos. Cerca del 59% de la cosecha total de frutos fue dispersada existosamente por pájaros. Aunque los frutos fueron consumidos por diez especies de aves, el 79% de los frutos fueron dispersados por solamente tres especies (Tyrannus melancholicus, Molothrus bonariensis y Elaenia chiriquensis). T. melancholicus (Tyrannidae) fue el más importante agente dispersor con más del 39% del total de semillas dispersadas. En contraste con el paradigma de especialización vs. generalización, este sistema alcanza alta efectividad pero se caracteriza por rasgos poco especializados, tanto en la planta como en los dispersores.

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Quirk, Christopher, and Arul Menezes. "Dependency treelet translation: the convergence of statistical and example-based machine-translation?" Machine Translation 20, no. 1 (February 8, 2007): 43–65. http://dx.doi.org/10.1007/s10590-006-9008-4.

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47

Ye, Hung-Yu, and C. W. Liu. "On-Current Enhancement in TreeFET by Combining Vertically Stacked Nanosheets and Interbridges." IEEE Electron Device Letters 41, no. 9 (September 2020): 1292–95. http://dx.doi.org/10.1109/led.2020.3010240.

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48

Schmalz, B., F. Tavares, and N. Fohrer. "Assessment of nutrient entry pathways and dominating hydrological processes in lowland catchments." Advances in Geosciences 11 (June 15, 2007): 107–12. http://dx.doi.org/10.5194/adgeo-11-107-2007.

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Abstract. The achievement of a good water quality in all water bodies until 2015 is legally regulated since December 2000 for all European Union member states by the European Water Framework Directive (EU, 2000). The aim of this project is to detect nutrient entry pathways and to assess the dominating hydrological processes in complex mesoscale catchments. The investigated Treene catchment is located in Northern Germany as a part of a lowland area. Sandy, loamy and peat soils are characteristic for this area. Land use is dominated by agriculture and pasture. Drainage changed the natural water balance. In a nested approach we examined two catchment areas: a) Treene catchment 517 km2, b) Kielstau catchment 50 km2. The nested approach assists to improve the process understanding by using data of different scales. Therefore these catchments serve not only as an example but the results are transferable to other lowland catchment areas. In a first step the river basin scale model SWAT (Soil and Water Assessment Tool, Arnold et al., 1998) was used successfully to model the water balance. Furthermore the water quality was analysed to distinguish the impact of point and diffuse sources. The results show that the tributaries in the Kielstau catchment contribute high amounts of nutrients, mainly nitrate and ammonium. For the parameters nitrate, ammonium and phosphorus it was observed as a tendency that the annual loads were increasing along the river profile of the Kielstau.

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Ramasubramanian, Venugopalan, Dahlia Malkhi, Fabian Kuhn, Mahesh Balakrishnan, Archit Gupta, and Aditya Akella. "On the treeness of internet latency and bandwidth." ACM SIGMETRICS Performance Evaluation Review 37, no. 1 (June 15, 2009): 61–72. http://dx.doi.org/10.1145/2492101.1555357.

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White, WT, SF Hills, R. Gaddam, BR Holland, and David Penny. "Treeness Triangles: Visualizing the Loss of Phylogenetic Signal." Molecular Biology and Evolution 24, no. 9 (July 13, 2007): 2029–39. http://dx.doi.org/10.1093/molbev/msm139.

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