Art and Learning: Fostering Ecological Awareness.

ERIC Educational Resources Information Center

Cole, Elizabeth

1992-01-01

Swaffield School, a nursery and primary school in innercity London, developed a multidisciplinary recycling project, Save the Trees, which focuses on paper and its link to the forest. The project involves group participation in handmade paper storycloths based on forest images and development of stories derived from the storycloths. (LB)

Keeping the Academics in Service Learning Projects, or Teaching Environmental History to Tree Planters

ERIC Educational Resources Information Center

Stemen, Mark

2003-01-01

In California, and elsewhere, faculty are being encouraged to create "service learning opportunities" for their students, such as having social work students volunteer at a local soup kitchen, or having environmental studies students reforest the local watershed. While these endeavors have obvious social value, their education value…

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

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

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

A Walk on the Wild Side: Adventures with Project Learning Tree. A Gifted Science Unit for Grades 1-5.

ERIC Educational Resources Information Center

Hestad, Marsha; Avellone, Kathy

This 9-week curriculum unit on trees is designed for gifted students in grades 1-5. The lessons are designed for 40-minute classes meeting two or three times a week and stress the development of creative thinking skills, creative problem solving and decision making skills, and critical and logical thinking skills. Each of the 12 lesson plans…

Using Tree-Ring Data, Research, and Expeditions as an Accessible, Hands-on "Bridge" into Climate Studies for Diverse Audiences

NASA Astrophysics Data System (ADS)

Davi, N. K.; Wattenberg, F.; Pringle, P. T.; Tanenbaum, J.; O'Brien, A.; Greidanus, I.; Perry, M.

2012-12-01

Tree-ring research provides an engaging, intuitive, and relevant entryway into understanding both climate-change and environmental research, as well as the process of science from inspiration, to fieldwork, to analysis, to publishing and communicating. The basic premise of dendrochronology is that annual rings reflect environmental conditions year-by-year and that by studying long-lived trees we can learn about past environments and climates for hundreds-to-thousands of years in the past. Conceptually, this makes tree-ring studies accessible to students and faculty for a number of reasons. First, in order to collect their data, dendrochronologists often launch expeditions to stunningly picturesque and remote places in search of long-lived, climate sensitive trees. Scientist exciting stories and images from the field can be leveraged to connect students to the study and the data. Second, tree-rings can be more easily explained as a proxy for climate than other methods (ice cores, carbon-isotope ratios, etc.), and most people have prior-knowledge about trees and annual growth rings. It is even possible, for example, for non-expert audiences to see climate variability through time with the naked eye by looking at climate sensitive tree cores. Third, tree-rings are interdisciplinary and illustrate the interplay between the mathematical sciences, the biological sciences, and the geosciences—that is, they show that the biosphere is a fundamental component of the Earth system. Here, we will present several projects have been initiated for a range of audiences, including; elementary school, where 5th graders visited a local forest to collect samples and apply their samples and what they learned to math and science classes. 5th grade students also leaned how to use Climate Explorer (KNMI), an online tool that allows scientist and students the opportunity to access and visualize global climate data within a few clicks. Geared to 2 and 4 year colleges, we are also collaboratively developing new interdisciplinary science and mathematical curriculum, interactive game modules, and multi-media that focus on using tree-ring expeditions and research projects that have real-world applications related to societal concerns (drought, warming, or in some cases, finances) to support student-centered inquiry-based learning. We are also creating professional development guides for teachers.

Lessons from the Rain Forest.

ERIC Educational Resources Information Center

Phillips, Shelley

2002-01-01

Presents a first-grade art project after students learned about the rain forest and heard the story, "The Great Kapok Tree: A Tale of the Amazon Rain Forest" (Lynn Cherry). Explains that the students created pictures of the rain forest. (CMK)

An Evaluation of Project Learning Tree in British Columbia. Appendices.

ERIC Educational Resources Information Center

Conry, Robert F.; And Others

The volume contains seven appendices (A-G) which accompany the first volume. Appendix A provides a list of project personnel and of teachers who participated in the unit development workshop. Appendix B, composed of six sections, includes the unit lesson plans and teachers' guides used in the field study for grades 3, 5, and 7. The grade materials…

Unconscious and Unnoticed Professional Practice within an Outstanding School for Children and Young People with Complex Learning Difficulties and Disabilities

ERIC Educational Resources Information Center

Crombie, Richard; Sullivan, Lesley; Walker, Kate; Warnock, Rebecca

2014-01-01

This article describes a three-year project undertaken at Pear Tree School for children and young people with severe and multiple and profound learning difficulties. Lesley Sullivan, the school's head teacher, believed that much of the value within the work of this outstanding school went unidentified by existing approaches to planning, monitoring…

A citizen science campaign encouraging urban forest professionals to engage the public in the collection of tree phenological data

NASA Astrophysics Data System (ADS)

Clarke, K. C.

2009-12-01

There are growing concerns among leading national and local organizations about American scientific literacy, fundamental understanding of science, and the value of scientific research. These organizations, including the University Corporation for Atmospheric Research, have been at the forefront in addressing these concerns. In an effort to improve scientific literacy, research conducted by Sam Droege, among others, suggested using citizen science and public participation as instrumental methods to engage the public. Urban Tree Phenology (UTP), a project of Project BudBurst and the USDA Forest Service, is one such citizen science program that sought to engage the public, including the professionals and amateurs among them, in collecting urban tree phenophase data. UTP participants monitored and reported the stages of phenological events, such as First Leaf and Leaf Fall, of 24 native and cultivated urban tree species, using the steps shown in Figure 1. Data collected will support the long-term research of plant ecology, climate change, public health, urban heat islands on tree physiology, and urban tree management. UTP, using the architectures of online learning, has developed two instructional tutorials to assist data collection (Phase 1). The instructional tutorials were published online, in print and PowerPoint formats, at www.UrbanTreePhenology.com. By completing these tutorials, participants will gain the skills necessary to provide urban tree phenological data to national research databases via the Internet. Phase 2 will test and review the instructional materials developed, and in Phase 3, the administrators of UTP will distribute promotional materials to national research organizations and to participants of the Project BudBurst national citizen science campaign.

The Greening of St Patrick's.

ERIC Educational Resources Information Center

Barron, Jennie

1993-01-01

The grade 6-7 class at St. Patrick's School in Hamilton (Ontario) engages in outdoor environmental projects to enhance classroom learning. Some student activities have been (1) worm composting; (2) tree planting; (3) restoring tern nesting areas; and (4) planning and cultivating a sophisticated garden on school grounds. (KS)

Tree of Life Web Project

Science.gov Websites

, etc.) about this picture Learn about ... Agaricales (a group of fungi) image info The Agaricales, or euagarics clade, is a monophyletic group of approximately 8500 mushroom species...read more more featured ). Each page contains information about a particular group, e.g., salamanders, segmented worms, phlox

Idea Notebook: Recycling with an Educational Purpose.

ERIC Educational Resources Information Center

Gerth, Tom; Wilson, David A.

1986-01-01

Four students at St. Louis University High School developed a project to clean up the environment while saving energy and natural resources. Aluminum and steel cans were recycled and the money was used to buy and plant trees. Students learned about recycling, organization, money management, and improving the environment. (JMM)

CFIRP: What we learned in the first ten years

USGS Publications Warehouse

Chambers, C.L.; McComb, W.C.; Tappeiner, J. C.; Kellogg, L.D.; Johnson, R.L.; Spycher, G.

1999-01-01

In response to public dissatisfaction with forest management methods, we initiated the College of Forestry Integrated Research Project (CFIRP) to test alternative silvicultural systems in Douglas-fir (Pseudotsuga menziesii stands in western Oregon. We compared costs and biological and human responses among a control and three replicated silvicultural alternatives to clearcutting that retained structural features found in old Douglas-fir forests. Treatments were applied within 8- to 15-ha stands and attempted to mimic crown fires (modified clearcut), windthrow (green tree retention), and small-scale impacts such as root rot diseases (small patch group selection). We also compared costs in three unreplicated treatments (large patch group selection, wedge cut, and strip cut). Each treatment included differences in the pattern of retained dead trees (snags), as either scattered individuals or as clumps. Good communication among researchers and managers, a long-term commitment to the project, and careful documentation of research sites and data are important to the success of long-term silvicultural research projects. To date, over 30 publications have resulted from the project.

DNA: The Strand that Connects Us All

ScienceCinema

Kaplan, Matt [Univ. of Arizona, Tucson, AZ (United States). Genetics Core Facility

2018-04-26

Learn how the methods and discoveries of human population genetics are applied for personal genealogical reconstruction and anthropological testing. Dr. Kaplan starts with a short general review of human genetics and the biology behind this form of DNA testing. He looks at how DNA testing is performed and how samples are processed in the University of Arizona laboratory. He also examines examples of personal genealogical results from Family Tree DNA and personal anthropological results from the Genographic Project. Finally, he describes the newest project in the UA laboratory, the DNA Shoah Project.

Environmental Education Activity Guide: Pre K-8.

ERIC Educational Resources Information Center

Iozzi, Lou; Halsey, Brent, Jr.

Project Learning Tree uses the forest as a window on the world to increase students' understanding of the complex environment in the United States; to stimulate critical and creative thinking; to develop the ability to make informed decisions on environmental issues; and to instill the confidence and commitment to take responsible action on behalf…

An Evaluation of "Forests of the World," a Project Learning Tree Secondary Module

ERIC Educational Resources Information Center

Ghent, Cynthia; Parmer, Giavanna; Haines, Sarah

2013-01-01

This study sought to determine whether a secondary level curricular model based on enhancing knowledge and awareness of global forest issues would have an effect on students' self-perceived knowledge of forest issues, actual content knowledge of these issues, and pro-environmental attitudes. The study instrument is the secondary module…

Assessing and Improving Student Understanding of Tree-Thinking

NASA Astrophysics Data System (ADS)

Kummer, Tyler A.

Evolution is the unifying theory of biology. The importance of understanding evolution by those who study the origins, diversification and diversity life cannot be overstated. Because of its importance, in addition to a scientific study of evolution, many researchers have spent time studying the acceptance and the teaching of evolution. Phylogenetic Systematics is the field of study developed to understand the evolutionary history of organisms, traits, and genes. Tree-thinking is the term by which we identify concepts related to the evolutionary history of organisms. It is vital that those who undertake a study of biology be able to understand and interpret what information these phylogenies are meant to convey. In this project, we evaluated the current impact a traditional study of biology has on the misconceptions students hold by assessing tree-thinking in freshman biology students to those nearing the end of their studies. We found that the impact of studying biology was varied with some misconceptions changing significantly while others persisted. Despite the importance of tree-thinking no appropriately developed concept inventory exists to measure student understanding of these important concepts. We developed a concept inventory capable of filling this important need and provide evidence to support its use among undergraduate students. Finally, we developed and modified activities as well as courses based on best practices to improve teaching and learning of tree-thinking and organismal diversity. We accomplished this by focusing on two key questions. First, how do we best introduce students to tree-thinking and second does tree-thinking as a course theme enhance student understanding of not only tree-thinking but also organismal diversity. We found important evidence suggesting that introducing students to tree-thinking via building evolutionary trees was less successful than introducing the concept via tree interpretation and may have in fact introduced or strengthened a misconception. We also found evidence that infusing tree-thinking into an organismal diversity course not only enhances student understanding of tree-thinking but also helps them better learn organismal diversity.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Project Interface Requirements Process Including Shuttle Lessons Learned

NASA Technical Reports Server (NTRS)

Bauch, Garland T.

2010-01-01

Most failures occur at interfaces between organizations and hardware. Processing interface requirements at the start of a project life cycle will reduce the likelihood of costly interface changes/failures later. This can be done by adding Interface Control Documents (ICDs) to the Project top level drawing tree, providing technical direction to the Projects for interface requirements, and by funding the interface requirements function directly from the Project Manager's office. The interface requirements function within the Project Systems Engineering and Integration (SE&I) Office would work in-line with the project element design engineers early in the life cycle to enhance communications and negotiate technical issues between the elements. This function would work as the technical arm of the Project Manager to help ensure that the Project cost, schedule, and risk objectives can be met during the Life Cycle. Some ICD Lessons Learned during the Space Shuttle Program (SSP) Life Cycle will include the use of hardware interface photos in the ICD, progressive life cycle design certification by analysis, test, & operations experience, assigning interface design engineers to Element Interface (EI) and Project technical panels, and linking interface design drawings with project build drawings

Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

PubMed

Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling

2015-11-01

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

A River Runs through It: A School on the Edge of the Columbia River Estuary Combines Science and Stewardship Right in Its Own Backyard.

ERIC Educational Resources Information Center

Sherman, Lee

2002-01-01

The estuary at the mouth of the Columbia River in Wahkiakum County Washington) provides a natural laboratory for experiential learning. Wahkiakum High School students participate in interdisciplinary projects that have included habitat restoration, a salmon hatchery, stream restoration, tree planting, and recreating the final leg of the Lewis and…

"The Trampoline Tree and the Swamp Monster with 18 Heads": Outdoor Play in the Foundation Stage and Foundation Phase

ERIC Educational Resources Information Center

Waller, Tim

2007-01-01

This paper considers pedagogy and outdoor play in the early years. The particular focus is on the specific features and benefits of outdoor play in the Foundation Stage (England) and Foundation Phase (Wales). The paper will draw on current international literature and evidence from outdoor learning constructed in an ongoing research project in two…

Lessons learned from Applications of a Decision Tree for Confronting Climate Change Uncertainty - the Short Term and the Long Term

NASA Astrophysics Data System (ADS)

Ray, P. A.; Wi, S.; Bonzanigo, L.; Taner, M. U.; Rodriguez, D.; Garcia, L.; Brown, C.

2016-12-01

The Decision Tree for Confronting Climate Change Uncertainty is a hierarchical, staged framework for accomplishing climate change risk management in water resources system investments. Since its development for the World Bank Water Group two years ago, the framework has been applied to pilot demonstration projects in Nepal (hydropower generation), Mexico (water supply), Kenya (multipurpose reservoir operation), and Indonesia (flood risks to dam infrastructure). An important finding of the Decision Tree demonstration projects has been the need to present the risks/opportunities of climate change to stakeholders and investors in proportion to risks/opportunities and hazards of other kinds. This presentation will provide an overview of tools and techniques used to quantify risks/opportunities to each of the project types listed above, with special attention to those found most useful for exploration of the risk space. Careful exploration of the risk/opportunity space shows that some interventions would be better taken now, whereas risks/opportunities of other types would be better instituted incrementally in order to maintain reversibility and flexibility. A number of factors contribute to the robustness/flexibility tradeoff: available capital, magnitude and imminence of potential risk/opportunity, modular (or not) character of investment, and risk aversion of the decision maker, among others. Finally, in each case, nuance was required in the translation of Decision Tree findings into actionable policy recommendations. Though the narrative of stakeholder solicitation, engagement, and ultimate partnership is unique to each case, summary lessons are available from the portfolio that can serve as a guideline to the community of climate change risk managers.

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

PubMed Central

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project. PMID:26339227

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects.

PubMed

Shin, Yoonseok

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Learning Extended Finite State Machines

NASA Technical Reports Server (NTRS)

Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

2014-01-01

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

Assessment of student learning associated with tree thinking in an undergraduate introductory organismal biology course.

PubMed

Smith, James J; Cheruvelil, Kendra Spence; Auvenshine, Stacie

2013-01-01

Phylogenetic trees provide visual representations of ancestor-descendant relationships, a core concept of evolutionary theory. We introduced "tree thinking" into our introductory organismal biology course (freshman/sophomore majors) to help teach organismal diversity within an evolutionary framework. Our instructional strategy consisted of designing and implementing a set of experiences to help students learn to read, interpret, and manipulate phylogenetic trees, with a particular emphasis on using data to evaluate alternative phylogenetic hypotheses (trees). To assess the outcomes of these learning experiences, we designed and implemented a Phylogeny Assessment Tool (PhAT), an open-ended response instrument that asked students to: 1) map characters on phylogenetic trees; 2) apply an objective criterion to decide which of two trees (alternative hypotheses) is "better"; and 3) demonstrate understanding of phylogenetic trees as depictions of ancestor-descendant relationships. A pre-post test design was used with the PhAT to collect data from students in two consecutive Fall semesters. Students in both semesters made significant gains in their abilities to map characters onto phylogenetic trees and to choose between two alternative hypotheses of relationship (trees) by applying the principle of parsimony (Occam's razor). However, learning gains were much lower in the area of student interpretation of phylogenetic trees as representations of ancestor-descendant relationships.

Assessment of Student Learning Associated with Tree Thinking in an Undergraduate Introductory Organismal Biology Course

PubMed Central

Smith, James J.; Cheruvelil, Kendra Spence; Auvenshine, Stacie

2013-01-01

Phylogenetic trees provide visual representations of ancestor–descendant relationships, a core concept of evolutionary theory. We introduced “tree thinking” into our introductory organismal biology course (freshman/sophomore majors) to help teach organismal diversity within an evolutionary framework. Our instructional strategy consisted of designing and implementing a set of experiences to help students learn to read, interpret, and manipulate phylogenetic trees, with a particular emphasis on using data to evaluate alternative phylogenetic hypotheses (trees). To assess the outcomes of these learning experiences, we designed and implemented a Phylogeny Assessment Tool (PhAT), an open-ended response instrument that asked students to: 1) map characters on phylogenetic trees; 2) apply an objective criterion to decide which of two trees (alternative hypotheses) is “better”; and 3) demonstrate understanding of phylogenetic trees as depictions of ancestor–descendant relationships. A pre–post test design was used with the PhAT to collect data from students in two consecutive Fall semesters. Students in both semesters made significant gains in their abilities to map characters onto phylogenetic trees and to choose between two alternative hypotheses of relationship (trees) by applying the principle of parsimony (Occam's razor). However, learning gains were much lower in the area of student interpretation of phylogenetic trees as representations of ancestor–descendant relationships. PMID:24006401

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

Patterns of thinking about phylogenetic trees: A study of student learning and the potential of tree thinking to improve comprehension of biological concepts

NASA Astrophysics Data System (ADS)

Naegle, Erin

Evolution education is a critical yet challenging component of teaching and learning biology. There is frequently an emphasis on natural selection when teaching about evolution and conducting educational research. A full understanding of evolution, however, integrates evolutionary processes, such as natural selection, with the resulting evolutionary patterns, such as species divergence. Phylogenetic trees are models of evolutionary patterns. The perspective gained from understanding biology through phylogenetic analyses is referred to as tree thinking. Due to the increasing prevalence of tree thinking in biology, understanding how to read phylogenetic trees is an important skill for students to learn. Interpreting graphics is not an intuitive process, as graphical representations are semiotic objects. This is certainly true concerning phylogenetic tree interpretation. Previous research and anecdotal evidence report that students struggle to correctly interpret trees. The objective of this research was to describe and investigate the rationale underpinning the prior knowledge of introductory biology students' tree thinking Understanding prior knowledge is valuable as prior knowledge influences future learning. In Chapter 1, qualitative methods such as semi-structured interviews were used to explore patterns of student rationale in regard to tree thinking. Seven common tree thinking misconceptions are described: (1) Equating the degree of trait similarity with the extent of relatedness, (2) Environmental change is a necessary prerequisite to evolution, (3) Essentialism of species, (4) Evolution is inherently progressive, (5) Evolution is a linear process, (6) Not all species are related, and (7) Trees portray evolution through the hybridization of species. These misconceptions are based in students' incomplete or incorrect understanding of evolution. These misconceptions are often reinforced by the misapplication of cultural conventions to make sense of trees. Chapter 2 explores the construction, validity, and reliability of a tree thinking concept inventory. Concept inventories are research based instruments that diagnose faulty reasoning among students. Such inventories are tools for improving teaching and learning of concepts. Test scores indicate that tree thinking misconceptions are held by novice and intermediate biology students. Finally, Chapter 3 presents a tree thinking rubric. The rubric aids teachers in selecting and improving introductory tree thinking learning exercises that address students' tree thinking misconceptions.

Introduction in IND and recursive partitioning

NASA Technical Reports Server (NTRS)

Buntine, Wray; Caruana, Rich

1991-01-01

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, and lists the manual pages for the routines and instructions on installation.

A probability approach to sawtimber tree-value projections

Treesearch

Roger E. McCay; Paul S. DeBald; Paul S. DeBald

1973-01-01

The authors present a method for projecting hardwood sawtimber tree values, using tree-development probabilities based on continuous forest inventory (CFI) data and describe some ways to use the resulting value projections to assemble management-planning information.

Tree Nut Allergies

MedlinePlus

... Blog Vision Awards Common Allergens Tree Nut Allergy Tree Nut Allergy Learn about tree nut allergy, how ... a Tree Nut Label card . Allergic Reactions to Tree Nuts Tree nuts can cause a severe and ...

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

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

A computerized tree growth projection system for forest resource evaluation in the lake states

Treesearch

Allen L. Lundgren; Burton L. Essex

1978-01-01

A computerized tree growth projection system has been developed for the Lake States Region as part of a larger Forest Resources Evaluation Program (FREP). Incorporating data from more than 1500 permanent growth plots throughout the Lake States, this system projects tree growth, mortality, regeneration, and removals in stands with any mixture of tree species and sizes,...

Introduction to IND and recursive partitioning, version 1.0

NASA Technical Reports Server (NTRS)

Buntine, Wray; Caruana, Rich

1991-01-01

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, lists the manual pages for the routines, and instructions on installation.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

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

Tentative guides for the selection of plus trees and superior stands in Douglas-fir.

Treesearch

Leo A. Isaac

1955-01-01

Interest among foresters in forest tree improvement has increased rapidly in recent years. Geneticists have learned that some individual trees greatly excel their neighbors in desirable characteristics, and that some entire stands are superior to other stands of the same species in a general locality. They have learned that many of the desirable tree characteristics...

"Trees and Things That Live in Trees": Three Children with Special Needs Experience the Project Approach

ERIC Educational Resources Information Center

Griebling, Susan; Elgas, Peg; Konerman, Rachel

2015-01-01

The authors report on research conducted during a project investigation undertaken with preschool children, ages 3-5. The report focuses on three children with special needs and the positive outcomes for each child as they engaged in the project Trees and Things That Live in Trees. Two of the children were diagnosed with developmental delays, and…

Water Awareness Through Environmental Restoration

NASA Astrophysics Data System (ADS)

Davis-Caldwell, K.

2012-04-01

This poster will highlight a series of project based activities carried out at Hammond Elementary School in Laurel, Maryland, USA. All of the featured projects revolve around the school's Green School Initiative or an integral part of the science curricula. The Maryland Green School program was developed by a diverse team of educators representing the Maryland Association for Environmental and Outdoor Education (MAEOE), Office of the Governor, the Maryland Association of Student Councils, Maryland Department of Education, Department of Natural Resources and Maryland Department of the Environment. The program is administered through the Maryland Association for Environmental and Outdoor Education. The Maryland Green Schools Award Program recognizes Maryland schools that include environmental education in the curricula, model best management practices at the school and address community environmental issues. Among these numerous projects water is a common thread. Hammond Elementary School lies within the Chesapeake Bay watershed which stretches across 64,000 square miles and encompasses the entire District of Columbia. Educational components address habitats, tributaries and, the estuary system. The projects being highlighted in the poster will include: Trout to Streams Project: This 4th grade project focuses on the natural filtration system that area trout provide to the local and global waterways. As students learn about the importance of various fish to the watershed, they come to understand the effect of changes in the population of fish species due to consumption and pollution. The service learning project highlighted teaches students about water quality as they raise trout eggs and monitor their development into hatching and later stream release. Buffer Streams Tree Planting Projects: This 5th grade science service learning project allows students to investigate the water quality and conditions of local area streams. This project teaches students the positive and negative effects of human presence on the local and global water supply. Student research scientifically tested ways to slow down the effects of run-off contaminants. Students also revisit water analysis and plant trees as buffers as part of their stream preservation efforts in a culminating activity. Oyster Reef Restoration Project: As a result of changes in climate, pollution and human consumption, the oyster population in the Chesapeake Bay had previously been on a rapid decline. The Oyster Reef Restoration Project allows students to understand the creatures of the bay and the cause of this decline. They explore the domino effect this has had on the quality of the water in the bay and future implications on the environment when the oyster population fluctuates significantly. Students construct concrete reefs and study the components of its contents and the reef's impact on the bay. Students are responsible for mixing, pouring and preparing the reef for its eventual drop in the bay. Wetlands Recovery: Following the elimination of a substantial amount of the natural wetlands behind the elementary and middle schools, a wetlands area was erected on the school grounds. This pond has been used to learn about habitats and the role humans, plants and organisms play in the preservation of the earth soil and water supply. This wetland is used by both the elementary and middle schools as a place for hands-on inquiry based learning. Students maintain the upkeep of the pond and teach other students at lower grades.

7 CFR 1214.60 - Programs, plans, and projects.

Code of Federal Regulations, 2014 CFR

2014-01-01

... the image, desirability, or quality of Christmas trees. (b) A program, plan, or project may not be... TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information... Christmas trees; (2) The establishment and conduct of research with respect to the image, desirability, use...

7 CFR 1214.60 - Programs, plans, and projects.

Code of Federal Regulations, 2013 CFR

2013-01-01

... the image, desirability, or quality of Christmas trees. (b) A program, plan, or project may not be... TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information... Christmas trees; (2) The establishment and conduct of research with respect to the image, desirability, use...

7 CFR 1214.60 - Programs, plans, and projects.

Code of Federal Regulations, 2012 CFR

2012-01-01

... the image, desirability, or quality of Christmas trees. (b) A program, plan, or project may not be... TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information... Christmas trees; (2) The establishment and conduct of research with respect to the image, desirability, use...

Forest tree improvement research in the Lake States, 1965.

Treesearch

Paul O. Rudolf

1966-01-01

Gives brief reports on 133 forest tree improvement research projects conducted by 12 agencies and 8 developmental tree improvement projects being conducted by 4 agencies in Michigan, Minnesota, North Dakota, and Wisconsin. Reports the object, species studied, methods, accomplishments, cooperators, and assignment for each project. Includes indexes by genera and...

Comprehensive decision tree models in bioinformatics.

PubMed

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 redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

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

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Multitask visual learning using genetic programming.

PubMed

Jaśkowski, Wojciech; Krawiec, Krzysztof; Wieloch, Bartosz

2008-01-01

We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.

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

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Reinforcement Learning Trees

PubMed Central

Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.

2015-01-01

In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings. PMID:26903687

Building of fuzzy decision trees using ID3 algorithm

NASA Astrophysics Data System (ADS)

Begenova, S. B.; Avdeenko, T. V.

2018-05-01

Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

A Foray into Fungal Ecology: Understanding Fungi and Their Functions Across Ecosystems

NASA Astrophysics Data System (ADS)

Francis, N.; Dunkirk, N. C.; Peay, K.

2015-12-01

Despite their incredible diversity and importance to terrestrial ecosystems, fungi are not included in a standard high school science curriculum. This past summer, however, my work for the Stanford EARTH High School Internship program introduced me to fungal ecology through experiments involving culturing, genomics and root dissections. The two fungal experiments I worked on had very different foci, both searching for answers to broad ecological questions of fungal function and physiology. The first, a symbiosis experiment, sought to determine if the partners of the nutrient exchange between pine trees and their fungal symbionts could choose one another. The second experiment, a dung fungal succession project, compared the genetic sequencing results of fungal extractions from dung versus fungal cultures from dung. My part in the symbiosis experiment involved dissection, weighing and encapsulation of root tissue samples characterized based on the root thickness and presence of ectomycorrhizal fungi. The dung fungi succession project required that I not only learn how to culture various genera of dung fungi but also learn how to extract DNA and RNA for sequencing from the fungal tissue. Although I primarily worked with dung fungi cultures and thereby learned about their unique physiologies, I also learned about the different types of genetic sequencing since the project compared sequences of cultured fungi versus Next Generation sequencing of all fungi present within a dung pellet. Through working on distinct fungal projects that reassess how information about fungi is known within the field of fungal ecology, I learned not only about the two experiments I worked on but also many past related experiments and inquiries through reading scientific papers. Thanks to my foray into fungal research, I now know not only the broader significance of fungi in ecological research but also how to design and conduct ecological experiments.

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

ERIC Educational Resources Information Center

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

2010-01-01

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

The Wish Tree Project

ERIC Educational Resources Information Center

Brooks, Sarah DeWitt

2010-01-01

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

A generalized system of models forecasting Central States tree growth.

Treesearch

Stephen R. Shifley

1987-01-01

Describes the development and testing of a system of individual tree-based growth projection models applicable to species in Indiana, Missouri, and Ohio. Annual tree basal area growth is estimated as a function of tree size, crown ratio, stand density, and site index. Models are compatible with the STEMS and TWIGS Projection System.

A 3,000-year quantitative drought record derived from XRF element data from a south Texas playa

NASA Astrophysics Data System (ADS)

Livsey, D. N.; Simms, A.; Hangsterfer, A.; Nisbet, R.; DeWitt, R.

2013-12-01

Recent droughts throughout the central United States highlight the need for a better understanding of the past frequency and severity of drought occurrence. Current records of past drought for the south Texas coast are derived from tree-ring data that span approximately the last 900 years before present (BP). In this study we utilize a supervised learning routine to create a transfer function between X-Ray Fluorescence (XRF) derived elemental data from Laguna Salada, Texas core LS10-02 to a locally derived tree-ring drought record. From this transfer function the 900 BP tree-ring drought record was extended to 3,000 BP. The supervised learning routine was trained on the first 100 years of XRF element data and tree-ring drought data to create the transfer function and training data set output. The model was then projected from the XRF elemental data for the next 800 years to create a deployed data set output and to test the transfer function parameters. The coefficients of determination between the model output and observed values are 0.77 and 0.70 for the 100-year training data set and 900-year deployed data set respectively. Given the relatively high coefficients of determination for both the training data set and deployed data set we interpret the model parameters are fairly robust and that a high-resolution drought record can be derived from the XRF element data. These results indicate that XRF element data can be used as a quantitative tool to reconstruct past drought records.

Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

ERIC Educational Resources Information Center

Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

2016-01-01

Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

75 FR 64243 - Umatilla National Forest, Walla Walla Ranger District; Oregon Tollgate Fuels Reduction Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-19

... trees; however, larger trees may be removed as necessary to meet project objectives. Activities within... through the removal of standing (live and dead) trees and dead and down material. These activities would...

Metabolite identification through multiple kernel learning on fragmentation trees.

PubMed

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Forest Tree Improvement Research in the South and Southeast

Treesearch

Keith W. Dorman

1966-01-01

The Committee on Southern Forest Tree improvement has directed its Subcommittee on Tree Selection and Breeding to summarize the projects by agencies doing forest tree improvement research in the South and Southeast. This area corresponds roughly to what is known as the southern pine region. The project summaries and the consolidated report were to be patterned after...

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction.

PubMed

Hajiloo, Mohsen; Sapkota, Yadav; Mackey, John R; Robson, Paula; Greiner, Russell; Damaraju, Sambasivarao

2013-02-22

Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual's continental and sub-continental ancestry. To predict an individual's continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of ≥ 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control's λ from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% ± 2.4%, 95.6% ± 3.9%, 95.6% ± 2.1%, 98.3% ± 2.0%, and 95.9% ± 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver. ETHNOPRED is a novel technique for producing classifiers that can identify an individual's continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values.

Visualizing Biological Data in Museums: Visitor Learning with an Interactive Tree of Life Exhibit

ERIC Educational Resources Information Center

Horn, Michael S.; Phillips, Brenda C.; Evans, Evelyn Margaret; Block, Florian; Diamond, Judy; Shen, Chia

2016-01-01

In this study, we investigate museum visitor learning and engagement at an interactive visualization of an evolutionary tree of life consisting of over 70,000 species. The study was conducted at two natural history museums where visitors collaboratively explored the tree of life using direct touch gestures on a multi-touch tabletop display. In the…

Tree cover and aridity projections to 2060: a technical document supporting the Forest Service 2010 RPA assessment

Treesearch

Eric J. Greenfield; David J. Nowak

2013-01-01

Future projections of tree cover and climate change are useful to natural resource managers as they illustrate potential changes to our natural resources and the ecosystem services they provide. This report a) details three projections of tree cover change across the conterminous United States based on predicted land-use changes from 2000 to 2060; b) evaluates nine...

Using existing programs as vehicles to disseminate knowledge, provide opportunities for scientists to assist educators, and to engage students in using real data

NASA Astrophysics Data System (ADS)

Smith, S. C.; Wegner, K.; Branch, B. D.; Miller, B.; Schulze, D. G.

2013-12-01

Many national and statewide programs throughout the K-12 science education environment teach students about science in a hands-on format, including programs such as Global Learning and Observations to Benefit the Environment (GLOBE), Project Learning Tree (PLT), Project Wild, Project Wet, and Hoosier River Watch. Partnering with one or more of these well-known programs can provide many benefits to both the scientists involved in disseminating research and the K-12 educators. Scientists potentially benefit by broader dissemination of their research by providing content enrichment for educators. Educators benefit by gaining understanding in content, becoming more confident in teaching the concept, and increasing their enthusiasm in teaching the concepts addressed. This presentation will discuss an innovative framework for professional development that was implemented at Purdue University, Indiana in July 2013. The professional development incorporated GLOBE protocols with iPad app modules and interactive content sessions from faculty and professionals. By collaborating with the GLOBE program and scientists from various content areas, the Department of Earth, Atmospheric, and Planetary Sciences at Purdue University successfully facilitated a content rich learning experience for educators. Such activity is promoted and supported by Purdue University Libraries where activities such as Purdue's GIS Day are efforts of making authentic learning sustainable in the State of Indiana and for national consideration. Using iPads to visualize soil transitions on a field trip. Testing Water quality in the field.

Planting forests in NYC: Is the goal restoration, reforestation, or afforestation?

Treesearch

R.A. Hallett

2013-01-01

Chicago, Los Angeles, Philadelphia, Detroit, New York City (NYC) and many other cities in the United States and around the world are engaging in urban greening projects. Urban greening almost always involves planting trees... lots of trees. New York City, for example, has planted over 750,000 trees to date as part of a project that started in 2007 – a project with the...

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

77 FR 42694 - Helena National Forest, Montana, Telegraph Vegetation Project

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-20

... slashing generally small diameter trees followed by prescribed burning within the Jericho Mountain... dead and dying trees, promoting desirable regeneration, reducing fuels and the risk of wildfire, and... for Action Wide-scale tree mortality has occurred throughout the project area due to the mountain pine...

Using Narrative-Based Design Scaffolds within a Mobile Learning Environment to Support Learning Outdoors with Young Children

ERIC Educational Resources Information Center

Seely, Brian J.

2015-01-01

This study aims to advance learning outdoors with mobile devices. As part of the ongoing Tree Investigators design-based research study, this research investigated a mobile application to support observation, identification, and explanation of the tree life cycle within an authentic, outdoor setting. Recognizing the scientific and conceptual…

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

PubMed

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

PubMed Central

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context. PMID:20113515

A Study of Building a Resource-Based Learning Environment with the Inquiry Learning Approach: Knowledge of Family Trees

ERIC Educational Resources Information Center

Kong, Siu Cheung; So, Wing Mui Winnie

2008-01-01

This study aims to provide teachers with ways and means to facilitate learners to develop nomenclature knowledge of family trees through the establishment of resource-based learning environments (RBLEs). It discusses the design of an RBLE in the classroom by selecting an appropriate context with the assistance of computer-mediated learning…

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty.

PubMed

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.

An ecoregion assessment of projected tree species vulnerabilities in western North America through the 21st century.

PubMed

Mathys, Amanda S; Coops, Nicholas C; Waring, Richard H

2017-02-01

Forest ecosystems across western North America will likely see shifts in both tree species dominance and composition over the rest of this century in response to climate change. Our objective in this study was to identify which ecological regions might expect the greatest changes to occur. We used the process-based growth model 3-PG, to provide estimates of tree species responses to changes in environmental conditions and to evaluate the extent that species are resilient to shifts in climate over the rest of this century. We assessed the vulnerability of 20 tree species in western North America using the Canadian global circulation model under three different emission scenarios. We provided detailed projections of species shifts by including soil maps that account for the spatial variation in soil water availability and soil fertility as well as by utilizing annual climate projections of monthly changes in air temperature, precipitation, solar radiation, vapor pressure deficit and frost at a spatial resolution of one km. Projected suitable areas for tree species were compared to their current ranges based on observations at >40 000 field survey plots. Tree species were classified as vulnerable if environmental conditions projected in the future appear outside that of their current distribution ≥70% of the time. We added a migration constraint that limits species dispersal to <200 m yr -1 to provide more realistic projections on species distributions. Based on these combinations of constraints, we predicted the greatest changes in the distribution of dominant tree species to occur within the Northwest Forested Mountains and the highest number of tree species stressed will likely be in the North American Deserts. Projected climatic changes appear especially unfavorable for species in the subalpine zone, where major shifts in composition may lead to the emergence of new forest types. © 2016 John Wiley & Sons Ltd.

B-tree search reinforcement learning for model based intelligent agent

NASA Astrophysics Data System (ADS)

Bhuvaneswari, S.; Vignashwaran, R.

2013-03-01

Agents trained by learning techniques provide a powerful approximation of active solutions for naive approaches. In this study using B - Trees implying reinforced learning the data search for information retrieval is moderated to achieve accuracy with minimum search time. The impact of variables and tactics applied in training are determined using reinforcement learning. Agents based on these techniques perform satisfactory baseline and act as finite agents based on the predetermined model against competitors from the course.

Scalable Regression Tree Learning on Hadoop using OpenPlanet

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yin, Wei; Simmhan, Yogesh; Prasanna, Viktor

As scientific and engineering domains attempt to effectively analyze the deluge of data arriving from sensors and instruments, machine learning is becoming a key data mining tool to build prediction models. Regression tree is a popular learning model that combines decision trees and linear regression to forecast numerical target variables based on a set of input features. Map Reduce is well suited for addressing such data intensive learning applications, and a proprietary regression tree algorithm, PLANET, using MapReduce has been proposed earlier. In this paper, we describe an open source implement of this algorithm, OpenPlanet, on the Hadoop framework usingmore » a hybrid approach. Further, we evaluate the performance of OpenPlanet using realworld datasets from the Smart Power Grid domain to perform energy use forecasting, and propose tuning strategies of Hadoop parameters to improve the performance of the default configuration by 75% for a training dataset of 17 million tuples on a 64-core Hadoop cluster on FutureGrid.« less

Looking at the Trees around Us

ERIC Educational Resources Information Center

Bellous, Karen

2004-01-01

This article describes a tree project undertaken by a class of 5- to 7-year-old children in the Child Study Centre at the University of Alberta. Following a description of the school and the children, the article discusses how the project evolved and discusses the three phases of the project. Photographs taken during the project are included.

Projecting a Stand Table Through Time

Treesearch

Quang V. Cao; V. Clark Baldwin

1999-01-01

Stand tables provide number of trees per acre for each diameter class. This paper presents a general technique to predict a future stand table, based on the current stand table and future stand summary statistics such as trees and basal area per acre, and average diameter. The stand projection technique involves (a) predicting surviving trees for each class, and (b)...

Global climate change will increase the abundance of symbiotic nitrogen-fixing trees in much of North America.

PubMed

Liao, Wenying; Menge, Duncan N L; Lichstein, Jeremy W; Ángeles-Pérez, Gregorio

2017-11-01

Symbiotic nitrogen (N)-fixing trees can drive N and carbon cycling and thus are critical components of future climate projections. Despite detailed understanding of how climate influences N-fixation enzyme activity and physiology, comparatively little is known about how climate influences N-fixing tree abundance. Here, we used forest inventory data from the USA and Mexico (>125,000 plots) along with climate data to address two questions: (1) How does the abundance distribution of N-fixing trees (rhizobial, actinorhizal, and both types together) vary with mean annual temperature (MAT) and precipitation (MAP)? (2) How will changing climate shift the abundance distribution of N-fixing trees? We found that rhizobial N-fixing trees were nearly absent below 15°C MAT, but above 15°C MAT, they increased in abundance as temperature rose. We found no evidence for a hump-shaped response to temperature throughout the range of our data. Rhizobial trees were more abundant in dry than in wet ecosystems. By contrast, actinorhizal trees peaked in abundance at 5-10°C MAT and were least abundant in areas with intermediate precipitation. Next, we used a climate-envelope approach to project how N-fixing tree relative abundance might change in the future. The climate-envelope projection showed that rhizobial N-fixing trees will likely become more abundant in many areas by 2080, particularly in the southern USA and western Mexico, due primarily to rising temperatures. Projections for actinorhizal N-fixing trees were more nuanced due to their nonmonotonic dependence on temperature and precipitation. Overall, the dominant trend is that warming will increase N-fixing tree abundance in much of the USA and Mexico, with large increases up to 40° North latitude. The quantitative link we provide between climate and N-fixing tree abundance can help improve the representation of symbiotic N fixation in Earth System Models. © 2017 John Wiley & Sons Ltd.

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction

PubMed Central

2013-01-01

Background Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case–control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. Results We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual’s continental and sub-continental ancestry. To predict an individual’s continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of ≥ 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control’s λ from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% ± 2.4%, 95.6% ± 3.9%, 95.6% ± 2.1%, 98.3% ± 2.0%, and 95.9% ± 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver. Conclusions ETHNOPRED is a novel technique for producing classifiers that can identify an individual’s continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values. PMID:23432980

Its Seat Is in the Heart.

ERIC Educational Resources Information Center

Mesplay, Gail

2001-01-01

Presents several practical ideas for making peace a priority within the classroom. Shares stories of a high school and an elementary school where peace projects have flourished. The elementary project involved planting a tree germinated from a Japanese tree that had survived the atomic bomb. The high school project involved apprenticing teenagers…

A New Framework for Adaptive Sampling and Analysis During Long-Term Monitoring and Remedial Action Management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minsker, Barbara

2004-12-01

The Argonne team has gathered available data on monitoring wells and measured hydraulic heads from the Argonne 317/319 site and sent it to UIUC. Xiaodong Li, a research assistant supported by the project, has reviewed the data and has fit initial spatiotemporal statistical models to it. Another research assistant, Yonas Demissie, has completed generation of the artificial data that will be used for model development and testing. In order to generate the artificial data a detailed groundwater flow and contaminant transport model was developed based upon characteristics of the 317/319 site. The model covers a multi-year time horizon that includesmore » both before and after planting of the trees. As described in the proposal, the artificial data is created by adding ''measurement'' error to the ''true'' value from the numerical model. To date, only simple white noise error models have been considered. He is now reviewing the literature and beginning to develop a hierarchical modeling approach for the artificial data. Abhishek Singh, a third research assistant supported by the project, is implementing learning models for learning users preferences in an interactive genetic algorithm for solving the inverse problem. Meghna Babbar, the fourth research assistant supported by the project, has been improving the user interface for the interactive genetic algorithm and preparing a long-term monitoring design problem for testing the approach. Gayathri Gopalakrishnan, the last research assistant who is partially supported by the project, has collected substantial data from the 317/319 phytoremediation site at Argonne and has begun learning approaches for modeling these data.« less

An evaluation of the STEMS tree growth projection system.

Treesearch

Margaret R. Holdaway; Gary J. Brand

1983-01-01

STEMS (Stand and Tree Evaluation and Modeling System) is a tree growth projection system. This paper (1) compares the performance of the current version of STEMS developed for the Lake States with that of the original model and (2) reports the results of an analysis of the current model over a wide range of conditions and identifies its main strengths and weaknesses...

Project CAPTURE: a U.S. national prioritization assessment of tree species for conservation, management, and restoration

Treesearch

Kevin M. Potter; Barbara S. Crane; Valerie D. Hipkins

2017-01-01

that forest tree species will undergo population-level extirpation or species-level extinction during the next century. Project CAPTURE (Conservation Assessment and Prioritization of Forest Trees Under Risk of Extirpation) is a cooperative effort across the three U.S. Department of Agriculture Forest Service (USDA FS) deputy areas to establish a framework for...

Project CAPTURE: using forest inventory and analysis data to prioritize tree species for conservation, management, and restoration

Treesearch

Kevin M. Potter; Barbara S. Crane; William W. Hargrove

2015-01-01

A variety of threats, most importantly climate change and insect and disease infestation, will increase the likelihood that forest tree species could experience population-level extirpation or species-level extinction during the next century. Project CAPTURE (Conservation Assessment and Prioritization of Forest Trees Under Risk of Extirpation) is a cooperative effort...

Living Classrooms: Learning Guide for Famous & Historic Trees.

ERIC Educational Resources Information Center

American Forest Foundation, Washington, DC.

This guide provides information to create and care for a Famous and Historic Trees Living Classroom in which students learn American history and culture in the context of environmental change. The booklet contains 10 hands-on activities that emphasize observation, critical thinking, and teamwork. Worksheets and illustrations provide students with…

How to Identify and Interpret Evolutionary Tree Diagrams

ERIC Educational Resources Information Center

Kong, Yi; Anderson, Trevor; Pelaez, Nancy

2016-01-01

Evolutionary trees are key tools for modern biology and are commonly portrayed in textbooks to promote learning about biological evolution. However, many people have difficulty in understanding what evolutionary trees are meant to portray. In fact, some ideas that current professional biologists depict with evolutionary trees are neither clearly…

75 FR 48927 - Sierra National Forest, Bass Lake Ranger District, California, Fish Camp Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-12

... stocked and thinning is needed. This thinning is needed to reduce inter-tree competition and improve tree... densities are above what can be sustained, with inter-tree competition increasing and tree vigor beginning... communities and reduce inter tree competition to improve tree vigor and increase stand resistance to drought...

Totally Tree-mendous Activities: Projects To Discover the Beauty and Benefits of Trees.

ERIC Educational Resources Information Center

Hollister, Sarah

This teacher's guide supplies information and hands-on activities to teach about trees from several disciplines. Activities are grouped into six areas that cover botany, social studies, arts and literature (aesthetics), and trees as a resource. Sections include: (1) Tree Identification, which defines trees and leaves and presents activities that…

Oak Tree Planting Project

Treesearch

Sherryl L. Nives; William D. Tietje; William H. Weitkamp

1991-01-01

An Oak Tree Planting Project was conducted during 1989/90 in San Luis Obispo County by the Integrated Hardwood Range Management Program (IHRMP)/Central Coast. The local media and an IHRMP workshop were used to publicize the Planting Project and give information on the status of oaks (Quercus spp.) in California and oak planting techniques. Outreach...

Secondhand Trees, Firsthand Learning. Holiday Evergreens Revitalized.

ERIC Educational Resources Information Center

Graves, C. John

1990-01-01

Described is an activity that uses discarded evergreen trees from Christmas. Tree age and growth characteristics are investigated by looking at the number of whorls and rings of the trunks. Extensions and follow-up activities are included. (KR)

Projected tree species redistribution under climate change: Implications for ecosystem vulnerability across protected areas in the eastern United States

Treesearch

Scott G. Zolkos; Patrick Jantz; Tina Cormier; Louis R. Iverson; Daniel W. McKenney; Scott J. Goetz

2015-01-01

The degree to which tree species will shift in response to climate change is uncertain yet critical to understand for assessing ecosystem vulnerability. We analyze results from recent studies that model potential tree species habitat across the eastern United States during the coming century. Our goals were to quantify and spatially analyze habitat projections and...

The user's guide to STEMS (Stand and Tree Evaluation and Modeling System).

Treesearch

David M. Belcher

1981-01-01

Presents the structure of STEMS, a computer program for projecting growth of individual trees within the Lake States Region, and discusses its input, processing, major subsystems, and output. Includes an example projection.

Projecting range-wide sun bear population trends using tree cover and camera-trap bycatch data.

PubMed

Scotson, Lorraine; Fredriksson, Gabriella; Ngoprasert, Dusit; Wong, Wai-Ming; Fieberg, John

2017-01-01

Monitoring population trends of threatened species requires standardized techniques that can be applied over broad areas and repeated through time. Sun bears Helarctos malayanus are a forest dependent tropical bear found throughout most of Southeast Asia. Previous estimates of global population trends have relied on expert opinion and cannot be systematically replicated. We combined data from 1,463 camera traps within 31 field sites across sun bear range to model the relationship between photo catch rates of sun bears and tree cover. Sun bears were detected in all levels of tree cover above 20%, and the probability of presence was positively associated with the amount of tree cover within a 6-km2 buffer of the camera traps. We used the relationship between catch rates and tree cover across space to infer temporal trends in sun bear abundance in response to tree cover loss at country and global-scales. Our model-based projections based on this "space for time" substitution suggested that sun bear population declines associated with tree cover loss between 2000-2014 in mainland southeast Asia were ~9%, with declines highest in Cambodia and lowest in Myanmar. During the same period, sun bear populations in insular southeast Asia (Malaysia, Indonesia and Brunei) were projected to have declined at a much higher rate (22%). Cast forward over 30-years, from the year 2000, by assuming a constant rate of change in tree cover, we projected population declines in the insular region that surpassed 50%, meeting the IUCN criteria for endangered if sun bears were listed on the population level. Although this approach requires several assumptions, most notably that trends in abundance across space can be used to infer temporal trends, population projections using remotely sensed tree cover data may serve as a useful alternative (or supplement) to expert opinion. The advantages of this approach is that it is objective, data-driven, repeatable, and it requires that all assumptions be clearly stated.

Not Just a Fall Tree

ERIC Educational Resources Information Center

Miller-Hewes, Kathy A.

2004-01-01

Trees burst with color in the northern states. Autumn leaves dust the ground. Painting the fall landscape is nothing new. Teachers have been doing it in classrooms for decades. The approach, however, can make the difference between whether the fall landscape is simply painting for fun, or a real learning experience. Students learn best when they…

National Forest Health Monitoring Program Maryland and Massachusetts Street Tree Monitoring Pilot Projects

Treesearch

Buckelew Cumming Anne; Daniel Twardus; William Smith

2006-01-01

Urban forests have many components: park trees, small woodlands, riparian buffers, street trees, and others. While some communities conduct city-wide inventories of street tree populations, there has been no comprehensive, statewide sampling to characterize the structure, health, and function of street tree populations. A statewide Street Tree Monitoring pilot study...

Community Tree Planting Guide

Treesearch

Katie Himanga; Douglas Jones; Jean Miller; Janette Monear; Gail Steinman; Katherine Widin

2001-01-01

Tree Trust has been helping people plant trees in their communities since 1976. Our goal is to educate people about the importance of trees in their community and guide them through the process of successful tree-planting projects. Franklin Delano Roosevelt once said ?to exist as a nation, to prosper as a state, and to live as a people, we must have trees?....

A key for the Forest Service hardwood tree grades

Treesearch

Gary W. Miller; Leland F. Hanks; Harry V., Jr. Wiant

1986-01-01

A dichotomous key organizes the USDA Forest Service hardwood tree grade specifications into a stepwise procedure for those learning to grade hardwood sawtimber. The key addresses the major grade factors, tree size, surface characteristics, and allowable cull deductions in a series of paried choices that lead the user to a decision regarding tree grade.

Chicago's urban forest ecosystem: results of the Chicago Urban Forest Climate Project

Treesearch

Gregory E. McPherson; David J. Nowak; Rowan A. Rowntree

1994-01-01

Results of the 3-year Chicago Urban Forest Climate Project indicate that there are an estimated 50.8 million trees in the Chicago area of Cook and DuPage Counties; 66 percent of these trees rated in good or excellent condition. During 1991, trees in the Chicago area removed an estimated 6,145 tons of air pollutants, providing air cleansing valued at $9.2 million...

Summary of tree-breeding experiments by The Northeastern Forest Experiment Station 1947-1950

Treesearch

Jonathan W. Wright

1953-01-01

The tree-breeding work of the Northeastern Forest Experiment Station has its roots in a project started in 1924 by the Oxford Paper Company of Rumford, Maine, to develop fast-growing poplars that would be suitable for pulpwood. The initial tree-breeding work in this project was done by A. B. Stout and Ernst J. Schreiner, most of it at the New York Botanical Garden and...

An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea.

PubMed

McDonald, Daniel; Price, Morgan N; Goodrich, Julia; Nawrocki, Eric P; DeSantis, Todd Z; Probst, Alexander; Andersen, Gary L; Knight, Rob; Hugenholtz, Philip

2012-03-01

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a 'taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408,315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.

An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

PubMed Central

McDonald, Daniel; Price, Morgan N; Goodrich, Julia; Nawrocki, Eric P; DeSantis, Todd Z; Probst, Alexander; Andersen, Gary L; Knight, Rob; Hugenholtz, Philip

2012-01-01

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/. PMID:22134646

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

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.

Undergraduate Students’ Difficulties in Reading and Constructing Phylogenetic Tree

NASA Astrophysics Data System (ADS)

Sa'adah, S.; Tapilouw, F. S.; Hidayat, T.

2017-02-01

Representation is a very important communication tool to communicate scientific concepts. Biologists produce phylogenetic representation to express their understanding of evolutionary relationships. The phylogenetic tree is visual representation depict a hypothesis about the evolutionary relationship and widely used in the biological sciences. Phylogenetic tree currently growing for many disciplines in biology. Consequently, learning about phylogenetic tree become an important part of biological education and an interesting area for biology education research. However, research showed many students often struggle with interpreting the information that phylogenetic trees depict. The purpose of this study was to investigate undergraduate students’ difficulties in reading and constructing a phylogenetic tree. The method of this study is a descriptive method. In this study, we used questionnaires, interviews, multiple choice and open-ended questions, reflective journals and observations. The findings showed students experiencing difficulties, especially in constructing a phylogenetic tree. The students’ responds indicated that main reasons for difficulties in constructing a phylogenetic tree are difficult to placing taxa in a phylogenetic tree based on the data provided so that the phylogenetic tree constructed does not describe the actual evolutionary relationship (incorrect relatedness). Students also have difficulties in determining the sister group, character synapomorphy, autapomorphy from data provided (character table) and comparing among phylogenetic tree. According to them building the phylogenetic tree is more difficult than reading the phylogenetic tree. Finding this studies provide information to undergraduate instructor and students to overcome learning difficulties of reading and constructing phylogenetic tree.

Determinants of establishment survival for residential trees in Sacramento County, CA

Treesearch

Lara A. Roman; John J. Battles; Joe R. McBride

2014-01-01

Urban forests can provide ecosystem services that motivate tree planting campaigns, and tree survival is a key element of program success and projected benefits. We studied survival in a shade tree give-away program in Sacramento, CA, monitoring a cohort of young trees for five years on single-family residential properties. We used conditional inference trees to...

FORAST Database: Forest Responses to Anthropogenic Stress (FORAST)

DOE Data Explorer

McLaughlin, S. B. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Downing, D. J. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Blasing, T. J. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Jackson, B. L. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Pack, D. J. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Duvick, D. N. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Mann, L. K. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Doyle, T. W. [ESD, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA)

1995-01-01

The Forest Responses to Anthropogenic Stress (FORAST) project was designed to determine whether evidence of alterations of long-term growth patterns of several species of eastern forest trees was apparent in tree-ring chronologies from within the region and to identify environmental variables that were temporally or spatially correlated with any observed changes. The project was supported principally by the U.S. Environmental Protection Agency (EPA) with additional support from the National Park Service. The FORAST project was initiated in 1982 as exploratory research to document patterns of radial growth of forest trees during the previous 50 or more years within 15 states in the northeastern United States. Radial growth measurements from more than 7,000 trees are provided along with data on a variety of measured and calculated indices of stand characteristics (basal area, density, and competitive indices); climate (temperature, precipitation, and drought); and anthropogenic pollutants (state and regional emissions of SO2 and NOX, ozone monitoring data, and frequency of atmospheric-stagnation episodes and atmospheric haze). These data were compiled into a single database to facilitate exploratory analysis of tree growth patterns and responses to local and regional environmental conditions. The project objectives, experimental design, and documentation of procedures for assessing data collected in the 3-year research project are reported in McLaughlin et al. (1986).

Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

2000-01-01

Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

Technical Manual for the Conceptual Learning and Development Assessment Series IV: Tree. Technical Report No. 437. Reprinted December 1977.

ERIC Educational Resources Information Center

DiLuzio, Geneva J.; And Others

This document accompanies the Conceptual Learning and Development Assessment Series III: Tree, a test constructed to chart the conceptual development of individuals. As a technical manual, it contains information on the rationale, development, standardization, and reliability of the test, as well as essential information and statistical data for…

Urban tree mortality: a primer on demographic approaches

Treesearch

Lara A. Roman; John J. Battles; Joe R. McBride

2016-01-01

Realizing the benefits of tree planting programs depends on tree survival. Projections of urban forest ecosystem services and cost-benefit analyses are sensitive to assumptions about tree mortality rates. Long-term mortality data are needed to improve the accuracy of these models and optimize the public investment in tree planting. With more accurate population...

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Using Tree-Ring Data to Develop Critical Scientific and Mathematical Thinking Skills in Undergraduate Students

NASA Astrophysics Data System (ADS)

Fiondella, F.; Davi, N. K.; Wattenberg, F.; Pringle, P. T.; Greidanus, I.; Oelkers, R.

2015-12-01

Tree-ring science provides an engaging, intuitive, and relevant entryway into understanding both climate change and environmental research. It also sheds light on the process of science--from inspiration, to fieldwork, to analysis, to publishing and communication. The basic premise of dendrochronology is that annual rings reflect year-to-year environmental conditions and that by studying long-lived trees we can learn about environmental and climatic conditions going back hundreds to thousands of years. Conceptually, this makes tree-ring studies accessible to students and faculty for a number of reasons. First, in order to collect their data, dendrochronologists often launch expeditions to stunningly picturesque and remote places in search of long-lived, climate sensitive trees. The exciting stories and images that scientists bring back from the field can help connect students to the studies, their motivation, and the data collected. Second, tree rings can be more easily explained as a proxy for climate than ice cores, speleothems and others. Most people have prior knowledge about trees and annual growth rings. It is even possible, for example, for non-expert audiences to see climate variability through time with the naked eye by looking at climate-sensitive tree cores. Third, tree rings are interdisciplinary and illustrate the interplay between the mathematical sciences, the biological sciences, and the geosciences—that is, they show that the biosphere is a fundamental component of the Earth system. Here, we present online, multi-media learning modules for undergraduates that introduce students to several foundational studies in tree-ring science. These include evaluating tree-ring cores from ancient hemlock trees growing on a talus slope in New Paltz, NY to learn about drought in the Northeastern US, evaluating long-term streamflow and drought of the Colorado River based on tree-ring records, and using tree-ring dating techniques to develop construction histories of cliff dwellings and pueblos in the US Southwest. Our modules are designed to give undergraduate students a sense of the scientific process, from fieldwork and logistics, to data processing and data analysis.

Understanding the challenges of municipal tree planting

Treesearch

E.G. McPherson; R. Young

2010-01-01

Nine of the twelve largest cities in the U.S. have mayoral tree planting initiatives (TPIs), with pledges to plant nearly 20 million trees. Although executive-level support for trees has never been this widespread, many wonder if this support will endure as administrations change and budgets tighten. In an effort to share lessons learned from successes and setbacks, a...

Looking/Learning Drawing: Trees.

ERIC Educational Resources Information Center

Hurwitz, Al; Blume, Sharon

1985-01-01

Secondary students are asked to study and compare three reproductions--Van Gogh's "Grove of Cypresses," Da Vinci's "Study of a Tree," and Mondrian's "Tree II." The activity will help students develop their powers of observation and analysis, powers that can be applied to their own drawings. (RM)

Tree mortality rates and tree population projections in Baltimore, Maryland, USA

Treesearch

David J. Nowak; Miki Kuroda; Daniel E. Crane

2004-01-01

Based on re-measurements (1999 and 2001) of randomly-distributed permanent plots within the city boundaries of Baltimore, Maryland, trees are estimated to have an annual mortality rate of 6.6% with an overall annual net change in the number of live trees of -4.2%. Tree mortality rates were significantly different based on tree size, condition, species, and Land use....

Context Inference for Mobile Applications in the UPCASE Project

NASA Astrophysics Data System (ADS)

Santos, André C.; Tarrataca, Luís; Cardoso, João M. P.; Ferreira, Diogo R.; Diniz, Pedro C.; Chainho, Paulo

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

Potential energy savings in buildings by an urban tree planting programme in California

Treesearch

E.G. McPherson; J.R. Simpson

2003-01-01

Tree canopy cover data from aerial photographs and building energy simulations were applied to estimate energy savings from existing trees and new plantings in California. There are approximately 177.3 million energy-conserving trees in California communities and 241.6 million empty planting sites. Existing trees are projected to reduce annual air conditioning energy...

A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

ERIC Educational Resources Information Center

Poe, Retta E.

1988-01-01

Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Using Educational Games and Simulation Software in a Computer Science Course: Learning Achievements and Student Flow Experiences

ERIC Educational Resources Information Center

Liu, Tsung-Yu

2016-01-01

This study investigates how educational games impact on students' academic performance and multimedia flow experiences in a computer science course. A curriculum consists of five basic learning units, that is, the stack, queue, sort, tree traversal, and binary search tree, was conducted for 110 university students during one semester. Two groups…

The Learning Tree Montessori Child Care: An Approach to Diversity

ERIC Educational Resources Information Center

Wick, Laurie

2006-01-01

In this article the author describes how she and her partners started The Learning Tree Montessori Child Care, a Montessori program with a different approach in Seattle in 1979. The author also relates that the other area Montessori schools then offered half-day programs, and as a result the children who attended were, for the most part,…

Automated identification and geometrical features extraction of individual trees from Mobile Laser Scanning data in Budapest

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Székely, Balázs; Folly-Ritvay, Zoltán; Skobrák, Ferenc; Koenig, Kristina; Höfle, Bernhard

2016-04-01

Mobile Laser Scanning (MLS) is an evolving operational measurement technique for urban environment providing large amounts of high resolution information about trees, street features, pole-like objects on the street sides or near to motorways. In this study we investigate a robust segmentation method to extract the individual trees automatically in order to build an object-based tree database system. We focused on the large urban parks in Budapest (Margitsziget and Városliget; KARESZ project) which contained large diversity of different kind of tree species. The MLS data contained high density point cloud data with 1-8 cm mean absolute accuracy 80-100 meter distance from streets. The robust segmentation method contained following steps: The ground points are determined first. As a second step cylinders are fitted in vertical slice 1-1.5 meter relative height above ground, which is used to determine the potential location of each single trees trunk and cylinder-like object. Finally, residual values are calculated as deviation of each point from a vertically expanded fitted cylinder; these residual values are used to separate cylinder-like object from individual trees. After successful parameterization, the model parameters and the corresponding residual values of the fitted object are extracted and imported into the tree database. Additionally, geometric features are calculated for each segmented individual tree like crown base, crown width, crown length, diameter of trunk, volume of the individual trees. In case of incompletely scanned trees, the extraction of geometric features is based on fitted circles. The result of the study is a tree database containing detailed information about urban trees, which can be a valuable dataset for ecologist, city planners, planting and mapping purposes. Furthermore, the established database will be the initial point for classification trees into single species. MLS data used in this project had been measured in the framework of KARESZ project for whole Budapest. BSz contributed as an Alexander von Humboldt Research Fellow.

Concept Model on Topological Learning

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Kioi, Kazumasa

2010-11-01

We discuss a new model for concept based on topological learning, where the learning process on the neural network is represented by mathematical topology. The topological learning of neural networks is summarized by a quotient of input space and the hierarchical step induces a tree where each node corresponds to a quotient. In general, the concept acquisition is a difficult problem, but the emotion for a subject is represented by providing the questions to a person. Therefore, a kind of concept is captured by such data and the answer sheet can be mapped into a topology consisting of trees. In this paper, we will discuss a way of mapping the emotional concept to a topological learning model.

Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodruff, Katherine

MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is tomore » combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.« less

Ailanthus, tree-of-heaven update, a northeast regional biological control project

Treesearch

Scott M. Salom; Loke T. Kok; Nathan Herrick; Tom McAvoy; Donald Davis; Mark Schall; Matt Kasson; Du Yu-Zhou; Ji Hailong; He Xiao; Richard Reardon

2009-01-01

The tree-of-heaven (TOH), Ailanthus altissima (Mill.) Swingle (Sapindales: Simaroubaceae), is an invasive weed tree distributed throughout most of the continental United States. It is a hardy pioneer species that...

A Spring Playscape Project: Building a Tree Circle

ERIC Educational Resources Information Center

Keeler, Rusty

2009-01-01

The Tree Circle is a green gathering area for children made by planting trees in a circle. For children, the Tree Circle becomes a magical place for dramatic play, quiet retreat, or lively nature exploration. For teachers and parents it becomes a shady grove for snacks and stories. The trees create a sweet spot that changes during the seasons and…

The national tree-list layer

Treesearch

Stacy A. Drury; Jason M. Herynk

2011-01-01

The National Tree-List Layer (NTLL) project used LANDFIRE map products to produce the first national tree-list map layer that represents tree populations at stand and regional levels. The NTLL was produced in a short time frame to address the needs of Fire and Aviation Management for a map layer that could be used as input for simulating fire-caused tree mortality...

Exploration on Construction of Hospital "Talent Tree" Project.

PubMed

Yi, Lihua; Wei, Lei; Hao, Aimin; Hu, Minmin; Xu, Xinzhou

2015-05-01

Talent is the core competitive force of a hospital's development. Wuxi No. 2 People's Hospital followed the characteristics that medical talents mature slowly and their growth requires a long period. The innovated "talent tree" project, trained classified talents corresponding to "base-trunk-crown" of a tree, formed an individualized professional training plan with different levels and at different periods. We carried out a relay of the "talent tree" to bring their initiative into play. In practice, we gradually found this as a unique way of the talent construction, which conforms to our hospital's condition. This guarantees sustained development and innovative force of the hospital.

Assessment of Student Learning Associated with Tree Thinking in an Undergraduate Introductory Organismal Biology Course

ERIC Educational Resources Information Center

Smith, James J.; Cheruvelil, Kendra Spence; Auvenshine, Stacie

2013-01-01

Phylogenetic trees provide visual representations of ancestor-descendant relationships, a core concept of evolutionary theory. We introduced "tree thinking" into our introductory organismal biology course (freshman/sophomore majors) to help teach organismal diversity within an evolutionary framework. Our instructional strategy consisted…

Talking Trees

ERIC Educational Resources Information Center

Tolman, Marvin

2005-01-01

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

A systematic risk management approach employed on the CloudSat project

NASA Technical Reports Server (NTRS)

Basilio, R. R.; Plourde, K. S.; Lam, T.

2000-01-01

The CloudSat Project has developed a simplified approach for fault tree analysis and probabilistic risk assessment. A system-level fault tree has been constructed to identify credible fault scenarios and failure modes leading up to a potential failure to meet the nominal mission success criteria.

Newquay Treviglas School.

ERIC Educational Resources Information Center

Ingham, Donald

1995-01-01

Describes a long-term scheme to develop a pond, nature trail, and tree-planting project (in Cornwall, England). The project was designed by teams of students. Plans included a large pond, meadow area, sequential cuttings of school fields to encourage insects, butterfly garden, extensive tree plantings (including a dwindling native species), and a…

3D Visualization of Machine Learning Algorithms with Astronomical Data

NASA Astrophysics Data System (ADS)

Kent, Brian R.

2016-01-01

We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

Peanut Allergy

MedlinePlus

... of reactions. Learn more here. Milk Egg Peanut Tree Nuts Soy Wheat Fish Shellfish Sesame Other Food ... food allergies. Peanuts are not the same as tree nuts (almonds, cashews, walnuts, etc.), which grow on ...

Supersonic air jets preserve tree roots in underground pipeline installation

Treesearch

Rob Gross; Michelle Julene

2002-01-01

Tree roots are often damaged during construction projects, particularly during trenching operations for pipeline installation. Although mechanical soil excavation using heavy equipment, such as an excavator or backhoe is considered the fastest the most economical method, it damages and destroys tree roots and can lead to unintentional tree loss, poor public relations,...

How to select the best tree planting locations to enhance air pollution removal in the MillionTreesNYC initiative

Treesearch

Arianna Morani; David J. Nowak; Satoshi Hirabayashi; Carlo Calfapietra

2011-01-01

Highest priority zones for tree planting within New York City were selected by using a planting priority index developed combining three main indicators: pollution concentration, population density and low canopy cover. This new tree population was projected through time to estimate potential air quality and carbon bene!ts. Those trees will likely remove more than 10...

Species distribution models predict temporal but not spatial variation in forest growth.

PubMed

van der Maaten, Ernst; Hamann, Andreas; van der Maaten-Theunissen, Marieke; Bergsma, Aldo; Hengeveld, Geerten; van Lammeren, Ron; Mohren, Frits; Nabuurs, Gert-Jan; Terhürne, Renske; Sterck, Frank

2017-04-01

Bioclimate envelope models have been widely used to illustrate the discrepancy between current species distributions and their potential habitat under climate change. However, the realism and correct interpretation of such projections has been the subject of considerable discussion. Here, we investigate whether climate suitability predictions correlate to tree growth, measured in permanent inventory plots and inferred from tree-ring records. We use the ensemble classifier RandomForest and species occurrence data from ~200,000 inventory plots to build species distribution models for four important European forestry species: Norway spruce, Scots pine, European beech, and pedunculate oak. We then correlate climate-based habitat suitability with volume measurements from ~50-year-old stands, available from ~11,000 inventory plots. Secondly, habitat projections based on annual historical climate are compared with ring width from ~300 tree-ring chronologies. Our working hypothesis is that habitat suitability projections from species distribution models should to some degree be associated with temporal or spatial variation in these growth records. We find that the habitat projections are uncorrelated with spatial growth records (inventory plot data), but they do predict interannual variation in tree-ring width, with an average correlation of .22. Correlation coefficients for individual chronologies range from values as high as .82 or as low as -.31. We conclude that tree responses to projected climate change are highly site-specific and that local suitability of a species for reforestation is difficult to predict. That said, projected increase or decrease in climatic suitability may be interpreted as an average expectation of increased or reduced growth over larger geographic scales.

Learning about Allergies

MedlinePlus

... other things from some animals grass, flower, and tree pollen (the fine dust from plants) mold and ... instance, you might be allergic to pollen from trees, which is present in the air only in ...

Phylogenomic analyses data of the avian phylogenomics project.

PubMed

Jarvis, Erich D; Mirarab, Siavash; Aberer, Andre J; Li, Bo; Houde, Peter; Li, Cai; Ho, Simon Y W; Faircloth, Brant C; Nabholz, Benoit; Howard, Jason T; Suh, Alexander; Weber, Claudia C; da Fonseca, Rute R; Alfaro-Núñez, Alonzo; Narula, Nitish; Liu, Liang; Burt, Dave; Ellegren, Hans; Edwards, Scott V; Stamatakis, Alexandros; Mindell, David P; Cracraft, Joel; Braun, Edward L; Warnow, Tandy; Jun, Wang; Gilbert, M Thomas Pius; Zhang, Guojie

2015-01-01

Determining the evolutionary relationships among the major lineages of extant birds has been one of the biggest challenges in systematic biology. To address this challenge, we assembled or collected the genomes of 48 avian species spanning most orders of birds, including all Neognathae and two of the five Palaeognathae orders. We used these genomes to construct a genome-scale avian phylogenetic tree and perform comparative genomic analyses. Here we present the datasets associated with the phylogenomic analyses, which include sequence alignment files consisting of nucleotides, amino acids, indels, and transposable elements, as well as tree files containing gene trees and species trees. Inferring an accurate phylogeny required generating: 1) A well annotated data set across species based on genome synteny; 2) Alignments with unaligned or incorrectly overaligned sequences filtered out; and 3) Diverse data sets, including genes and their inferred trees, indels, and transposable elements. Our total evidence nucleotide tree (TENT) data set (consisting of exons, introns, and UCEs) gave what we consider our most reliable species tree when using the concatenation-based ExaML algorithm or when using statistical binning with the coalescence-based MP-EST algorithm (which we refer to as MP-EST*). Other data sets, such as the coding sequence of some exons, revealed other properties of genome evolution, namely convergence. The Avian Phylogenomics Project is the largest vertebrate phylogenomics project to date that we are aware of. The sequence, alignment, and tree data are expected to accelerate analyses in phylogenomics and other related areas.

Tree grade distribution in Allegheny hardwoods

Treesearch

Richard L. Ernst; David A. Marquis

1978-01-01

Estimates of the distribution of tree grades by diameter class were developed for six hardwood species on the Allegheny Plateau. These estimates can be used to calculate present and projected stand values when actual tree grade measurements are not available.

DupTree: a program for large-scale phylogenetic analyses using gene tree parsimony.

PubMed

Wehe, André; Bansal, Mukul S; Burleigh, J Gordon; Eulenstein, Oliver

2008-07-01

DupTree is a new software program for inferring rooted species trees from collections of gene trees using the gene tree parsimony approach. The program implements a novel algorithm that significantly improves upon the run time of standard search heuristics for gene tree parsimony, and enables the first truly genome-scale phylogenetic analyses. In addition, DupTree allows users to examine alternate rootings and to weight the reconciliation costs for gene trees. DupTree is an open source project written in C++. DupTree for Mac OS X, Windows, and Linux along with a sample dataset and an on-line manual are available at http://genome.cs.iastate.edu/CBL/DupTree

The Trees that surround us

NASA Astrophysics Data System (ADS)

Costa, M. E. G.; Rodrigues, M. A. S.

2012-04-01

In our school the activities linked with sciences are developed in a partnership with other school subjects. Interdisciplinary projects are always valued from beginning to end of a project. It is common for teachers of different areas to work together in a Science project. Research of English written articles is very important not only for the development of our students' scientific literacy but also as a way of widening knowledge and a view on different perspectives of life instead of being limited to research of any articles in Portuguese language. In this study we are going to collect data about the predominant tree species in the region, especially the invasive trees from the acacia species, the native tree species and the commercial species. We are going to study the reasons for the appearance of each species and draw a chart of soil occupation in the council. This chart will also allow the study of the distribution and use of land for each tree species. This research work is the first stage for a contribution to warn the town council of the dangers of the invasive species to the future economy of the council.

A Sustainability Initiative to Quantify Carbon Sequestration by Campus Trees

ERIC Educational Resources Information Center

Cox, Helen M.

2012-01-01

Over 3,900 trees on a university campus were inventoried by an instructor-led team of geography undergraduates in order to quantify the carbon sequestration associated with biomass growth. The setting of the project is described, together with its logistics, methodology, outcomes, and benefits. This hands-on project provided a team of students…

Tree grades for eastern white pine

Treesearch

Robert L. Brisbin; David L. Sonderman; David L. Sonderman

1971-01-01

In 1957 the Forest Service Log Grade Committee recommended a service-wide action program in log and tree grade research. Approval of the program late in 1958 resulted in the establishment of five species-oriented timber- quality research projects covering the several groups of commercially important timber species. The eastern softwood timber-quality project was...

Does the Owl Fly out of the Tree or Does the Owl Exit the Tree Flying? How L2 Learners Overcome Their L1 Lexicalization Biases

ERIC Educational Resources Information Center

Song, Lulu; Pulverman, Rachel; Pepe, Christina; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathy

2016-01-01

Learning a language is more than learning its vocabulary and grammar. For example, compared with English, Spanish uses many more path verbs such as "ascender" ("to move upward") and "salir" ("to go out"), and expresses manner of motion optionally. English, in contrast, has many manner verbs (e.g., "run,…

Tapping the Sugar Maple--Learning and Appreciating

ERIC Educational Resources Information Center

Malone, Charles

1976-01-01

The article discusses how to tap a maple tree. Tapping a maple tree to produce maple syrup can: (1) lead to better understanding in many subject areas, (2) develop skills through participation in a rewarding activity, and (3) help students appreciate the many important roles that trees play in our environment and daily lives. (NQ)

The Tree Man: Robert Mazibuko's Story.

ERIC Educational Resources Information Center

Bloch, Joanne, Ed.

This book for beginning readers highlights Robert Mazibuko, the "Tree Man," who spent his life teaching people how to enrich the soil and plant vegetables and trees. Born in South Africa in 1904, he lived on a farm, learning to work with livestock, raise crops, and share with the community. In college, his professor of agriculture…

Floating production platforms and their applications in the development of oil and gas fields in the South China Sea

NASA Astrophysics Data System (ADS)

Zhang, Dagang; Chen, Yongjun; Zhang, Tianyu

2014-03-01

This paper studies the current available options for floating production platforms in developing deepwater oil fields and the potential development models of future oil and gas exploration in the South China Sea. A detailed review of current deepwater platforms worldwide was performed through the examples of industry projects, and the pros and cons of each platform are discussed. Four types of platforms are currently used for the deepwater development: tension leg platform, Spar, semi-submersible platform, and the floating production system offloading. Among these, the TLP and Spar can be used for dry tree applications, and have gained popularity in recent years. The dry tree application enables the extension of the drilling application for fixed platforms into floating systems, and greatly reduces the cost and complexity of the subsea operation. Newly built wet tree semi-submersible production platforms for ultra deepwater are also getting their application, mainly due to the much needed payload for deepwater making the conversion of the old drilling semi-submersible platforms impossible. These platforms have been used in different fields around the world for different environments; each has its own advantages and disadvantages. There are many challenges with the successful use of these floating platforms. A lot of lessons have been learned and extensive experience accumulated through the many project applications. Key technologies are being reviewed for the successful use of floating platforms for field development, and potential future development needs are being discussed. Some of the technologies and experience of platform applications can be well used for the development of the South China Sea oil and gas field.

Tree cover and species composition effects on academic performance of primary school students.

PubMed

Sivarajah, Sivajanani; Smith, Sandy M; Thomas, Sean C

2018-01-01

Human exposure to green space and vegetation is widely recognized to result in physical and mental health benefits; however, to date, the specific effects of tree cover, diversity, and species composition on student academic performance have not been investigated. We compiled standardized performance scores in Grades 3 and 6 for the collective student body in 387 schools across the Toronto District School Board (TDSB), and examined variation in relation to tree cover, tree diversity, and tree species composition based on comprehensive inventories of trees on school properties combined with aerial-photo-based assessments of tree cover. Analyses accounted for variation due to socioeconomic factors using the learning opportunity index (LOI), a regional composite index of external challenges to learning that incorporates income and other factors, such as students with English as a second language. As expected, LOI had the greatest influence on student academic performance; however, the proportion of tree cover, as distinct from other types of "green space" such as grass, was found to be a significant positive predictor of student performance, accounting for 13% of the variance explained in a statistical model predicting mean student performance assessments. The effects of tree cover and species composition were most pronounced in schools that showed the highest level of external challenges, suggesting the importance of urban forestry investments in these schools.

Tree cover and species composition effects on academic performance of primary school students

PubMed Central

Smith, Sandy M.; Thomas, Sean C.

2018-01-01

Human exposure to green space and vegetation is widely recognized to result in physical and mental health benefits; however, to date, the specific effects of tree cover, diversity, and species composition on student academic performance have not been investigated. We compiled standardized performance scores in Grades 3 and 6 for the collective student body in 387 schools across the Toronto District School Board (TDSB), and examined variation in relation to tree cover, tree diversity, and tree species composition based on comprehensive inventories of trees on school properties combined with aerial-photo-based assessments of tree cover. Analyses accounted for variation due to socioeconomic factors using the learning opportunity index (LOI), a regional composite index of external challenges to learning that incorporates income and other factors, such as students with English as a second language. As expected, LOI had the greatest influence on student academic performance; however, the proportion of tree cover, as distinct from other types of “green space” such as grass, was found to be a significant positive predictor of student performance, accounting for 13% of the variance explained in a statistical model predicting mean student performance assessments. The effects of tree cover and species composition were most pronounced in schools that showed the highest level of external challenges, suggesting the importance of urban forestry investments in these schools. PMID:29474503

Chapter 9 - Monitoring survival of fire-injured trees in Oregon and Washington (Project WC-F-08-03)

Treesearch

Robert A. Progar; Lisa Ganio; Lindsay Grayson; Sharon M. Hood

2018-01-01

Wild and prescribed fire injury to trees can produce mortality that is not immediately apparent, and environmental stress subsequent to a fire may also contribute to tree mortality in the years after a fire (Hood and Bentz 2007). In order to predict post-fire tree mortality from fire injury variables before tree mortality is clearly apparent, dozens of statistical...

Accuracy of tree grade projections for five Appalachian hardwood species

Treesearch

Gary W. Miller; Aaron T. Graves; Kurt W. Gottschalk; John E. Baumgras

2008-01-01

The potential value increase of individual trees is an important factor in planning effective forest management strategies. Similar to other investments, trees with high potential value increase are retained and allowed to grow, and those with relatively low potential value increase are harvested so that the proceeds may earn a higher rate of return elsewhere. Tree...

A Simple Model for Estimating Total and Merchantable Tree Heights

Treesearch

Alan R. Ek; Earl T. Birdsall; Rebecca J. Spears

1984-01-01

A model is described for estimating total and merchantable tree heights for Lake States tree species. It is intended to be used for compiling forest survey data and in conjunction with growth models for developing projections of tree product yield. Model coefficients are given for 25 species along with fit statistics. Supporting data sets are also described.

Predicting abundance of 80 tree species following climate change in the Eastern United States

Treesearch

Louis R. Iverson; Anantha M. Prasad; Anantha M. Prasad

1998-01-01

Projected climate warming will potentially have profound effects on the earth?s biota, including a large redistribution of tree species. We developed models to evaluate potential shifts for 80 individual tree species in the eastern United States. First, environmental factors associated with current ranges of tree species were assessed using geographic information...

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Seeing The "New Forest": A Visual Curricular Experiment

NASA Astrophysics Data System (ADS)

Garramone, Pariss Nicola

In contemporary Western contexts, human interaction with and interpretation of nature is a perpetually mediated process. Understandings and engagements with natural environments are informed by and often overlaid with meanings derived from representations. In other words, representations help constitute human relationships with nature. Thus learning how representations shape human understandings and experiences of nature and the resulting social, political, and ecological impact of these mediated relationships has emerged as an important field of inquiry within environmental education. This dissertation examines how a critical, self-reflexive act of looking at photographs can challenge an individual's concepts of nature/culture, real/imaginary, and self/other. The project engages in a curricular experiment where the researcher explores how photography meditates her abstract and embodied understandings of specific natural environments. A critical, self-reflexive approach to aesthetic engagement with photographs moves beyond simply deciphering or decoding representations; it incorporates the learner's own narrative and embodied responses to the photographic representations being explored. This approach also recognizes that pedagogy has a transformative effect; both the learner and the representations being explored are transformed through the process of engagement. In this dissertation, a selection of iconic photographs of Canadian tree planting from the collection of the National Gallery of Canada are looked at: Lorraine Gilbert's (1987-2004) series "Shaping the New Forest" and Sarah Anne Johnson's (2005) work "The Tree Planting Project." The aim of this project is twofold: to unravel how these photographs construct and transform knowledge of and relationships with the environment in Canada, and to demonstrate a model of environmental inquiry that can be integrated into critical environmental education curricula.

Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351

Estimating Coextinction Risks from Epidemic Tree Death: Affiliate Lichen Communities among Diseased Host Tree Populations of Fraxinus excelsior

PubMed Central

Jönsson, Mari T.; Thor, Göran

2012-01-01

At least 10% of the world’s tree species are threatened with extinction and pathogens are increasingly implicated in tree threats. Coextinction and threats to affiliates as a consequence of the loss or decline of their host trees is a poorly understood phenomenon. Ash dieback is an emerging infectious disease causing severe dieback of common ash Fraxinus excelsior throughout Europe. We utilized available empirical data on affiliate epiphytic lichen diversity (174 species and 17,800 observations) among 20 ash dieback infected host tree populations of F. excelsior on the island Gotland in the Baltic Sea, Sweden. From this, we used structured scenario projections scaled with empirical data of ash dieback disease to generate probabilistic models for estimating local and regional lichen coextinction risks. Average coextinction probabilities (Ā) were 0.38 (95% CI ±0.09) for lichens occurring on F. excelsior and 0.14 (95% CI ±0.03) when considering lichen persistence on all tree species. Ā was strongly linked to local disease incidence levels and generally increasing with lichen host specificity to F. excelsior and decreasing population size. Coextinctions reduced affiliate community viability, with significant local reductions in species richness and shifts in lichen species composition. Affiliates were projected to become locally extirpated before their hosts, illuminating the need to also consider host tree declines. Traditionally managed open wooded meadows had the highest incidence of ash dieback disease and significantly higher proportions of affiliate species projected to go extinct, compared with unmanaged closed forests and semi-open grazed sites. Most cothreatened species were not previously red-listed, which suggest that tree epidemics cause many unforeseen threats to species. Our analysis shows that epidemic tree deaths represent an insidious, mostly overlooked, threat to sessile affiliate communities in forested environments. Current conservation and management strategies must account for secondary extinctions associated with epidemic tree death. PMID:23049840

A report on conceptual advances in roll on/off technology in forestry

Treesearch

Dave Atkins; Robert Rummer; Beth Dodson; Craig E. Thomas; Andy Horcher; Ed Messerlie; Craig Rawlings; David Haston

2007-01-01

Over the last two decades, increasingly severe fire seasons have led policymakers to recognize the need for thinning overgrown stands of trees.However, thinning presents a financial challenge. The problem is that hazardous fuel reduction projects —especially projects in the Wildland/Urban Interface— contain mostly smaller trees, which have...

TESTING TREE-CLASSIFIER VARIANTS AND ALTERNATE MODELING METHODOLOGIES IN THE EAST GREAT BASIN MAPPING UNIT OF THE SOUTHWEST REGIONAL GAP ANALYSIS PROJECT (SW REGAP)

EPA Science Inventory

We tested two methods for dataset generation and model construction, and three tree-classifier variants to identify the most parsimonious and thematically accurate mapping methodology for the SW ReGAP project. Competing methodologies were tested in the East Great Basin mapping un...

The Tree of Life Project

ERIC Educational Resources Information Center

Milbrath, Sherry

2009-01-01

Middle-school students are just beginning to recognize their place in the world. That is why this author believes it is important to incorporate their world into their art. In this article, the author discusses the "Tree of Life" project, which she developed for her students in order to make them aware of various environmental issues, and how to…

Root and Branch Reform: Teaching City Kids about Urban Trees

ERIC Educational Resources Information Center

Walker, Mark

2017-01-01

In today's electronic age, suburban and city children are increasingly disconnected with the natural world. Studying trees allows children to learn about the world they live in and can teach a variety of useful topics contained within the National Curriculum in England. Knowledge of trees is specifically required in the science curriculum at key…

Further Effects of Phylogenetic Tree Style on Student Comprehension in an Introductory Biology Course

ERIC Educational Resources Information Center

Dees, Jonathan; Bussard, Caitlin; Momsen, Jennifer L.

2018-01-01

Phylogenetic trees have become increasingly important across the life sciences, and as a result, learning to interpret and reason from these diagrams is now an essential component of biology education. Unfortunately, students often struggle to understand phylogenetic trees. Style (i.e., diagonal or bracket) is one factor that has been observed to…

Prioritizing preferable locations for increasing urban tree canopy in New York City

Treesearch

Dexter Locke; J. Morgan Grove; Jacqueline W.T. Lu; Austin Troy; Jarlath P.M. O' Neil-Dunne; Brian Beck

2010-01-01

This paper presents a set of Geographic Information System (GIS) methods for identifying and prioritizing tree planting sites in urban environments. It uses an analytical approach created by a University of Vermont service-learning class called "GIS Analysis of New York City's Ecology" that was designed to provide research support to the MillionTreesNYC...

Estimating root collar diameter growth for multi-stem western woodland tree species on remeasured forest inventory and analysis plots

Treesearch

Michael T. Thompson; Maggie. Toone

2012-01-01

Tree diameter growth models are widely used in many forestry applications, often to predict tree size at a future point in time. Also, there are instances where projections of past diameters are needed. An individual tree model has been developed to estimate diameter growth of multi-stem woodland tree species where the diameter is measured at root collar. The model was...

Visualizing phylogenetic tree landscapes.

PubMed

Wilgenbusch, James C; Huang, Wen; Gallivan, Kyle A

2017-02-02

Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. Different partitions within these new alignments (e.g., genes, codon positions, and structural features) often favor hundreds if not thousands of competing phylogenies. Summarizing and comparing phylogenies obtained from multi-source data sets using current consensus tree methods discards valuable information and can disguise potential methodological problems. Discovery of efficient and accurate dimensionality reduction methods used to display at once in 2- or 3- dimensions the relationship among these competing phylogenies will help practitioners diagnose the limits of current evolutionary models and potential problems with phylogenetic reconstruction methods when analyzing large multi-source data sets. We introduce several dimensionality reduction methods to visualize in 2- and 3-dimensions the relationship among competing phylogenies obtained from gene partitions found in three mid- to large-size mitochondrial genome alignments. We test the performance of these dimensionality reduction methods by applying several goodness-of-fit measures. The intrinsic dimensionality of each data set is also estimated to determine whether projections in 2- and 3-dimensions can be expected to reveal meaningful relationships among trees from different data partitions. Several new approaches to aid in the comparison of different phylogenetic landscapes are presented. Curvilinear Components Analysis (CCA) and a stochastic gradient decent (SGD) optimization method give the best representation of the original tree-to-tree distance matrix for each of the three- mitochondrial genome alignments and greatly outperformed the method currently used to visualize tree landscapes. The CCA + SGD method converged at least as fast as previously applied methods for visualizing tree landscapes. We demonstrate for all three mtDNA alignments that 3D projections significantly increase the fit between the tree-to-tree distances and can facilitate the interpretation of the relationship among phylogenetic trees. We demonstrate that the choice of dimensionality reduction method can significantly influence the spatial relationship among a large set of competing phylogenetic trees. We highlight the importance of selecting a dimensionality reduction method to visualize large multi-locus phylogenetic landscapes and demonstrate that 3D projections of mitochondrial tree landscapes better capture the relationship among the trees being compared.

The Efficiency of Random Forest Method for Shoreline Extraction from LANDSAT-8 and GOKTURK-2 Imageries

NASA Astrophysics Data System (ADS)

Bayram, B.; Erdem, F.; Akpinar, B.; Ince, A. K.; Bozkurt, S.; Catal Reis, H.; Seker, D. Z.

2017-11-01

Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model - Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.

Tree-Structured Infinite Sparse Factor Model

PubMed Central

Zhang, XianXing; Dunson, David B.; Carin, Lawrence

2013-01-01

A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. PMID:25279389

7 CFR 1214.15 - Programs, plans and projects.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AGREEMENTS AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE CHRISTMAS TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information Order Definitions...

7 CFR 1214.15 - Programs, plans and projects.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AGREEMENTS AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE CHRISTMAS TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information Order Definitions...

7 CFR 1214.15 - Programs, plans and projects.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AGREEMENTS AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE CHRISTMAS TREE PROMOTION, RESEARCH, AND INFORMATION ORDER Christmas Tree Promotion, Research, and Information Order Definitions...

Facts and Statistics about Food Allergies

MedlinePlus

... of reactions. Learn more here. Milk Egg Peanut Tree Nuts Soy Wheat Fish Shellfish Sesame Other Food ... reactions. Eight major food allergens – milk, egg, peanut, tree nuts, wheat, soy, fish and crustacean shellfish – are ...

76 FR 5396 - Notice of Intent To Prepare an Environmental Impact Statement for the Proposed Rising Tree Wind...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-31

...] Notice of Intent To Prepare an Environmental Impact Statement for the Proposed Rising Tree Wind Farm... on issues and alternatives related to the Rising Tree Wind Farm Draft EIS/EIR and possible CDCA PA by...-megawatt Rising Tree Wind Farm. The proposed project is approximately three miles west of the town of...

Planting and care of fine hardwood seedlings: Financial and tax aspects of tree planting

Treesearch

William L. Hoover

2004-01-01

Trees are planted for many reasons, including soil and water conservation, wildlife habitat, nut and timber production. Altruism motivates many landowners to plant trees. There are, however, those who plant with the expectation of increasing their family's wealth. In this publication I discuss the financial and tax aspects of tree planting projects. The focus is...

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

LIFE CLIMATREE project: A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas

NASA Astrophysics Data System (ADS)

Stergiou, John; Tagaris, Efthimios; -Eleni Sotiropoulou, Rafaella

2016-04-01

Climate Change Mitigation is one of the most important objectives of the Kyoto Convention, and is mostly oriented towards reducing GHG emissions. However, carbon sink is retained only in the calculation of the forests capacity since agricultural land and farmers practices for securing carbon stored in soils have not been recognized in GHG accounting, possibly resulting in incorrect estimations of the carbon dioxide balance in the atmosphere. The agricultural sector, which is a key sector in the EU, presents a consistent strategic framework since 1954, in the form of Common Agricultural Policy (CAP). In its latest reform of 2013 (reg. (EU) 1305/13) CAP recognized the significance of Agriculture as a key player in Climate Change policy. In order to fill this gap the "LIFE ClimaTree" project has recently founded by the European Commission aiming to provide a novel method for including tree crop cultivations in the LULUCF's accounting rules for GHG emissions and removal. In the framework of "LIFE ClimaTree" project estimation of carbon sink within EU through the inclusion of the calculated tree crop capacity will be assessed for both current and future climatic conditions by 2050s using the GISS-WRF modeling system in a very fine scale (i.e., 9km x 9km) using RCP8.5 and RCP4.5 climate scenarios. Acknowledgement: LIFE CLIMATREE project "A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas" (LIFE14 CCM/GR/000635).

From Google Maps to a fine-grained catalog of street trees

NASA Astrophysics Data System (ADS)

Branson, Steve; Wegner, Jan Dirk; Hall, David; Lang, Nico; Schindler, Konrad; Perona, Pietro

2018-01-01

Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases.

Risk Analysis of Return Support Material on Gas Compressor Platform Project

NASA Astrophysics Data System (ADS)

Silvianita; Aulia, B. U.; Khakim, M. L. N.; Rosyid, Daniel M.

2017-07-01

On a fixed platforms project are not only carried out by a contractor, but two or more contractors. Cooperation in the construction of fixed platforms is often not according to plan, it is caused by several factors. It takes a good synergy between the contractor to avoid miss communication may cause problems on the project. For the example is about support material (sea fastening, skid shoe and shipping support) used in the process of sending a jacket structure to operation place often does not return to the contractor. It needs a systematic method to overcome the problem of support material. This paper analyses the causes and effects of GAS Compressor Platform that support material is not return, using Fault Tree Analysis (FTA) and Event Tree Analysis (ETA). From fault tree analysis, the probability of top event is 0.7783. From event tree analysis diagram, the contractors lose Rp.350.000.000, - to Rp.10.000.000.000, -.

ARPA-E: Engineering Innovative New Biofuels

ScienceCinema

Burbaum, Jonathan; Peter, Gary; Kirby, Jim; Lemaux

2018-05-30

ARPA-E's PETRO program was created to supply the transportation sector with plant-derived fuels that are cost-competitive with petroleum and don't affect U.S. food supply. This video highlights the role that ARPA-E has played in connecting traditionally distinct research areas to inform the research and development efforts of PETRO project teams. Specifically, it highlights how the University of Florida leveraged lessons learned from the Joint BioEnergy Institute's work with E. coli to directly influence their work in harvesting fuel molecules from pine trees, as well as how the same genes tested in pine are now being tested in tobacco at Lawrence Berkeley National Laboratory. This transfer of knowledge facilitates new discovery.

Integrating human and machine intelligence in galaxy morphology classification tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Seeing the Forest through the Trees: Helping Students Appreciate Life's Diversity by Building the Tree of Life

ERIC Educational Resources Information Center

Staub, Nancy L.; Pauw, Peter G.; Pauw, Daniel

2006-01-01

Introductory biology students can be overwhelmed by the diversity of life. By focusing on learning characteristics of individual taxa, they often lose and miss the larger perspective--that all taxa are connected through evolution, resulting in the Tree of Life. In this article, the authors present an exercise that helps students develop an…

Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space.

DTIC Science & Technology

1997-01-01

create a dependency tree containing an optimum set of n-1 first-order dependencies. To do this, first, we select an arbitrary bit Xroot to place at the...the root to an arbitrary bit Xroot -For all other bits Xi, set bestMatchingBitInTree[Xi] to Xroot . -While not all bits have been

Minnesota wood energy scale-up project 1994 establishment cost data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Downing, M.; Pierce, R.; Kroll, T.

1996-03-18

The Minnesota Wood Energy Scale-up Project began in late 1993 with the first trees planted in the spring of 1994. The purpose of the project is to track and monitor economic costs of planting, maintaining and monitoring larger scale commercial plantings. For 15 years, smaller scale research plantings of hybrid poplar have been used to screen for promising, high-yielding poplar clones. In this project 1000 acres of hybrid poplar trees were planted on Conservation Reserve Program (CRP) land near Alexandria, Minnesota in 1994. The fourteen landowners involved re-contracted with the CRP for five-year extensions of their existing 10-year contracts. Thesemore » extended contracts will expire in 2001, when the plantings are 7 years old. The end use for the trees planted in the Minnesota Wood Energy Scale-up Project is undetermined. They will belong to the owner of the land on which they are planted. There are no current contracts in place for the wood these trees are projected to supply. The structure of the wood industry in the Minnesota has changed drastically over the past 5 years. Stumpage values for fiber have risen to more than $20 per cord in some areas raising the possibility that these trees could be used for fiber rather than energy. Several legislative mandates have forced the State of Minnesota to pursue renewable energy including biomass energy. These mandates, a potential need for an additional 1700 MW of power by 2008 by Northern States Power, and agricultural policies will all affect development of energy markets for wood produced much like agricultural crops. There has been a tremendous amount of local and international interest in the project. Contractual negotiations between area landowners, the CRP, a local Resource Conservation and Development District, the Minnesota Department of Natural Resources and others are currently underway for additional planting of 1000 acres in spring 1995.« less

Social Studies: It's a Family Affair.

ERIC Educational Resources Information Center

Melendez, Ruth

1999-01-01

Describes an elementary-level family tree project for social studies classes that teaches students about their personal history and the country's diverse culture. Children complete a family tree chart, then the class creates visual presentations using a world map and bar graph. Finally, students write summary statements based on the family trees,…

A life cycle greenhouse gas inventory of a tree production system

Treesearch

Alissa Kendall; E. Gregory McPherson

2012-01-01

PurposeThis study provides a detailed, process-based life cycle greenhouse gas (GHG) inventory of an ornamental tree production system for urban forestry. The success of large-scale tree planting initiatives for climate protection depends on projects being net sinks for CO2 over their entire life cycle....

Utilizing Municipal Trees: Ideas From Across the Country

Treesearch

Stephen M. Bratkovich

2001-01-01

To show how municipal tree removals can be utilized for traditional wood products, this publication highlights 16 successful projects from around the country. These case studies are organized by the different types of participants: State and regional partnerships, municipalities, tree service firms, entrepreneurs, and sawmills. Contact information is provided for each...

Reclamation: what about trees

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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

A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest

PubMed Central

Wang, Yunsheng; Weinacker, Holger; Koch, Barbara

2008-01-01

A procedure for both vertical canopy structure analysis and 3D single tree modelling based on Lidar point cloud is presented in this paper. The whole area of research is segmented into small study cells by a raster net. For each cell, a normalized point cloud whose point heights represent the absolute heights of the ground objects is generated from the original Lidar raw point cloud. The main tree canopy layers and the height ranges of the layers are detected according to a statistical analysis of the height distribution probability of the normalized raw points. For the 3D modelling of individual trees, individual trees are detected and delineated not only from the top canopy layer but also from the sub canopy layer. The normalized points are resampled into a local voxel space. A series of horizontal 2D projection images at the different height levels are then generated respect to the voxel space. Tree crown regions are detected from the projection images. Individual trees are then extracted by means of a pre-order forest traversal process through all the tree crown regions at the different height levels. Finally, 3D tree crown models of the extracted individual trees are reconstructed. With further analyses on the 3D models of individual tree crowns, important parameters such as crown height range, crown volume and crown contours at the different height levels can be derived. PMID:27879916

The North American Society for Pediatric and Adolescent Gynecology Fellowship Family Tree.

PubMed

Pecchioli, Yael; Jamieson, Mary Anne

2015-12-01

To create a family tree to chronicle the proliferation of our specialty through fellowships (formal and informal) within the pediatric and adolescent gynecology practice and among the membership of the North American Society for Pediatric and Adolescent Gynecology (NASPAG). This historical project was undertaken as a way to demonstrate NASPAG's rich sense of heritage and community. The tree is meant to be a dynamic project, a living document, changing and expanding as this field of medicine grows, and offers a form of institutional memory for NASPAG. Questionnaires were sent out to all current NASPAG members via e-mail (and the list-serve) and were available at the 2014 NASPAG Annual Clinical and Research Meeting. Data from the questionnaires were recorded within GRAMPS 3.4.8, software used to create a family tree. The result of the project was an elegant and intricate tree, containing 379 "family members" including physicians who specialize in pediatric and adolescent gynecology, adolescent medicine, reproductive endocrinology and infertility, and pediatric endocrinology. The family tree, which shows how one mentor might train multiple trainees and how past trainees later become mentors, highlights the value of physicians who take on supervisory and educational roles and the existence of comprehensive and inspirational training programs. Copyright © 2015 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.

PubMed

Zeng, Qinghui; Liu, Yi; Zhao, Hongtao; Sun, Mingdong; Li, Xuyong

2017-04-01

Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Garden Banks 388 horizontal tree design and development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Granhaug, O.; Soul, J.

1995-12-31

This paper describes the Horizontal Subsea Production Tree System, later referred to as a SpoolTree{trademark}, developed for the Enserch Garden Banks 388 field in the Gulf of Mexico. The paper starts with a project overview followed by a comparison between the SpoolTree and the Conventional Tree design. A brief discussion explains why Enserch elected to use the SpoolTree for this field development, including available technology, workover frequency, cost etc. The rigorous safety analysis carried out for the subsea production equipment is then explained in depth. The paper continues with a technical discussion of the main features specific to the SpoolTreemore » design and the Garden Banks 388 field development. Issues discussed include the SpoolTree itself, BOP Adapter Plate (for control during installation, workover and production), Tubing Hanger and pressure barrier design, debris cap design, downhole communication (SCSSV, chemical injection, pressure and temperature) ROV intervention, template wellbay insert design and other relevant issues. The use of computer based 3-D modelling tool is also briefly described. The experience and results described in this paper have direct application to numerous subsea development prospects worldwide, particularly in deep water. In addition, the ``system development`` aspect of the project is relevant to most marine equipment development projects. This includes the use of safety analysis techniques, 3-D computer modelling tools and clearly defined engineering procedures. A full account of the final design configuration of the SpoolTree system is given in the paper. A summary of the experience gained during the extensive testing at the factory and during the template integration tests is also provided.« less

Bioinformatics in proteomics: application, terminology, and pitfalls.

PubMed

Wiemer, Jan C; Prokudin, Alexander

2004-01-01

Bioinformatics applies data mining, i.e., modern computer-based statistics, to biomedical data. It leverages on machine learning approaches, such as artificial neural networks, decision trees and clustering algorithms, and is ideally suited for handling huge data amounts. In this article, we review the analysis of mass spectrometry data in proteomics, starting with common pre-processing steps and using single decision trees and decision tree ensembles for classification. Special emphasis is put on the pitfall of overfitting, i.e., of generating too complex single decision trees. Finally, we discuss the pros and cons of the two different decision tree usages.

Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds

NASA Astrophysics Data System (ADS)

Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

2018-06-01

In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

NASA Astrophysics Data System (ADS)

Iyer, V.; Shetty, S.; Iyengar, S. S.

2015-07-01

Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

Fremont Tree-Well Filter

EPA Pesticide Factsheets

Information about the SFBWQP Fremont Tree-Well Filter Spine project, part of an EPA competitive grant program to improve SF Bay water quality focused on restoring impaired waters and enhancing aquatic resources.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

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

Students Engaged in Climate Change Research Through Vegetation Phenology Studies

NASA Astrophysics Data System (ADS)

Sparrow, E. B.; Verbyla, D. L.; White, M. A.; Gordon, L. S.

2004-12-01

The project goal is to engage students in scientific research as a way of learning science, math, and technology in K-12 classrooms by providing an opportunity for student-scientist collaborations. This NSF-funded GLOBE project is of significance to scientists who track plant phenological changes as an indicator of climate change and study carbon cycling. To students it is a means of studying Earth as a system. Plants and their phenology stages reflect and integrate the effects of weather and other environmental parameters that are components of the Earth system. Remotely sensed data indicate that the plant growing season has increased in northern latitudes. The greenness estimates could vary due to possible interference from clouds and other atmospheric properties, low sun angles at high latitudes and aging of satellite detectors; hence the need for ground-based observations to help validate satellite-derived estimates of plant growing season lengths. GLOBE plant phenology measurements (protocols) of Green-up and Green-down for deciduous trees and shrubs, and for grasses were developed at the University of Alaska Fairbanks, and Budburst at Utah State University. These were pilot- tested, and revised several times with input from teachers and GLOBE personnel. Learning activities to support understanding of science concepts, were also developed and/or adapted. The protocols and learning activities were aligned to national science standards and incorporated in the "Earth as a System" chapter in the 2003 GLOBE Teacher Guide published and also posted on the GLOBE website (www.globe.gov). Phenology protocols and learning activities are being used in Alaska by teachers and students who participate in different NSF and NASA-funded science education programs, such as the Schoolyard Long Term Ecological Research Project, the Global Change Education Using Western Science and Native Observations (OLCG) Project, the Alaska GLOBE program and the EPSCoR Rural Research Partnership Education Outreach program. Pre-college students and their teachers from 77 schools in 11 countries have engaged in GLOBE plant phenology research and entered phenology data on the GLOBE web server. Thus, collaborative efforts in research and education among science education projects in Alaska and in other countries have been facilitated. Scientists now have access to global plant phenology data-ground-based observations that previously have been very rare. Students have also used the phenology protocols for their own investigations.

Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy.

PubMed

Letunic, Ivica; Bork, Peer

2011-07-01

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. In addition to classical tree viewer functions, iTOL offers many novel ways of annotating trees with various additional data. Current version introduces numerous new features and greatly expands the number of supported data set types. Trees can be interactively manipulated and edited. A free personal account system is available, providing management and sharing of trees in user defined workspaces and projects. Export to various bitmap and vector graphics formats is supported. Batch access interface is available for programmatic access or inclusion of interactive trees into other web services.

Electric Trees and Pond Creatures.

ERIC Educational Resources Information Center

Weaver, Helen; Hounshell, Paul B.

1978-01-01

Two learning activities are presented to develop observation and classification skills at the elementary level. The first is an electric box that associates tree names with leaf and bark specimens, and the second is a pond water observation and slide preparation activity. (BB)

Land-use change outweighs projected effects of changing rainfall on tree cover in sub-Saharan Africa.

PubMed

Aleman, Julie C; Blarquez, Olivier; Staver, Carla A

2016-09-01

Global change will likely affect savanna and forest structure and distributions, with implications for diversity within both biomes. Few studies have examined the impacts of both expected precipitation and land use changes on vegetation structure in the future, despite their likely severity. Here, we modeled tree cover in sub-Saharan Africa, as a proxy for vegetation structure and land cover change, using climatic, edaphic, and anthropic data (R(2)  = 0.97). Projected tree cover for the year 2070, simulated using scenarios that include climate and land use projections, generally decreased, both in forest and savanna, although the directionality of changes varied locally. The main driver of tree cover changes was land use change; the effects of precipitation change were minor by comparison. Interestingly, carbon emissions mitigation via increasing biofuels production resulted in decreases in tree cover, more severe than scenarios with more intense precipitation change, especially within savannas. Evaluation of tree cover change against protected area extent at the WWF Ecoregion scale suggested areas of high biodiversity and ecosystem services concern. Those forests most vulnerable to large decreases in tree cover were also highly protected, potentially buffering the effects of global change. Meanwhile, savannas, especially where they immediately bordered forests (e.g. West and Central Africa), were characterized by a dearth of protected areas, making them highly vulnerable. Savanna must become an explicit policy priority in the face of climate and land use change if conservation and livelihoods are to remain viable into the next century. © 2016 John Wiley & Sons Ltd.

Deep Multi-Task Learning for Tree Genera Classification

NASA Astrophysics Data System (ADS)

Ko, C.; Kang, J.; Sohn, G.

2018-05-01

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) - Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (Lcd) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7 % to 91.0 % (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

DeepNeuron: an open deep learning toolbox for neuron tracing.

PubMed

Zhou, Zhi; Kuo, Hsien-Chi; Peng, Hanchuan; Long, Fuhui

2018-06-06

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

75 FR 5941 - Umatilla National Forest, Walla Walla Ranger District, Walla Walla, WA; Cobbler II Timber Sale...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-05

... construction (that will be decommissioned after project use), new road construction, danger tree removal along... increasing population. Late seral tree species have become dominant after long periods without disturbance... and vigor. Timber stands of seral tree species such as western larch and ponderosa pine are infilling...

76 FR 70955 - Helena Nation Forest: Dalton Mountain Forest Restoration & Fuels Reduction Project

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-16

... allow reestablishment of controlled periodic fire; and capturing the value of removed trees in an... mixed-severity fire regime that is dominated by lodgepole pine. Tree mortality from a mountain pine... other tree species native to the area including aspen, whitebark pine, and ponderosa pine do not occur...

A description of STEMS-- the stand and tree evaluation and modeling system.

Treesearch

David M. Belcher; Margaret R. Holdaway; Gary J. Brand

1982-01-01

This paper describes STEMS (Stand and Tree Evaluation and Modeling System), the current computerized Lake State tree growth projection system. It presents the program structure, discusses the growth and mortality components, the management subsystem, and the regeneration subsystem. Some preliminary results of model testing are presented and an application is...

Climate ready urban trees for Central Valley cities

Treesearch

E.G. McPherson; A.M. Berry

2015-01-01

Urban forests provide many societal and ecological services to cities and their inhabitants. Many species of trees are under stress due to anthropogenic and natural climate changes. Projected climatic shifts will change temperature, precipitation, and the incidences of pest and disease outbreaks. The tolerance of urban trees to these stressors varies considerably among...

Following the fate of harvest-damaged trees 13 years after harvests

Treesearch

Randy G. Jensen; John M. Kabrick

2014-01-01

Logging damage to residual trees during harvest operations can reduce the future volume, quality, and value of wood products. Timber harvests in 1996 on the Missouri Ozark Forest Ecosystem Project (MOFEP) provided a rare opportunity to follow the fate of trees wounded by felling or by skidding with rubber-tired skidders.

Tree cavity estimation and verification in the Missouri Ozarks

Treesearch

Randy G. Jensen; John M. Kabrick; Eric K. Zenner

2002-01-01

Missouri forest management guidelines require that cavity trees and snags be provided for wildlife. Missouri Ozark Forest Ecosystem Project (MOFEP) timber inventories provided opportunities to determine if cavity tree and snag densities in a mature second-growth oak-hickory-pine forest meet forest management guidelines, to evaluate the effects of the first-entry...

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

PubMed

Nguyen, Thuy Tuong; Slaughter, David C; Hanson, Bradley D; Barber, Andrew; Freitas, Amy; Robles, Daniel; Whelan, Erin

2015-07-28

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images.

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera

PubMed Central

Nguyen, Thuy Tuong; Slaughter, David C.; Hanson, Bradley D.; Barber, Andrew; Freitas, Amy; Robles, Daniel; Whelan, Erin

2015-01-01

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images. PMID:26225982

Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section

USGS Publications Warehouse

Nydick, Koren R.; Stephenson, Nathan L.; Ambrose, Anthony R.; Asner, Gregory P.; Baxter, Wendy L.; Das, Adrian J.; Dawson, Todd E.; Martin, Roberta E.; Paz-Kagan, Tarin

2018-01-01

Hotter droughts are becoming more common as climate change progresses, and they may already have caused instances of forest dieback on all forested continents. Learning from hotter droughts, including where on the landscape forests are more or less vulnerable to these events, is critical to help resource managers proactively prepare for the future. As part of our Leaf to Landscape Project, we measured the response of giant sequoia, the world’s largest tree species, to the extreme 2012–2016 hotter drought in California. The project integrated leaf-level physiology measurements, crown-level foliage dieback surveys, and remotely sensed canopy water content (CWC) to shed light on mechanisms and spatial patterns in drought response. Here we summarize initial findings, present a conceptual model of drought response, and discuss management implications; details are presented in the other four articles of the special section on Giant Sequoias and Drought. Giant sequoias exhibited both leaf- and canopy-level responses that were effective in protecting whole-tree hydraulic integrity for the vast majority of individual sequoias. Very few giant sequoias died during the drought compared to other mixed conifer tree species; however, the magnitude of sequoia drought response varied across the landscape. This variability was partially explained by local site characteristics, including variables related to site water balance. We found that low CWC is an indicator of recent foliage dieback, which occurs when stress levels are high enough that leaf-level adjustments alone are insufficient for giant sequoias to maintain hydraulic integrity. CWC or change in CWC may be useful indicators of drought stress that reveal patterns of vulnerability to future hotter droughts. Future work will measure recovery from the drought and strengthen our ability to interpret CWC maps. Our ultimate goal is to produce giant sequoia vulnerability maps to help target management actions, such as reducing other stressors, increasing resistance to hotter drought through prescribed fire or mechanical thinning, and planting sequoias in projected future suitable habitat, which may occur outside current grove distributions. We suggest that managers compare different types of vulnerability assessments and combine vulnerability maps with other sources of information to inform decisions.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

PubMed

Deb, Alok Kanti; Jayadeva; Gopal, Madan; Chandra, Suresh

2007-07-01

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

Assessing student understanding of sound waves and trigonometric reasoning in a technology-rich, project-enhanced environment

NASA Astrophysics Data System (ADS)

Wilhelm, Jennifer Anne

This case study examined what student content understanding could occur in an inner city Industrial Electronics classroom located at Tree High School where project-based instruction, enhanced with technology, was implemented for the first time. Students participated in a project implementation unit involving sound waves and trigonometric reasoning. The unit was designed to foster common content learning (via benchmark lessons) by all students in the class, and to help students gain a deeper conceptual understanding of a sub-set of the larger content unit (via group project research). The objective goal of the implementation design unit was to have students gain conceptual understanding of sound waves, such as what actually waves in a wave, how waves interfere with one another, and what affects the speed of a wave. This design unit also intended for students to develop trigonometric reasoning associated with sinusoidal curves and superposition of sinusoidal waves. Project criteria within this design included implementation features, such as the need for the student to have a driving research question and focus, the need for benchmark lessons to help foster and scaffold content knowledge and understanding, and the need for project milestones to complete throughout the implementation unit to allow students the time for feedback and revision. The Industrial Electronics class at Tree High School consisted of nine students who met daily during double class periods giving 100 minutes of class time per day. The class teacher had been teaching for 18 years (mathematics, physics, and computer science). He had a background in engineering and experience teaching at the college level. Benchmark activities during implementation were used to scaffold fundamental ideas and terminology needed to investigate characteristics of sound and waves. Students participating in benchmark activities analyzed motion and musical waveforms using probeware, and explored wave phenomena using waves simulation software. Benchmark activities were also used to bridge the ideas of triangle trigonometric ratios to the graphs of sinusoidal curves, which could lead to understanding the concepts of frequency, period, amplitude, and wavelength. (Abstract shortened by UMI.)

Machine learning models for lipophilicity and their domain of applicability.

PubMed

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Laak, Antonius Ter; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-01-01

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

On Forests and Trees: A Response to Klingner.

ERIC Educational Resources Information Center

Neuman, Susan B.; Koskinen, Patricia

1993-01-01

Responds to criticisms raised in another article in this issue concerning a study of incidental word learning among second-language learners viewing captioned television. Suggests that the criticisms fail to "see the forest for the trees." Responds to specific methodological criticisms. (RS)

Bugs and burns: effects of fire on ponderosa pine bark beetle (Project INT-F-07-02)

Treesearch

Thomas DeGomez; Thomas Kolb; Sabrina Kleinman; Kelly Williams

2013-01-01

Fire-damaged trees that otherwise would have survived can be killed by bark beetles (McCullough and others 1998, McHugh and others 2003). Wallin and others (2008) found that fire weakens a tree’s defense against bark beetles. An unacceptable level of tree mortality may occur after a controlled burn as a result of weakened tree defenses (Sullivan and others 2003)....

Using Taxonomic Indexing Trees to Efficiently Retrieve SCORM-Compliant Documents in e-Learning Grids

ERIC Educational Resources Information Center

Shih, Wen-Chung; Tseng, Shian-Shyong; Yang, Chao-Tung

2008-01-01

With the flourishing development of e-Learning, more and more SCORM-compliant teaching materials are developed by institutes and individuals in different sites. In addition, the e-Learning grid is emerging as an infrastructure to enhance traditional e-Learning systems. Therefore, information retrieval schemes supporting SCORM-compliant documents…

Missing the Trees for the Forest?: Learning Environments versus Learning Techniques in Simulations

ERIC Educational Resources Information Center

Raymond, Chad

2012-01-01

Institutions of higher learning are increasingly asked to defend curricular and pedagogical outcomes. Faculty must demonstrate that simulations are productive tools for learning, but a review of the literature shows that the evidence of their effectiveness is inconclusive, despite their popularity in the classroom. Simulations may in fact help…

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Influence of climate change on the flowering of temperate fruit trees

NASA Astrophysics Data System (ADS)

Perez-Lopez, D.; Ruiz-Ramos, M.; Sánchez-Sánchez, E.; Centeno, A.; Prieto-Egido, I.; Lopez-de-la-Franca, N.

2012-04-01

It is well known that winter chilling is necessary for the flowering of temperate trees. The chilling requirement is a criterion for choosing a species or variety at a given location. Also chemistry products can be used for reducing the chilling-hours needs but make our production more expensive. This study first analysed the observed values of chilling hours for some representative agricultural locations in Spain for the last three decades and their projected changes under climate change scenarios. Usually the chilling is measured and calculated as chilling-hours, and different methods have been used to calculate them (e.g. Richarson et al., 1974 among others) according to the species considered. For our objective North Carolina method (Shaltout and Unrath, 1983) was applied for apples, Utah method (Richardson et al. 1974) for peach and grapevine and the approach used by De Melo-Abreu et al. (2004) for olive trees. The influence of climate change in temperate trees was studied by calculating projections of chilling-hours with climate data from Regional Climate Models (RCMs) at high resolution (25 km) from the European Project ENSEMBLES (http://www.ensembles-eu.org/). These projections will allow for analysing the modelled variations of chill-hours between 2nd half of 20C and 1st half of 21C at the study locations.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Crown Health of Reserve Hardwood Trees Following Reproduction Cutting in the Ouachita Mountains

Treesearch

Dale A. Starkey; James M. Guldin

2004-01-01

Abstract - Monitoring the health of reserve hardwood trees is being performed as part of the Ecosystem Management Research Project on the Ouachita and Ozark National Forests in Arkansas. A suite of crown variables (diameter, live crown ratio, density, dieback, and foliage transparency) was used to detect significant changes in reserve tree health...

Tree genetic and improvement research at the University of Minnesota

Treesearch

Scott S. Pauley

1970-01-01

The School of Forestry's Tree Improvement Research Project was initiated in 1955. Studies in this area during the past fourteen years have been designed to accumulate information on genetic diversity in native and exotic tree species and isolate genetically superior lines for direct use in Minnesota forest plantings or for further selective breeding. Nursery...

Temperature and tree growth [editorial

Treesearch

Michael G. Ryan

2010-01-01

Tree growth helps US forests take up 12% of the fossil fuels emitted in the USA (Woodbury et al. 2007), so predicting tree growth for future climates matters. Predicting future climates themselves is uncertain, but climate scientists probably have the most confidence in predictions for temperature. Temperatures are projected to rise by 0.2 °C in the next two decades,...

Assisted migration: What it means to nursery managers and tree planters

Treesearch

Mary I. Williams; R. Kasten Dumroese

2014-01-01

Projections indicate that natural plant adaptation and migration may not keep pace with climate changes. This mismatch in rates will pose significant challenges for practitioners that select, grow, and outplant native tree species. Populations of native tree species planted today must be able to meet the climatic challenges they will face during this century. One...

Million TreesNYC: the integration of research and practice

Treesearch

Lindsay K. Campbell; Morgan Monaco; Nancy Falxa-Raymond; Jacqueline Lu; Andrew Newman; Ruth A. Rae; Erika S. Svendsen

2014-01-01

MillionTreesNYC is an ambitious campaign to plant and care for one million new trees in New York City. Implemented by the City of New York Department of Parks & Recreation and the nonprofit New York Restoration Project, this innovative, citywide effort crosses property jurisdictions and physical sites. The goal is to enhance the entire 'green matrix' of...

Adaptation of trees, forests and forestry to climate change

Treesearch

Daniel J. Chmura; Glenn T. Howe; Paul D. Anderson; Bradley J. St Clair

2010-01-01

Ongoing climate change will likely expose trees and forests to new stresses and disturbances during this century. Trees naturally adapt to changes in climate, but their natural adaptive ability may be compromised by the rapid changes projected for this century. In the broad sense, adaptation to climate change also includes the purposeful adaptation of human systems,...

Tree Owner's Manual for the Northeastern Midwestern United States

Treesearch

Jill Johnson; Gary Johnson; Maureen McDonough; Lisa Burban; Janette Monear

2008-01-01

One common issue facing our urban forests is the fact that trees are dying prematurely. Many are planted improperly, setting them up for failure. Many do not receive regular maintenance. And few are adequately protected during construction projects. To help remedy this issue, the Forest Service has created this Tree Owner's Manual. Just like the owner...

Diameter Growth, Survival, and Volume Estimates for Missouri Trees

Treesearch

Stephen R. Shifley; W. Brad Smith

1982-01-01

Measurements of more than 20,000 Missouri trees were summarized by species and diameter class into tables of mean annual diameter growth, annual probability of survival, net cubic foot volume, and net board foot volume. In the absence of better forecasting techniques, this information can be utilized to project short-term changes for Missouri trees, inventory plots,...

Does tree planting pay us back? Lessons from Sacramento, CA

Treesearch

Yekang Ko; Lara A. Roman; E. Gregory McPherson; Junhak Lee

2016-01-01

The past decade could be called a renaissance of urban forestry, driven by mayoral tree planting initiatives and increased attention on city trees as green infrastructure. The political support for urban greening has been fueled by research that quantifies and projects the ecosystem services of planting initiatives (Young and McPherson 2013). Major cities have been...

Tree improvement opportunities in the North-Central States related to economic trends, a problem analysis.

Treesearch

David H. Dawson; John A. Pitcher

1970-01-01

Economic trends are interpreted and related to planning applied forest tree-improvement programs for the North-Central Region. Projected demands for forest products are considered in light of the forest resource and alternatives for its use. Suggestions are given for tree-improvement programs for seven conifer and three hardwood species.

AN EXAMINATION OF CITIZEN PARTICIPATION AND PROCEDURAL FAIRNESS IN LARGE-SCALE URBAN TREE PLANTING INITIATIVES IN THE UNITED STATES

EPA Science Inventory

This project will result in a typology of the degrees and forms of citizen participation in large-scale urban tree planting initiatives. It also will identify specific aspects of urban tree planting processes that residents perceive as fair and unfair, which will provide ad...

Can Children Read Evolutionary Trees?

ERIC Educational Resources Information Center

Ainsworth, Shaaron; Saffer, Jessica

2013-01-01

Representations of the "tree of life" such as cladograms show the history of lineages and their relationships. They are increasingly found in formal and informal learning settings. Unfortunately, there is evidence that these representations can be challenging to interpret correctly. This study explored the question of whether children…

Falling into Winter.

ERIC Educational Resources Information Center

Harrington, Carolyn Lang

2000-01-01

Presents an activity that connects art, science, and nature in which elementary school students learn about deciduous trees. Explains that students create a torn-tissue collage, using fall colors for a background and drawing a silhouette of a tree without leaves on top of the background with black crayon. (CMK)

Young Children Learn to Restructure Personal Ideas about Growth in Trees.

ERIC Educational Resources Information Center

Sunal, Dennis W.; Sunal, Cynthia S.

1991-01-01

Three activities in which students identify and demonstrate similarities and differences in plants and in plant growth are presented. The activities include background information, objectives for the tree ring activities, a list of needed materials, procedures, evaluation procedures, and a glossary. (KR)

The water balance components of Mediterranean pine trees on a steep mountain slope during two hydrologically contrasting years

NASA Astrophysics Data System (ADS)

Eliades, Marinos; Bruggeman, Adriana; Lubczynski, Maciek W.; Christou, Andreas; Camera, Corrado; Djuma, Hakan

2018-07-01

Pines in semi-arid mountain environments manage to survive and thrive despite the limited soil water, due to shallow soil depths, and overall water scarcity. This study aims to develop a method for computing soil evaporation, bedrock water uptake and transpiration from a natural, open forest, based on sap flow (Heat Ratio Method), soil moisture and meteorological observations. The water balance of individual trees was conceptualized with a geometric approach, using canopy projected areas and Voronoi (Thiesen) polygons. The canopy approach assumes that the tree's root area extent is equal to its canopy projected area, while the Voronoi approach assumes that the tree roots exploit the open area that is closer to the tree than to any other tree. The methodology was applied in an open Pinus brutia forest (68% canopy cover) in Cyprus, characterized by steep slopes and fractured bedrock, during two hydrologically contrasting years (2015 wet, 2016 dry). Sap flow sensors, soil moisture sensors, throughfall and stemflow gauges were installed on and around eight trees. Rainfall was 507 mm in 2015 and 359 mm in 2016. According to the canopy approach, the sum of tree transpiration and soil evaporation exceeded the throughfall in both years, which implies that the trees' bedrock water uptake exceeds the surface runoff and drainage losses. This indicated that trees extend their roots beyond the canopy-projected areas and the use of the Voronoi polygons captures this effect. According to the stand scale water balance, average throughfall during the two years was 81% of the rainfall. Transpiration was 61% of the rainfall in 2015, but only 32% in 2016. On the contrary, the soil evaporation fraction increased from 26% in 2015 to 35% in the dry year of 2016. The contribution of bedrock water to tree transpiration was 77% of rainfall in 2015 and 66% in 2016. During the summer months, trees relied 100% on the uptake of water from the fractured bedrock to cover their transpiration needs. Average monthly transpiration areas ranged between 0.1 mm d-1 in October 2016 and 1.7 mm d-1 in April 2015. This study shows that bedrock uptake could be an essential water balance component of semi-arid, mountainous pine forests and should be accounted for in hydrologic models.

Application of data mining approaches to drug delivery.

PubMed

Ekins, Sean; Shimada, Jun; Chang, Cheng

2006-11-30

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Mapping tree density in forests of the southwestern USA using Landsat 8 data

USGS Publications Warehouse

Humagain, Kamal; Portillo-Quintero, Carlos; Cox, Robert D.; Cain, James W.

2017-01-01

The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100–1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

36 CFR 327.14 - Public property.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... (b) Cutting or gathering of trees or parts of trees and/or the removal of wood from project lands is prohibited without written permission of the District Commander. (c) Gathering of dead wood on the ground for...

Acceleration and novelty: community restoration speeds recovery and transforms species composition in Andean cloud forest.

PubMed

Wilson, Sarah Jane; Rhemtulla, Jeanine M

2016-01-01

Community-based tropical forest restoration projects, often promoted as a win-win solution for local communities and the environment, have increased dramatically in number in the past decade. Many such projects are underway in Andean cloud forests, which, given their extremely high biodiversity and history of extensive clearing, are understudied. This study investigates the efficacy of community-based tree-planting projects to accelerate cloud forest recovery, as compared to unassisted natural regeneration. This study takes place in northwest Andean Ecuador, where the majority of the original, highly diverse cloud forests have been cleared, in five communities that initiated tree-planting projects to restore forests in 2003. In 2011, we identified tree species along transects in planted forests (n = 5), naturally regenerating forests (n = 5), and primary forests (n = 5). We also surveyed 120 households about their restoration methods, tree preferences, and forest uses. We found that tree diversity was higher in planted than in unplanted secondary forest, but both were less diverse than primary forests. Ordination analysis showed that all three forests had distinct species compositions, although planted forests shared more species with primary forests than did unplanted forests. Planted forests also contained more animal-dispersed species in both the planted canopy and in the unplanted, regenerating understory than unplanted forests, and contained the highest proportion of species with use value for local people. While restoring forest increased biodiversity and accelerated forest recovery, restored forests may also represent novel ecosystems that are distinct from the region's previous ecosystems and, given their usefulness to people, are likely to be more common in the future.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2013-07-01

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

Re-Construction of Reference Population and Generating Weights by Decision Tree

DTIC Science & Technology

2017-07-21

2017 Claflin University Orangeburg, SC 29115 DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE RESEARCH, DEVELOPMENT, AND STRATEGIC...Original Dataset 32 List of Figures in Appendix B Figure 1: Flow and Components of Project 20 Figure 2: Decision Tree 31 Figure 3: Effects of Weight...can compare the sample data. The dataset of this project has the reference population on unit level for group and gender, which is in red-dotted box

Informal Science Learning through Inquiry: Effects on Preschool Students' Achievement in Early Science Learning

ERIC Educational Resources Information Center

Samsudin, Mohd Ali; Haniza, Noor Hasyimah; Ismail, Juliah; Abd-Talib, Corrienna

2015-01-01

This study was undertaken to explore the effects of informal science learning outside the classroom on preschool students' achievement in the Early Science learning topic (plant-related topics that presented concepts about tree leaves, height and roots) using an inquiry method. A sample of 64 preschool students was selected using purposive…

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters. Overall, the probability of detection of an event with a return period of 10 years is 62%. 44% of all 10-year flood peaks can be detected with a timing error of 2 hours or less. These results indicate that the modeling system can provide useful information about the timing and magnitude of flood events at ungauged sites.

SETs: stand evaluation tools: I. an individual-tree approach to making stand evaluations

Treesearch

Paul S. DeBald; Joseph J. Mendel

1976-01-01

The authors outline a stand-evaluation method that stresses individuality by (1) making on-the-ground projections of individual tree development; (2) summarizing stand values in terms of the individual trees in the stand and their potential development; and (3) tailoring several management possibilities to an individual stand so the owner can choose among them.

Assessing the stability of tree ranges and influence of disturbance in eastern US forests

Treesearch

C.W. Woodall; K. Zhu; J.A. Westfall; C.M. Oswalt; A.W. D' Amato; B.F. Walters; H.E. Lintz

2013-01-01

Shifts in tree species ranges may occur due to global climate change, which in turn may be exacerbated by natural disturbance events. Within the context of global climate change, developing techniques to monitor tree range dynamics as affected by natural disturbances may enable mitigation/adaptation of projected impacts. Using a forest inventory across the eastern U.S...

Projections of forest contributions to global carbon cycles

Treesearch

Michael E. Goerndt; Stephen R. Shifley; Patrick D. Miles; Dave Wear; Francisco X. Aguilar

2016-01-01

Forests cover 42 percent of the Northern United States, and collectively they store 13 billion tons of carbon in live trees (29 percent), roots (6 percent), forest floor (9 percent), dead trees (6 percent), and soils (50 percent). About half the biomass of a live tree (dry weight basis) is sequestered carbon (Woodall et al. 2011) - not the largest but the most dynamic...

Adjustments of individual-tree survival and diameter-growth equations to match whole-stand attributes

Treesearch

Quang V. Cao

2010-01-01

Individual-tree models are flexible and can perform well in predicting tree survival and diameter growth for a certain growing period. However, the resulting stand-level outputs often suffer from accumulation of errors and subsequently cannot compete with predictions from whole-stand models, especially when the projection period lengthens. Evaluated in this study were...

Past and ongoing shifts in Joshua tree distribution support future modeled range contraction

Treesearch

Kenneth L. Cole; Kirsten Ironside; Jon Eischeid; Gregg Garfin; Phillip B. Duffy; Chris Toney

2011-01-01

The future distribution of the Joshua tree (Yucca brevifolia) is projected by combining a geostatistical analysis of 20th-century climates over its current range, future modeled climates, and paleoecological data showing its response to a past similar climate change. As climate rapidly warmed ~11 700 years ago, the range of Joshua tree contracted, leaving only the...

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Treesearch

David J. P. Moore; Nicole A. Trahan; Phil Wilkes; Tristan Quaife; Britton B. Stephens; Kelly Elder; Ankur R. Desai; Jose Negron; Russell K. Monson

2013-01-01

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no...

Mapping Tree Density at the Global Scale

NASA Astrophysics Data System (ADS)

Covey, K. R.; Crowther, T. W.; Glick, H.; Bettigole, C.; Bradford, M.

2015-12-01

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global-scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.39 trillion exist in tropical and subtropical regions, with 0.74, and 0.61 trillion in boreal and temperate regions, respectively. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming impact of humans across most of the world. Based on our projected tree densities, we estimate that deforestation is currently responsible for removing over 15 billion trees each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Mapping tree density at a global scale

NASA Astrophysics Data System (ADS)

Crowther, T. W.; Glick, H. B.; Covey, K. R.; Bettigole, C.; Maynard, D. S.; Thomas, S. M.; Smith, J. R.; Hintler, G.; Duguid, M. C.; Amatulli, G.; Tuanmu, M.-N.; Jetz, W.; Salas, C.; Stam, C.; Piotto, D.; Tavani, R.; Green, S.; Bruce, G.; Williams, S. J.; Wiser, S. K.; Huber, M. O.; Hengeveld, G. M.; Nabuurs, G.-J.; Tikhonova, E.; Borchardt, P.; Li, C.-F.; Powrie, L. W.; Fischer, M.; Hemp, A.; Homeier, J.; Cho, P.; Vibrans, A. C.; Umunay, P. M.; Piao, S. L.; Rowe, C. W.; Ashton, M. S.; Crane, P. R.; Bradford, M. A.

2015-09-01

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.39 trillion exist in tropical and subtropical forests, with 0.74 trillion in boreal regions and 0.61 trillion in temperate regions. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming effect of humans across most of the world. Based on our projected tree densities, we estimate that over 15 billion trees are cut down each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Mapping tree density at a global scale.

PubMed

Crowther, T W; Glick, H B; Covey, K R; Bettigole, C; Maynard, D S; Thomas, S M; Smith, J R; Hintler, G; Duguid, M C; Amatulli, G; Tuanmu, M-N; Jetz, W; Salas, C; Stam, C; Piotto, D; Tavani, R; Green, S; Bruce, G; Williams, S J; Wiser, S K; Huber, M O; Hengeveld, G M; Nabuurs, G-J; Tikhonova, E; Borchardt, P; Li, C-F; Powrie, L W; Fischer, M; Hemp, A; Homeier, J; Cho, P; Vibrans, A C; Umunay, P M; Piao, S L; Rowe, C W; Ashton, M S; Crane, P R; Bradford, M A

2015-09-10

The global extent and distribution of forest trees is central to our understanding of the terrestrial biosphere. We provide the first spatially continuous map of forest tree density at a global scale. This map reveals that the global number of trees is approximately 3.04 trillion, an order of magnitude higher than the previous estimate. Of these trees, approximately 1.39 trillion exist in tropical and subtropical forests, with 0.74 trillion in boreal regions and 0.61 trillion in temperate regions. Biome-level trends in tree density demonstrate the importance of climate and topography in controlling local tree densities at finer scales, as well as the overwhelming effect of humans across most of the world. Based on our projected tree densities, we estimate that over 15 billion trees are cut down each year, and the global number of trees has fallen by approximately 46% since the start of human civilization.

Risk Management in Complex Construction Projects that Apply Renewable Energy Sources: A Case Study of the Realization Phase of the Energis Educational and Research Intelligent Building

NASA Astrophysics Data System (ADS)

Krechowicz, Maria

2017-10-01

Nowadays, one of the characteristic features of construction industry is an increased complexity of a growing number of projects. Almost each construction project is unique, has its project-specific purpose, its own project structural complexity, owner’s expectations, ground conditions unique to a certain location, and its own dynamics. Failure costs and costs resulting from unforeseen problems in complex construction projects are very high. Project complexity drivers pose many vulnerabilities to a successful completion of a number of projects. This paper discusses the process of effective risk management in complex construction projects in which renewable energy sources were used, on the example of the realization phase of the ENERGIS teaching-laboratory building, from the point of view of DORBUD S.A., its general contractor. This paper suggests a new approach to risk management for complex construction projects in which renewable energy sources were applied. The risk management process was divided into six stages: gathering information, identification of the top, critical project risks resulting from the project complexity, construction of the fault tree for each top, critical risks, logical analysis of the fault tree, quantitative risk assessment applying fuzzy logic and development of risk response strategy. A new methodology for the qualitative and quantitative risk assessment for top, critical risks in complex construction projects was developed. Risk assessment was carried out applying Fuzzy Fault Tree analysis on the example of one top critical risk. Application of the Fuzzy sets theory to the proposed model allowed to decrease uncertainty and eliminate problems with gaining the crisp values of the basic events probability, common during expert risk assessment with the objective to give the exact risk score of each unwanted event probability.

EvolView, an online tool for visualizing, annotating and managing phylogenetic trees.

PubMed

Zhang, Huangkai; Gao, Shenghan; Lercher, Martin J; Hu, Songnian; Chen, Wei-Hua

2012-07-01

EvolView is a web application for visualizing, annotating and managing phylogenetic trees. First, EvolView is a phylogenetic tree viewer and customization tool; it visualizes trees in various formats, customizes them through built-in functions that can link information from external datasets, and exports the customized results to publication-ready figures. Second, EvolView is a tree and dataset management tool: users can easily organize related trees into distinct projects, add new datasets to trees and edit and manage existing trees and datasets. To make EvolView easy to use, it is equipped with an intuitive user interface. With a free account, users can save data and manipulations on the EvolView server. EvolView is freely available at: http://www.evolgenius.info/evolview.html.

EvolView, an online tool for visualizing, annotating and managing phylogenetic trees

PubMed Central

Zhang, Huangkai; Gao, Shenghan; Lercher, Martin J.; Hu, Songnian; Chen, Wei-Hua

2012-01-01

EvolView is a web application for visualizing, annotating and managing phylogenetic trees. First, EvolView is a phylogenetic tree viewer and customization tool; it visualizes trees in various formats, customizes them through built-in functions that can link information from external datasets, and exports the customized results to publication-ready figures. Second, EvolView is a tree and dataset management tool: users can easily organize related trees into distinct projects, add new datasets to trees and edit and manage existing trees and datasets. To make EvolView easy to use, it is equipped with an intuitive user interface. With a free account, users can save data and manipulations on the EvolView server. EvolView is freely available at: http://www.evolgenius.info/evolview.html. PMID:22695796

Simulation of Drought-induced Tree Mortality Using a New Individual and Hydraulic Trait-based Model (S-TEDy)

NASA Astrophysics Data System (ADS)

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.

ColorTree: a batch customization tool for phylogenic trees

PubMed Central

Chen, Wei-Hua; Lercher, Martin J

2009-01-01

Background Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. Findings In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. Conclusion ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files. PMID:19646243

ColorTree: a batch customization tool for phylogenic trees.

PubMed

Chen, Wei-Hua; Lercher, Martin J

2009-07-31

Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files.

A Grab Bag of Nature Activities.

ERIC Educational Resources Information Center

Miller, Lenore

1993-01-01

Suggested nature activities include (1) sensory experiences to distinguish all characteristics of various objects; (2) adopt-a-tree activities where children learn about "their own" tree; (3) finding evidence of animals in nature; (4) nature questions of the week with prizes for correct answers; and (5) activities related to the…

A Universal Phylogenetic Tree.

ERIC Educational Resources Information Center

Offner, Susan

2001-01-01

Presents a universal phylogenetic tree suitable for use in high school and college-level biology classrooms. Illustrates the antiquity of life and that all life is related, even if it dates back 3.5 billion years. Reflects important evolutionary relationships and provides an exciting way to learn about the history of life. (SAH)

TreeQ-VISTA: An Interactive Tree Visualization Tool withFunctional Annotation Query Capabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gu, Shengyin; Anderson, Iain; Kunin, Victor

2007-05-07

Summary: We describe a general multiplatform exploratorytool called TreeQ-Vista, designed for presenting functional annotationsin a phylogenetic context. Traits, such as phenotypic and genomicproperties, are interactively queried from a relational database with auser-friendly interface which provides a set of tools for users with orwithout SQL knowledge. The query results are projected onto aphylogenetic tree and can be displayed in multiple color groups. A richset of browsing, grouping and query tools are provided to facilitatetrait exploration, comparison and analysis.Availability: The program,detailed tutorial and examples are available online athttp://genome-test.lbl.gov/vista/TreeQVista.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

PubMed

Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

2016-11-01

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

A new approach to enhance the performance of decision tree for classifying gene expression data.

PubMed

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

How To Assess The Future Tree-Cover Potential For Reforestation Planning In Semi-Arid Regions? An Attempt Using The Vegetation Model ORCHIDEE

NASA Astrophysics Data System (ADS)

Rajaud, A.; De Noblet-Ducoudré, N.

2015-12-01

More and more reforestation projects are undertaken at local to continental scales to fight desertification, to address development challenges, and to improve local living conditions in tropical semi-arid regions. These regions are very sensitive to climatic changes and the potential for maintaining tree-covers will be altered in the next decades. Therefore, reforestation planning needs predicting the future "climatic tree-cover potential": the optimum tree-fraction sustainable in future climatic states. Global circulation models projections provide possible future climatologies for the 21st century. These can be used at the global scale to force a land-surface model, which in turn simulates the vegetation development under these conditions. The tree cover leading to an optimum development may then be identified. We propose here to run a state-of-the-art model and to assess the span and the relevance of the answers that can be obtained for reforestation planning. The ORCHIDEE vegetation model is chosen here to allow a multi-criteria evaluation of the optimum cover, as it returns surface climate state variables as well as vegetation functioning and biomass products. It is forced with global climate data (WFDEI and CRU) for the 20th century and models projections (CMIP5 outputs) for the 21st century. At the grid-cell resolution of the forcing climate data, tree-covers ranging from 0 to 100% are successively prescribed. A set of indicators is then derived from the model outputs, meant for modulating reforestation strategies according to the regional priorities (e.g. maximize the biomass production or decrease the surface air temperature). The choice of indicators and the relevance of the final answers provided will be collectively assessed by the climate scientists and reforestation project management experts from the KINOME social enterprise (http://en.kinome.fr). Such feedback will point towards the model most urging needs for improvement.

Using decision-tree classifier systems to extract knowledge from databases

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

A template-finding algorithm and a comprehensive benchmark for homology modeling of proteins

PubMed Central

Vallat, Brinda Kizhakke; Pillardy, Jaroslaw; Elber, Ron

2010-01-01

The first step in homology modeling is to identify a template protein for the target sequence. The template structure is used in later phases of the calculation to construct an atomically detailed model for the target. We have built from the Protein Data Bank a large-scale learning set that includes tens of millions of pair matches that can be either a true template or a false one. Discriminatory learning (learning from positive and negative examples) is employed to train a decision tree. Each branch of the tree is a mathematical programming model. The decision tree is tested on an independent set from PDB entries and on the sequences of CASP7. It provides significant enrichment of true templates (between 50-100 percent) when compared to PSI-BLAST. The model is further verified by building atomically detailed structures for each of the tentative true templates with modeller. The probability that a true match does not yield an acceptable structural model (within 6Å RMSD from the native structure), decays linearly as a function of the TM structural-alignment score. PMID:18300226

The Design of a Polymorphous Cognitive Agent Architecture (PCAA)

DTIC Science & Technology

2008-05-01

tree, and search agents may search the tree for documents or clusters, depositing pheromones on the way down the tree. 46 19 The quality of SODAS...location in the lattice is a node, connected to its neighbors by links, and agents roam across the lattice, depositing pheromones . 49 21 A possible FPGA...provided by swarming, and also figure out a way for learning in ACT-R to trickle down to swarming computations, e.g., through the pheromones . Integration

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

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.

Privacy-Preserving Classifier Learning

NASA Astrophysics Data System (ADS)

Brickell, Justin; Shmatikov, Vitaly

We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database held by a remote server without learning any additional information about the records held in the database. The server does not learn anything about the constructed classifier, not even the user’s choice of feature and class attributes.

The Community of Learning Is in the Baobab Tree--How the Branches Stay Together in the Context of Professional Preparation

ERIC Educational Resources Information Center

Wolfensberger-Le Fevre, Celeste; Fritz, Elzette; van der Westhuizen, Gert

2011-01-01

This article explores how participation in a community of learning supported transformation on a personal and professional level in a Master's programme at a South African university. It draws on the concept of transformational learning in the professional preparation of educational psychologists, and how such learning plays out in the development…

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

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

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

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

Predicting past and future diameter growth for trees in the northeastern United States

Treesearch

James A. Westfall

2006-01-01

Tree diameter growth models are widely used in forestry applications, often to predict tree size at a future point in time. Also, there are instances where projections of past diameters are needed. A relative diameter growth model was developed to allow prediction of both future and past growth rates. Coefficients were estimated for 15 species groups that cover most...

An empirical, hierarchical typology of tree species assemblages for assessing forest dynamics under global change scenarios

Treesearch

Jennifer K. Costanza; John W. Coulston; David N. Wear

2017-01-01

The composition of tree species occurring in a forest is important and can be affected by global change drivers such as climate change. To inform assessment and projection of global change impacts at broad extents, we used hierarchical cluster analysis and over 120,000 recent forest inventory plots to empirically define forest tree assemblages across the U.S., and...

Control of pest species: Tree shelters help protect seedlings from nutria

USGS Publications Warehouse

Allen, J.A.; Boykin, R.

1991-01-01

Various methods of nutria preventative techniques were tested in attempts to curb the loss of seedlings due to nutria capturing. The results of testing possibly indicate that tree shelters have real potential for use in forest restoration projects on sites with moderate nutria populations. Tree shelters may even prove effective on sites with high nutria populations, as long as alternative food supplies are available.

Simulated effects of climate change, fragmentation, and inter-specific competition on tree species migration in northern Wisconsin, USA

Treesearch

Robert M. Scheller; David J. Mladenoff

2008-01-01

The reproductive success, growth, and mortality rates of tree species in the northern United States will be differentially affected by projected climate change over the next century. As a consequence, the spatial distributions of tree species will expand or contract at differential rates. In addition, human fragmentation of the landscape may limit effective seed...

Ecological Restoration Through Silviculture--A Savanna Management Demonstration Area, Sinkin Experimental Forest, Missouri

Treesearch

Edward F. Loewenstein; Kenneth R. Davidson

2002-01-01

In 1998, a project was initiated to demonstrate techniques and evaluate the efficacy of reducing overstory tree density and reintroducing fire in order to develop the tree composition, structure, and herbaceous complex typical of a savanna. On three study areas, two dominated by oak and one by shortleaf pine, the total basal area of all trees = 1.6 inches DBH was...

Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance

Treesearch

Elizabeth A. Freeman; Gretchen G. Moisen; John W. Coulston; Barry T. (Ty) Wilson

2015-01-01

As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF...

Ecology Art Education On-Line: A World Community of Old Trees, A Story of the Research

ERIC Educational Resources Information Center

Julian, June

1997-01-01

"A World Community of Old Trees," http://www.nyu.edu/projects/julian/, is the Internet research component of the doctoral dissertation, "Ecology Art Education On-Line: A World Community of Old Trees." It is the first study in the discipline of Art Education to use the World Wide Web to transmit and receive data for doctoral…

OAKSIM: An individual-tree growth and yield simulator for managed, even-aged, upland oak stands

Treesearch

Donald E. Hilt; Donald E. Hilt

1985-01-01

OAKSIM is an individual-tree growth and yield simulator for managed, even-aged, upland oak stands. Growth and yield projections for various thinning alternatives can be made with OAKSIM for a period of up to 50 years. Simulator components include an individual-tree diameter growth model, a mortality model, height prediction equations, bark ratio equations, a taper-...

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

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

Accounting for Landscape Heterogeneity Improves Spatial Predictions of Tree Vulnerability to Drought

NASA Astrophysics Data System (ADS)

Schwantes, A. M.; Parolari, A.; Swenson, J. J.; Johnson, D. M.; Domec, J. C.; Jackson, R. B.; Pelak, N. F., III; Porporato, A. M.

2017-12-01

Globally, as climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability differs regionally and locally depending on landscape position. However, most models used in forecasting forest responses to heatwaves and droughts do not incorporate relevant spatial processes. To improve predictions of spatial tree vulnerability, we employed a non-linear stochastic model of soil moisture dynamics across a landscape, accounting for spatial differences in aspect, topography, and soils. Our unique approach integrated plant hydraulics and landscape processes, incorporating effects from lateral redistribution of water using a topographic index and radiation and temperature differences attributable to aspect. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei. We compared our results to a detailed spatial dataset of drought-impacted areas (>25% canopy loss) derived from remote sensing during the severe 2011 drought. We then projected future dynamic water stress through the 21st century using climate projections from 10 global climate models under two scenarios, and compared models with and without landscape heterogeneity. Within this watershed, 42% of J. ashei dominated systems were impacted by the 2011 drought. Modeled dynamic water stress tracked these spatial patterns of observed drought-impacted areas. Total accuracy increased from 59%, when accounting only for soil variability, to 73% when including lateral redistribution of water and radiation and temperature effects. Dynamic water stress was projected to increase through the 21st century, with only minimal buffering from the landscape. During the hotter and more severe droughts projected in the 21st century, up to 90% of the watershed crossed a dynamic water stress threshold associated with canopy loss in 2011. Favorable microsites may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of a heterogenous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

Strategies for reforestation under uncertain future climates: guidelines for Alberta, Canada.

PubMed

Gray, Laura K; Hamann, Andreas

2011-01-01

Commercial forestry programs normally use locally collected seed for reforestation under the assumption that tree populations are optimally adapted to local environments. However, in western Canada this assumption is no longer valid because of climate trends that have occurred over the last several decades. The objective of this study is to show how we can arrive at reforestation recommendations with alternative species and genotypes that are viable under a majority of climate change scenarios. In a case study for commercially important tree species of Alberta, we use an ecosystem-based bioclimate envelope modeling approach for western North America to project habitat for locally adapted populations of tree species using multi-model climate projections for the 2020s, 2050s and 2080s. We find that genotypes of species that are adapted to drier climatic conditions will be the preferred planting stock over much of the boreal forest that is commercially managed. Interestingly, no alternative species that are currently not present in Alberta can be recommended with any confidence. Finally, we observe large uncertainties in projections of suitable habitat that make reforestation planning beyond the 2050s difficult for most species. More than 50,000 hectares of forests are commercially planted every year in Alberta. Choosing alternative planting stock, suitable for expected future climates, could therefore offer an effective climate change adaptation strategy at little additional cost. Habitat projections for locally adapted tree populations under observed climate change conform well to projections for the 2020s, which suggests that it is a safe strategy to change current reforestation practices and adapt to new climatic realities through assisted migration prescriptions.

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

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

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

Climatic changes lead to declining winter chill for fruit and nut trees in California during 1950-2099.

PubMed

Luedeling, Eike; Zhang, Minghua; Girvetz, Evan H

2009-07-16

Winter chill is one of the defining characteristics of a location's suitability for the production of many tree crops. We mapped and investigated observed historic and projected future changes in winter chill in California, quantified with two different chilling models (Chilling Hours, Dynamic Model). Based on hourly and daily temperature records, winter chill was modeled for two past temperature scenarios (1950 and 2000), and 18 future scenarios (average conditions during 2041-2060 and 2080-2099 under each of the B1, A1B and A2 IPCC greenhouse gas emissions scenarios, for the CSIRO-MK3, HadCM3 and MIROC climate models). For each scenario, 100 replications of the yearly temperature record were produced, using a stochastic weather generator. We then introduced and mapped a novel climatic statistic, "safe winter chill", the 10% quantile of the resulting chilling distributions. This metric can be interpreted as the amount of chilling that growers can safely expect under each scenario. Winter chill declined substantially for all emissions scenarios, with the area of safe winter chill for many tree species or cultivars decreasing 50-75% by mid-21st century, and 90-100% by late century. Both chilling models consistently projected climatic conditions by the middle to end of the 21st century that will no longer support some of the main tree crops currently grown in California, with the Chilling Hours Model projecting greater changes than the Dynamic Model. The tree crop industry in California will likely need to develop agricultural adaptation measures (e.g. low-chill varieties and dormancy-breaking chemicals) to cope with these projected changes. For some crops, production might no longer be possible.

Past and ongoing shifts in Joshua tree distribution support future modeled range contraction

USGS Publications Warehouse

Cole, Kenneth L.; Ironside, Kirsten; Eischeid, Jon K.; Garfin, Gregg; Duffy, Phil; Toney, Chris

2011-01-01

The future distribution of the Joshua tree (Yucca brevifolia) is projected by combining a geostatistical analysis of 20th-century climates over its current range, future modeled climates, and paleoecological data showing its response to a past similar climate change. As climate rapidly warmed ;11 700 years ago, the range of Joshua tree contracted, leaving only the populations near what had been its northernmost limit. Its ability to spread northward into new suitable habitats after this time may have been inhibited by the somewhat earlier extinction of megafaunal dispersers, especially the Shasta ground sloth. We applied a model of climate suitability for Joshua tree, developed from its 20th-century range and climates, to future climates modeled through a set of six individual general circulation models (GCM) and one suite of 22 models for the late 21st century. All distribution data, observed climate data, and future GCM results were scaled to spatial grids of ;1 km and ;4 km in order to facilitate application within this topographically complex region. All of the models project the future elimination of Joshua tree throughout most of the southern portions of its current range. Although estimates of future monthly precipitation differ between the models, these changes are outweighed by large increases in temperature common to all the models. Only a few populations within the current range are predicted to be sustainable. Several models project significant potential future expansion into new areas beyond the current range, but the species' Historical and current rates of dispersal would seem to prevent natural expansion into these new areas. Several areas are predicted to be potential sites for relocation/ assisted migration. This project demonstrates how information from paleoecology and modern ecology can be integrated in order to understand ongoing processes and future distributions.

Potential Effects of Drought on Tree Dieback in Great Britain and Implications for Forest Management in Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Yu, Jianjun; Berry, Pam

2017-04-01

The drought and heat stress has alerted the composition, structure and biogeography of forests globally, whilst the projected severe and widespread droughts are potentially increasing. This challenges the sustainable forest management to better cope with future climate and maintain the forest ecosystem functions and services. Many studies have investigated the climate change impacts on forest ecosystem but less considered the climate extremes like drought. In this study, we implement a dynamic ecosystem model based on a version of LPJ-GUESS parameterized with European tree species and apply to Great Britain at a finer spatial resolution of 5*5 km. The model runs for the baseline from 1961 to 2011 and projects to the latter 21st century using 100 climate scenarios generated from MaRIUS project to tackle the climate model uncertainty. We will show the potential impacts of climate change on forest ecosystem and vegetation transition in Great Britain by comparing the modelled conditions in the 2030s and the 2080s relative to the baseline. In particular, by analyzing the modelled tree mortality, we will show the tree dieback patterns in response to drought for various species, and assess their drought vulnerability across Great Britain. We also use species distribution modelling to project the suitable climate space for selected tree species using the same climate scenarios. Aided by these two modelling approaches and based on the corresponding modelling results, we will discuss the implications for adaptation strategy for forest management, especially in extreme drought conditions. The gained knowledge and lessons for Great Britain are considered to be transferable in many other regions.

Climate and Vegetation Effects on Temperate Mountain Forest Evapotranspiration

EPA Science Inventory

Current forest composition may be resilient to typical climatic variability; however, climate trends, combined with projected changes in species composition, may increase tree vulnerability to water stress. A shift in forest composition toward tree species with higher water use h...

Transformative Sustainability Learning: Cultivating a Tree-Planting Ethos in Western Kenya

ERIC Educational Resources Information Center

Bull, Marijoan

2013-01-01

Given the fundamental objective of ESD--perspective change--it is increasingly being aligned with the theoretical foundation of Mezirow's Transformative Learning. In 2008, Sipos et al. built upon this connection by proposing a matrix of learning objectives to assess ESD in formal settings. These objectives, grouped under the title of…

Dead Wolves, Dead Birds, and Dead Trees: Catalysts for Transformative Learning in the Making of Scientist-Environmentalists

ERIC Educational Resources Information Center

Walter, Pierre

2013-01-01

This historical study identifies catalysts for transformative learning in the lives of three scientist-environmentalists important to the 20th-century environmental movement: Aldo Leopold, Rachel Carson, and David Suzuki. Following a brief review of theoretical perspectives on transformative learning, the article argues that transformative…

Topsoil depth substantially influences the responses to drought of the foliar metabolomes of Mediterranean forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rivas-Ubach, Albert; Barbeta, Adrià; Sardans, Jordi

Soils provide physical support, water, and nutrients to terrestrial plants. Upper soil layers are crucial for forest dynamics, especially under drought conditions, because many biological processes occur there and provide support, water and nutrients to terrestrial plants. We postulated that tree size and overall plant function manifested in the metabolome composition, the total set of metabolites, were dependent on the depth of upper soil layers and on water availability. We sampled leaves for stoichiometric and metabolomic analyses once per season from differently sized Quercus ilex trees under natural and experimental drought conditions as projected for the coming decades. Different sizedmore » trees had different metabolomes and plots with shallower soils had smaller trees. Soil moisture of the upper soil did not explain the tree size and smaller trees did not show higher concentrations of biomarker metabolites related to drought stress. However, the impact of drought treatment on metabolomes was higher in smaller trees in shallower soils. Our results suggested that tree size was more dependent on the depth of the upper soil layers, which indirectly affect the metabolomes of the trees, than on the moisture content of the upper soil layers. Metabolomic profiling of Q. ilex supported the premise that water availability in the upper soil layers was not necessarily correlated with tree size. The higher impact of drought on trees growing in shallower soils nevertheless indicates a higher vulnerability of small trees to the future increase in frequency, intensity, and duration of drought projected for the Mediterranean Basin and other areas. Metabolomics has proven to be an excellent tool detecting significant metabolic changes among differently sized individuals of the same species and it improves our understanding of the connection between plant metabolomes and environmental variables such as soil depth and moisture content.« less

The Gift of the Tree

ERIC Educational Resources Information Center

Jones, Marla Wagner

2009-01-01

A piece of children's literature can be a powerful tool for teaching and learning science; however, it takes more than reading about a topic to qualify as "doing science." Inspired by the book, "The Gift of the Tree", the author developed an in-depth interdisciplinary lesson for her sixth-grade students without diluting the science. Through this…

The Tree of Animal Life

ERIC Educational Resources Information Center

Braude, Stan

2007-01-01

In this article, the author describes a short activity which introduces third- to fifth-grade students to animal classification. The Tree of Animal Life activity is a simple, sorting exercise that can help them see a bigger picture. The activity sets the stage for learning about animal taxonomy and introduces the characteristics of various animal…

Do People Grow on Family Trees? Genealogy for Kids & Other Beginners. Teacher's Guide.

ERIC Educational Resources Information Center

Wolfman, Ira

This teacher's guide to "Do People Grow on Family Trees?" provides the classroom teacher with thought-provoking discussion topics and questions and curriculum-enhancing activities. It presents objective-based, action learning strategies that involve students in the following: simulation situations that lead to problem solving and other…

Parks, Trees, and Environmental Justice: Field Notes from Washington, DC

ERIC Educational Resources Information Center

Buckley, Geoffrey L.; Whitmer, Ali; Grove, J. Morgan

2013-01-01

Students enrolled in a graduate seminar benefited in multiple ways from an intensive 3-day field trip to Washington, DC. Constructed around the theme of environmental justice, the trip gave students a chance to learn about street tree distribution, park quality, and racial segregation "up close." Working with personnel from the United…

Oak Wilt: People and Trees, A Community Approach to Management

Treesearch

J. Juzwik; S. Cook; L. Haugen; J. Elwell

2004-01-01

Version 1.3. This self-paced short course on CD-ROM was designed as a learning tool for urban and community foresters, city administrators, tree inspectors, parks and recreation staff, and others involved in oak wilt management.Click the "View or print this publication" link below to request your Oak Wilt: People and...

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Fifth Graders' Interpretations of "The Red Tree"

ERIC Educational Resources Information Center

Barone, Diane; Barone, Rebecca

2017-01-01

Fifth graders responded to a video of a picturebook,"The Red Tree" by Shaun Tan. They had not experienced explicit instruction in visual literacy and their responses served as a foundation for basic understanding of their analysis. We learned that they focused on four major areas: emotional aspects; visual qualities, summaries of the…

Decision tree modeling using R.

PubMed

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.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Forest tree improvement at Michigan State University: Past, present, and future

Treesearch

Paul Bloese

2010-01-01

The Department of Forestry at Michigan State University has engaged in forest tree improvement for more than 50 years. This paper presents a brief historical perspective on past research, the status of current projects, and outlines plans for the future.

Progress report to the International Fruit Tree Association

USDA-ARS?s Scientific Manuscript database

This report provides an update on several projects that are fostered by the International Fruit Tree Association which covers some aspects of rootstock development, performance in the orchard and to address nursery industry needs. The report highlights results from graft union strength experiments,...

75 FR 10457 - Andrew Pickens Ranger District; South Carolina; AP Loblolly Pine Removal and Restoration Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-08

... relatively low tree densities of 25-60% forest cover with understories that are dominated by native grasses... relatively low tree densities of 25-60% forest cover with understories that are dominated by native grasses...

A greener Greenland? Climatic potential and long-term constraints on future expansions of trees and shrubs

PubMed Central

Normand, Signe; Randin, Christophe; Ohlemüller, Ralf; Bay, Christian; Høye, Toke T.; Kjær, Erik D.; Körner, Christian; Lischke, Heike; Maiorano, Luigi; Paulsen, Jens; Pearman, Peter B.; Psomas, Achilleas; Treier, Urs A.; Zimmermann, Niklaus E.; Svenning, Jens-Christian

2013-01-01

Warming-induced expansion of trees and shrubs into tundra vegetation will strongly impact Arctic ecosystems. Today, a small subset of the boreal woody flora found during certain Plio-Pleistocene warm periods inhabits Greenland. Whether the twenty-first century warming will induce a re-colonization of a rich woody flora depends on the roles of climate and migration limitations in shaping species ranges. Using potential treeline and climatic niche modelling, we project shifts in areas climatically suitable for tree growth and 56 Greenlandic, North American and European tree and shrub species from the Last Glacial Maximum through the present and into the future. In combination with observed tree plantings, our modelling highlights that a majority of the non-native species find climatically suitable conditions in certain parts of Greenland today, even in areas harbouring no native trees. Analyses of analogous climates indicate that these conditions are widespread outside Greenland, thus increasing the likelihood of woody invasions. Nonetheless, we find a substantial migration lag for Greenland's current and future woody flora. In conclusion, the projected climatic scope for future expansions is strongly limited by dispersal, soil development and other disequilibrium dynamics, with plantings and unintentional seed dispersal by humans having potentially large impacts on spread rates. PMID:23836785

Tree mortality predicted from drought-induced vascular damage

USGS Publications Warehouse

Anderegg, William R.L.; Flint, Alan L.; Huang, Cho-ying; Flint, Lorraine E.; Berry, Joseph A.; Davis, Frank W.; Sperry, John S.; Field, Christopher B.

2015-01-01

The projected responses of forest ecosystems to warming and drying associated with twenty-first-century climate change vary widely from resiliency to widespread tree mortality1, 2, 3. Current vegetation models lack the ability to account for mortality of overstorey trees during extreme drought owing to uncertainties in mechanisms and thresholds causing mortality4, 5. Here we assess the causes of tree mortality, using field measurements of branch hydraulic conductivity during ongoing mortality in Populus tremuloides in the southwestern United States and a detailed plant hydraulics model. We identify a lethal plant water stress threshold that corresponds with a loss of vascular transport capacity from air entry into the xylem. We then use this hydraulic-based threshold to simulate forest dieback during historical drought, and compare predictions against three independent mortality data sets. The hydraulic threshold predicted with 75% accuracy regional patterns of tree mortality as found in field plots and mortality maps derived from Landsat imagery. In a high-emissions scenario, climate models project that drought stress will exceed the observed mortality threshold in the southwestern United States by the 2050s. Our approach provides a powerful and tractable way of incorporating tree mortality into vegetation models to resolve uncertainty over the fate of forest ecosystems in a changing climate.

A greener Greenland? Climatic potential and long-term constraints on future expansions of trees and shrubs.

PubMed

Normand, Signe; Randin, Christophe; Ohlemüller, Ralf; Bay, Christian; Høye, Toke T; Kjær, Erik D; Körner, Christian; Lischke, Heike; Maiorano, Luigi; Paulsen, Jens; Pearman, Peter B; Psomas, Achilleas; Treier, Urs A; Zimmermann, Niklaus E; Svenning, Jens-Christian

2013-08-19

Warming-induced expansion of trees and shrubs into tundra vegetation will strongly impact Arctic ecosystems. Today, a small subset of the boreal woody flora found during certain Plio-Pleistocene warm periods inhabits Greenland. Whether the twenty-first century warming will induce a re-colonization of a rich woody flora depends on the roles of climate and migration limitations in shaping species ranges. Using potential treeline and climatic niche modelling, we project shifts in areas climatically suitable for tree growth and 56 Greenlandic, North American and European tree and shrub species from the Last Glacial Maximum through the present and into the future. In combination with observed tree plantings, our modelling highlights that a majority of the non-native species find climatically suitable conditions in certain parts of Greenland today, even in areas harbouring no native trees. Analyses of analogous climates indicate that these conditions are widespread outside Greenland, thus increasing the likelihood of woody invasions. Nonetheless, we find a substantial migration lag for Greenland's current and future woody flora. In conclusion, the projected climatic scope for future expansions is strongly limited by dispersal, soil development and other disequilibrium dynamics, with plantings and unintentional seed dispersal by humans having potentially large impacts on spread rates.

Applying Machine Learning to Star Cluster Classification

NASA Astrophysics Data System (ADS)

Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

2016-01-01

Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

Sulfur in Cometary Dust

NASA Technical Reports Server (NTRS)

Fomenkova, M. N.

1997-01-01

The computer-intensive project consisted of the analysis and synthesis of existing data on composition of comet Halley dust particles. The main objective was to obtain a complete inventory of sulfur containing compounds in the comet Halley dust by building upon the existing classification of organic and inorganic compounds and applying a variety of statistical techniques for cluster and cross-correlational analyses. A student hired for this project wrote and tested the software to perform cluster analysis. The following tasks were carried out: (1) selecting the data from existing database for the proposed project; (2) finding access to a standard library of statistical routines for cluster analysis; (3) reformatting the data as necessary for input into the library routines; (4) performing cluster analysis and constructing hierarchical cluster trees using three methods to define the proximity of clusters; (5) presenting the output results in different formats to facilitate the interpretation of the obtained cluster trees; (6) selecting groups of data points common for all three trees as stable clusters. We have also considered the chemistry of sulfur in inorganic compounds.

APOLLO-SOYUZ TEST PROJECT (ASTP) - EQUIPMENT (SEEDS)

NASA Image and Video Library

1975-06-06

S75-27445 (6 June 1975) --- American ASTP crewmen Vance D. Brand (left), Thomas P. Stafford (second from left) and Donald K. Slayton (right) receive a special box of genetically superior white spruce seeds from Glenn A. Kovar (second from right), USDA Forest Service project coordinator. The seeds, enough to plant an acre of trees, will be presented to the Soviet ASTP crewmen during the U.S.-USSR Apollo-Soyuz Test Project docking-in-Earth-orbit mission in July 1975. The seeds will produce faster-growing trees of exceptional height and shape. The trees will thrive in Moscow-like climate, and were developed by Forest Service?s Institute of Forest Genetics in Rhinelander, Wisconsin. The seed container box was made from recycled fibers and stabilized walnut. These seeds are an outstanding example of the U.S. Forest Service research to help produce new improved forests for the world. The four men are standing in the Building 2 briefing room at NASA's Johnson Space Center.

Perry Pinyon Pines Protection Project

Treesearch

Daniel McCarthy

2012-01-01

Fuel reduction treatments around pinyon pine trees began as a simple project but ended in something more complex, enjoyable, and rewarding. The project eventually led to pinyon species (Pinus monophylla and P. quadrifolia) reforestation efforts, something that has been tried in the past with disappointing results. The Perry Pinyon Pines Protection Project and current...

Using convolutional neural networks to explore the microbiome.

PubMed

Reiman, Derek; Metwally, Ahmed; Yang Dai

2017-07-01

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

Using narrative-based design scaffolds within a mobile learning environment to support learning outdoors with young children

NASA Astrophysics Data System (ADS)

Seely, Brian J.

This study aims to advance learning outdoors with mobile devices. As part of the ongoing Tree Investigators design-based research study, this research investigated a mobile application to support observation, identification, and explanation of the tree life cycle within an authentic, outdoor setting. Recognizing the scientific and conceptual complexity of this topic for young children, the design incorporated technological and design scaffolds within a narrative-based learning environment. In an effort to support learning, 14 participants (aged 5-9) were guided through the mobile app on tree life cycles by a comic-strip pedagogical agent, "Nutty the Squirrel", as they looked to explore and understand through guided observational practices and artifact creation tasks. In comparison to previous iterations of this DBR study, the overall patterns of talk found in this study were similar, with perceptual and conceptual talk being the first and second most frequently coded categories, respectively. However, this study coded considerably more instances of affective talk. This finding of the higher frequency of affective talk could possibly be explained by the relatively younger age of this iteration's participants, in conjunction with the introduced pedagogical agent, who elicited playfulness and delight from the children. The results also indicated a significant improvement when comparing the pretest results (mean score of .86) with the posttest results (mean score of 4.07, out of 5). Learners were not only able to recall the phases of a tree life cycle, but list them in the correct order. The comparison reports a significant increase, showing evidence of increased knowledge and appropriation of scientific vocabulary. The finding suggests the narrative was effective in structuring the complex material into a story for sense making. Future research with narratives should consider a design to promote learner agency through more interactions with the pedagogical agent and a conditional branching scenario framework to further evoke interest and engagement.

Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

ERIC Educational Resources Information Center

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-01-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning,…

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

PubMed

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

2016-07-07

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

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

Language Learning.

ERIC Educational Resources Information Center

Karolides, Nicholas J., Ed.

1985-01-01

The articles in this journal issue explore classroom methods for enhancing language acquisition. The titles of the articles and their authors are as follows: (1) Forests and Trees: Conservation and Reforestation" (Joyce S. Steward); (2) "Using Literature to Teach Language" (Richard D. Cureton); (3) "Language Learning through…

Best practices in incident investigation in the chemical process industries with examples from the industry sector and specifically from Nova Chemicals.

PubMed

Morrison, Lisa M

2004-07-26

This paper will summarize best practices in incident investigation in the chemical process industries and will provide examples from both the industry sector and specifically from NOVA Chemicals. As a sponsor of the Center for Chemical Process Safety (CCPS), an industry technology alliance of the American Institute of Chemical Engineers, NOVA Chemicals participates in a number of working groups to help develop best practices and tools for the chemical process and associated industries in order to advance chemical process safety. A recent project was to develop an update on guidelines for investigating chemical process incidents. A successful incident investigation management system must ensure that all incidents and near misses are reported, that root causes are identified, that recommendations from incident investigations identify appropriate preventive measures, and that these recommendations are resolved in a timely manner. The key elements of an effective management system for incident investigation will be described. Accepted definitions of such terms as near miss, incident, and root cause will be reviewed. An explanation of the types of incident classification systems in use, along with expected levels of follow-up, will be provided. There are several incident investigation methodologies in use today by members of the CCPS; most of these methodologies incorporate the use of several tools. These tools include: timelines, sequence diagrams, causal factor identification, brainstorming, checklists, pre-defined trees, and team-defined logic trees. Developing appropriate recommendations and then ensuring their resolution is the key to prevention of similar events from recurring, along with the sharing of lessons learned from incidents. There are several sources of information on previous incidents and lessons learned available to companies. In addition, many companies in the chemical process industries use their own internal databases to track recommendations from incidents and to share learnings internally.

Application of the Classification Tree Model in Predicting Learner Dropout Behaviour in Open and Distance Learning

ERIC Educational Resources Information Center

Yasmin, Dr.

2013-01-01

This paper demonstrates the meaningful application of learning analytics for determining dropout predictors in the context of open and distance learning in a large developing country. The study was conducted at the Directorate of Distance Education at the University of North Bengal, West Bengal, India. This study employed a quantitative research…

Identification of tree-crop rootstocks with resistance to Armillaria root disease.

USDA-ARS?s Scientific Manuscript database

Armillaria root disease attacks a broad range of tree crops in California. Instead of re-tooling ineffective conventional controls, namely soil fumigation, we focused on identification of Armillaria-resistant Juglans rootstocks, as part of a collaborative project to identify rootstocks with resistan...

Mechanized Red Pine Tree Planting Operation -- A Time Study

Treesearch

Joseph B. Sturos; Edwin S. Miyata

1984-01-01

Projected softwood shortages and high costs of mechanized tree planting indicate that more efficient planting equipment and systems are needed. This paper presents cost and productivity data for mechanically planting red pine seelings on a site previously occupied by hardwoods in northern Wisconsin

20th century Betula pubescens subsp. czerepanovii tree- and forest lines in Norway.

PubMed

Bryn, Anders; Potthoff, Kerstin

2017-01-01

Georeferenced tree- and forest line data has a wide range of applications and are increasingly used for e.g. monitoring of climate change impacts and range shift modelling. As part of a research project, registrations of previously re-mapped tree- and forest lines have been georeferenced. The data described in this paper contains 100 re-mapped registrations of Betula pubescens subsp. czerepanovii throughout Norway. All of the re-mapped tree- and forest line localities are georeferenced, elevation and aspect are given, elevational and spatial uncertainty are provided, and the re-mapping methods are explained. The published data weremapped for the first time between 1819 and 1963. The same sites were re-mapped between 1928 and 1996, but have until now been missing spatial coordinates. The entries contain 40 x 2 tree lines and 60 x 2 forest lines, most likely presenting the regionally highest registered tree- and forest lines at the given time. The entire material is stored and available for download through the GBIF server. Previously, the entries have been published in journals or reports, partly in Norwegian or German only. Without the provision of the spatial coordinates, the specific locations have been unknown. The material is now available for modelling and monitoring of tree- and forest line range shifts: The recordings are useful for interpretation of climate change impacts on tree- and forest lines, and the locations of re-mapped tree- and forest lines can be implemented in future monitoring projects. Since the recordings most likely provide the highest registered Betula pubescens subsp. czerepanovii locations within their specific regions, they are probably representing the contemporary physiognomic range limits.

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

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

Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction.

PubMed

Kuhn, Stefan; Egert, Björn; Neumann, Steffen; Steinbeck, Christoph

2008-09-25

Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

A basic approach to fire injury of tree stems

Treesearch

R. E. Martin

1963-01-01

Fire has come to be widely used as a tool in wildland management, particularly in the South. Its usefulness in fire hazard reduction, removal of undesirable trees, and changing of cover types has been demonstrated. We are continually trying to improve fire use, however, by learning more of the specific effects of fire on different species of plants.

Early crop-tree release in even-aged stands of Appalachian hardwoods

Treesearch

George R., Jr. Trimble; George R. Trimble

1971-01-01

Now that even-aged silviculture is well established as a successful method of growing Appalachian hardwoods, a pressing need exists for guidelines for precommercial operations. We started research several years ago on the Fernow Experimental Forest near Parsons, West Virginia, to learn more about the cost and methodology of early crop-tree release in mountain hardwood...

The Forest, Not the Tree(s): The Plight of the Generalist

ERIC Educational Resources Information Center

Reinsmith, William A.

2006-01-01

The great naturalist Edward O. Wilson's (1998) recent plea for the "consilience" of knowledge should strike a chord in the heart of every generalist. Invoking the unfinished agenda of the Enlightenment, Wilson has called for a rapprochement among the several branches of learning so that they can be viewed as interrelated and constituting a whole.…

Lessons from the Tree that Owns Itself: Implications for Education

ERIC Educational Resources Information Center

Mueller, Michael P.; Pattillo, Kemily K.; Mitchell, Debra B.; Luther, Rachel A.

2011-01-01

After taking seriously the idea that nature should have human rights argued by Cormac Cullinan in Orion Magazine (January/February 2008), we examined the lessons that could be learned from the tree that owns itself in Athens, Georgia. The point is to engage others in environmental and science education in a critical conversation about how school…

Risk management of PPP project in the preparation stage based on Fault Tree Analysis

NASA Astrophysics Data System (ADS)

Xing, Yuanzhi; Guan, Qiuling

2017-03-01

The risk management of PPP(Public Private Partnership) project can improve the level of risk control between government departments and private investors, so as to make more beneficial decisions, reduce investment losses and achieve mutual benefit as well. Therefore, this paper takes the PPP project preparation stage venture as the research object to identify and confirm four types of risks. At the same time, fault tree analysis(FTA) is used to evaluate the risk factors that belong to different parts, and quantify the influencing degree of risk impact on the basis of risk identification. In addition, it determines the importance order of risk factors by calculating unit structure importance on PPP project preparation stage. The result shows that accuracy of government decision-making, rationality of private investors funds allocation and instability of market returns are the main factors to generate the shared risk on the project.

Seeing the Forest for the Trees: A Qualitative Synthesis Project.

ERIC Educational Resources Information Center

Beck, Cheryl Tatano

2003-01-01

Describes a project that required graduate nursing students to conduct qualitative meta-synthesis of ethnographic research, using Noblit and Hare's approach. Discusses the process of meta-synthesis, interpretation of outcomes, and students' reactions to the project. (Contains 16 references.) (SK)

Strategies for Reforestation under Uncertain Future Climates: Guidelines for Alberta, Canada

PubMed Central

Gray, Laura K.; Hamann, Andreas

2011-01-01

Background Commercial forestry programs normally use locally collected seed for reforestation under the assumption that tree populations are optimally adapted to local environments. However, in western Canada this assumption is no longer valid because of climate trends that have occurred over the last several decades. The objective of this study is to show how we can arrive at reforestation recommendations with alternative species and genotypes that are viable under a majority of climate change scenarios. Methodology/Principal Findings In a case study for commercially important tree species of Alberta, we use an ecosystem-based bioclimate envelope modeling approach for western North America to project habitat for locally adapted populations of tree species using multi-model climate projections for the 2020s, 2050s and 2080s. We find that genotypes of species that are adapted to drier climatic conditions will be the preferred planting stock over much of the boreal forest that is commercially managed. Interestingly, no alternative species that are currently not present in Alberta can be recommended with any confidence. Finally, we observe large uncertainties in projections of suitable habitat that make reforestation planning beyond the 2050s difficult for most species. Conclusion/Significance More than 50,000 hectares of forests are commercially planted every year in Alberta. Choosing alternative planting stock, suitable for expected future climates, could therefore offer an effective climate change adaptation strategy at little additional cost. Habitat projections for locally adapted tree populations under observed climate change conform well to projections for the 2020s, which suggests that it is a safe strategy to change current reforestation practices and adapt to new climatic realities through assisted migration prescriptions. PMID:21853061

Detailed Project Report and Environmental Assessment, Wilson Branch, Chesterfield County, South Carolina.

DTIC Science & Technology

1982-06-01

of the creek. Some native tree species inc lud ing sweet gum, yel low poplar , sugar berry , loblolly pine, and longleaf pine occur within the...vegetation. In addition to the previously mentioned tree species, overstory species in this area include red maple, water oak, willow oak, willows, hickories...residential lawns and associated deciduous and evergreen trees and shrubs; and small stands of mixed pine/hardwoods. Mote detailed species composition for

Modeling the temporal dynamics of nonstructural carbohydrate pools in forest trees

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richardson, Andrew D.

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 fieldmore » 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.« less

Creating a Project-Based Learning Environment to Improve Project Management Skills of Graduate Students

ERIC Educational Resources Information Center

Arantes do Amaral, Joao Alberto; Gonçalves, Paulo; Hess, Aurélio

2015-01-01

This article describes the project-based learning environment created to support project management graduate courses. The paper will focus on the learning context and procedures followed for 13 years, in 47 project-based learning MBA courses, involving approximately 1,400 students and 34 community partners.

Tools for enhancing motivation in teaching climate change and impacts for students in forest- and environmental engineering

NASA Astrophysics Data System (ADS)

Gálos, Borbála

2017-04-01

Climate change is observed to have severe impacts on forest ecosystems. Ongoing research projects are dealing with the complex analysis of the causes of the health status decline and mortality of the vulnerable tree species. In the Carpathian Basin, recurrent long lasting drought periods and heatwaves of the last decades initiated the sequence of abiotic and biotic impacts in the beech and oak forests. Threatening extreme events are very likely to occur more frequent under changing climate conditions until the end of the 21st century. Therefore adaptation strategies and renewed regulations of the tree species selection are necessary. Learning material of forest education needs to be continuously updated with the new aspects and results of recent research and forest management planning. Therefore ideas and tools have been developed for teaching climate change impacts for students in forest- and environmental engineering. Using examples from world sport championships (e.g. losers and winners of climate change) these tools are applied to communicate the basic research questions in an easily understandable way as well as to motivate students and raise their awareness for the complex processes of forest - climate interactions. By the application of the developed examples for motivation, the key competences and learning outcomes can be the following: • students get an insight into the observed and projected tendencies of climate extremes; • they get an impression on the complexity of the climate change related damage chains; • they will be able to identify the climatic drivers of forest decline and mortality; • with the skill of critical thinking they will be able to evaluate the ecological role of forests that are already affected and that could be affected by the consequences of changing climate conditions; • they recognize the importance and urgency of the appropriate decisions in forestry and nature conservation. Keywords: climate change impacts, forest education, teaching tools for motivation The research is supported by the ÚNKP-16-4-3 New National Excellence Program of the Ministry of Human Capacities.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

75 FR 32960 - Hazardous Fire Risk Reduction, East Bay Hills, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-10

... program. The Strawberry Canyon Vegetation Management Project involves the removal of eucalyptus and other... tree sprouts from the area. The Claremont Canyon Vegetation Management Project involves the removal of... the Strawberry Canyon Vegetation Management Project for public comment. The draft environmental...

"Trees Live on Soil and Sunshine!"--Coexistence of Scientific and Alternative Conception of Tree Assimilation.

PubMed

Thorn, Christine Johanna; Bissinger, Kerstin; Thorn, Simon; Bogner, Franz Xaver

2016-01-01

Successful learning is the integration of new knowledge into existing schemes, leading to an integrated and correct scientific conception. By contrast, the co-existence of scientific and alternative conceptions may indicate a fragmented knowledge profile. Every learner is unique and thus carries an individual set of preconceptions before classroom engagement due to prior experiences. Hence, instructors and teachers have to consider the heterogeneous knowledge profiles of their class when teaching. However, determinants of fragmented knowledge profiles are not well understood yet, which may hamper a development of adapted teaching schemes. We used a questionnaire-based approach to assess conceptual knowledge of tree assimilation and wood synthesis surveying 885 students of four educational levels: 6th graders, 10th graders, natural science freshmen and other academic studies freshmen. We analysed the influence of learner's characteristics such as educational level, age and sex on the coexistence of scientific and alternative conceptions. Within all subsamples well-known alternative conceptions regarding tree assimilation and wood synthesis coexisted with correct scientific ones. For example, students describe trees to be living on "soil and sunshine", representing scientific knowledge of photosynthesis mingled with an alternative conception of trees eating like animals. Fragmented knowledge profiles occurred in all subsamples, but our models showed that improved education and age foster knowledge integration. Sex had almost no influence on the existing scientific conceptions and evolution of knowledge integration. Consequently, complex biological issues such as tree assimilation and wood synthesis need specific support e.g. through repeated learning units in class- and seminar-rooms in order to help especially young students to handle and overcome common alternative conceptions and appropriately integrate scientific conceptions into their knowledge profile.

Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

PubMed Central

Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

2014-01-01

Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

“Trees Live on Soil and Sunshine!”- Coexistence of Scientific and Alternative Conception of Tree Assimilation

PubMed Central

Thorn, Simon; Bogner, Franz Xaver

2016-01-01

Successful learning is the integration of new knowledge into existing schemes, leading to an integrated and correct scientific conception. By contrast, the co-existence of scientific and alternative conceptions may indicate a fragmented knowledge profile. Every learner is unique and thus carries an individual set of preconceptions before classroom engagement due to prior experiences. Hence, instructors and teachers have to consider the heterogeneous knowledge profiles of their class when teaching. However, determinants of fragmented knowledge profiles are not well understood yet, which may hamper a development of adapted teaching schemes. We used a questionnaire-based approach to assess conceptual knowledge of tree assimilation and wood synthesis surveying 885 students of four educational levels: 6th graders, 10th graders, natural science freshmen and other academic studies freshmen. We analysed the influence of learner’s characteristics such as educational level, age and sex on the coexistence of scientific and alternative conceptions. Within all subsamples well-known alternative conceptions regarding tree assimilation and wood synthesis coexisted with correct scientific ones. For example, students describe trees to be living on “soil and sunshine”, representing scientific knowledge of photosynthesis mingled with an alternative conception of trees eating like animals. Fragmented knowledge profiles occurred in all subsamples, but our models showed that improved education and age foster knowledge integration. Sex had almost no influence on the existing scientific conceptions and evolution of knowledge integration. Consequently, complex biological issues such as tree assimilation and wood synthesis need specific support e.g. through repeated learning units in class- and seminar-rooms in order to help especially young students to handle and overcome common alternative conceptions and appropriately integrate scientific conceptions into their knowledge profile. PMID:26807974

Mobile Learning Projects--A Critical Analysis of the State of the Art

ERIC Educational Resources Information Center

Frohberg, D.; Goth, C.; Schwabe, G.

2009-01-01

This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control,…

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

NASA Astrophysics Data System (ADS)

Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

2016-03-01

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

Mastering the game of Go without human knowledge.

PubMed

Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Hui, Fan; Sifre, Laurent; van den Driessche, George; Graepel, Thore; Hassabis, Demis

2017-10-18

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.

Mastering the game of Go without human knowledge

NASA Astrophysics Data System (ADS)

Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Hui, Fan; Sifre, Laurent; van den Driessche, George; Graepel, Thore; Hassabis, Demis

2017-10-01

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.

Mental Maps: A new instrument for teaching-learning-evaluation of engineering students

NASA Astrophysics Data System (ADS)

Oleschko, K.

2009-04-01

The use of interactive mind maps for teaching-learning-evaluation of postgraduate students is still not very common in Geosciences. Notwithstanding, these maps allow students to organize the huge volumes of information and data they are faced with (www.spinscape.com) for efficient research project elaboration and for understanding of basic anzatz and conjectures (Singer, 2009). The elaboration of mind maps is introduced as a principle teaching-learning-evaluation instrument (Cruza and Fierros, 2006) in my Research Methodology Seminar. Each student should to construct three types of multiscale mind maps before to write the formal proposal (Curiel and Radvansky, 2004; Zimmer, 2004). The main goal is to show how useful is to manage the physical, mathematical and linguistic information on the same structured way (Montibeller and Belton, 2009; Chu et al., 2009). The mental representation of the spatially and time organized physical world (physical map) is combined with the design of hierarchical tree of mathematical models used to describe it in mathematical terms (the map composed only by mathematical symbols), visualizing this tree branches by corresponding images inside the third map consisting on images. This three-faced representation of each research project helps the participant to perceive the complex nature of studied systems and visualize their features of universality and scale invariance. The maṕs elaboration is considered to be finished when any student of other specialties become able to present it in acceptable scientific way. Some examples of recent mental maps elaborated by the master degree students of Queretaro University, Mexico will be presented and discussed. Based on my experience I recommend this education technique in order to pass from sustainable engineer teaching to educate the engineers of Sustainability. References 1. Chu, H.-Ch., Chen, M.-Y., Chen, Y.-M., 2009. A semantic-based approach to content abstraction and annotation for content management. Expert Systems and Applications, 36: 2360-2376. 2. Cruza, N.S. and Fierros, L.E., 2006. Utility of conceptual schemes and mental maps on the teaching-learning process of residents in pediatrics. Gac. Med. Mex., 146 (6):457-465. 3. Curiel, J.M. and Radvansky, G.A., 2004. The accuracy of spatial information from temporally and spatially organized mental maps. Psychon. Bull. Rev., 11 (2):314-319. 4. Montibeller, G. and Belton, V. , 2009. Qualitative operators for reasoning maps: Evaluating multi-criteria options with networks of reasons. European J. of Operational Res., 195: 829-840. 5. Singer, F.M., 2009. The dynamic infrastructure of mind - A hypothesis and some of its applications. New ideas in Psychology, 27: 48-74. 6. http://www.spinscape.com 7. Zimmer, H.D. The construction of mental maps based on a fragmented view of physical maps. J. of Educational Psychology, 96 (3): 603-610.

Projected vegetation changes for the American Southwest: combined dynamic modeling and bioclimatic-envelope approach.

PubMed

Notaro, Michael; Mauss, Adrien; Williams, John W

2012-06-01

This study focuses on potential impacts of 21st century climate change on vegetation in the Southwest United States, based on debiased and interpolated climate projections from 17 global climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Among these models a warming trend is universal, but projected changes in precipitation vary in sign and magnitude. Two independent methods are applied: a dynamic global vegetation model to assess changes in plant functional types and bioclimatic envelope modeling to assess changes in individual tree and shrub species and biodiversity. The former approach investigates broad responses of plant functional types to climate change, while considering competition, disturbances, and carbon fertilization, while the latter approach focuses on the response of individual plant species, and net biodiversity, to climate change. The dynamic model simulates a region-wide reduction in vegetation cover during the 21st century, with a partial replacement of evergreen trees with grasses in the mountains of Colorado and Utah, except at the highest elevations, where tree cover increases. Across southern Arizona, central New Mexico, and eastern Colorado, grass cover declines, in some cases abruptly. Due to the prevalent warming trend among all 17 climate models, vegetation cover declines in the 21st century, with the greatest vegetation losses associated with models that project a drying trend. The inclusion of the carbon fertilization effect largely ameliorates the projected vegetation loss. Based on bioclimatic envelope modeling for the 21st century, the number of tree and shrub species that are expected to experience robust declines in range likely outweighs the number of species that are expected to expand in range. Dramatic shifts in plant species richness are projected, with declines in the high-elevation evergreen forests, increases in the eastern New Mexico prairies, and a northward shift of the Sonoran Desert biodiversity maximum.

An Economic Approach to Planting Trees for Carbon Storage

Treesearch

Peter J. Parks; David O. Hall; Bengt Kristrom; Omar R. Masera; Robert J. Multon; Andrew J. Plantinga; Joel N. Swisher; Jack K. Winjum

1997-01-01

Abstract: Methods are described for evaluating economic and carbon storage aspects of tree planting projects (e.g., plantations for restoration, roundwood, bioenergy, and nonwood products). Total carbon (C) stock is dynamic and comprises C in vegetation, decomposing matter, soil, products, and fuel substituted. An alternative (reference) case is...

Estimating Value Contribution of Tree and Stand Condition

Treesearch

R. Joss Hanna; Richard P. Thompson; Douglas D. Piirto; Jay E. Noel

1997-01-01

Key issues in encouraging forest management at the interface level in the oak woodlands are fire abatement, stand improvement, infection reduction, and hazard tree removal. The development of effective management prescriptions for stand improvement and economic returns provide guidance for homeowners, appraisers, and realtors. The purpose of this research project was...

2. Photocopy of Photograph, c. 1980. VIEW LOOKING WEST ALONG ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

2. Photocopy of Photograph, c. 1980. VIEW LOOKING WEST ALONG THE SAN FRANCISCO CANAL. NOTE THE COTTONWOOD TREE IN THE DISTANCE. THESE TREES ORIGINALLY LINED THE CANAL TO PREVENT EVAPORATION. WHEN IT WAS DISCOVERED THAT THE TREES' ROOTS SOAKED UP MORE WATER THAN THEIR SHADE PROTECTED, THEY WERE CUT DOWN. Photographer: Mark Durben, June 1986 Source: Salt River Project Archives - San Francisco Canal, Between Fortieth & Weir & Thirty-sixth Street & Roeser Road, Phoenix, Maricopa County, AZ

Development of a Prognostic Marker for Lung Cancer Using Analysis of Tumor Evolution

DTIC Science & Technology

2017-08-01

SUPPLEMENTARY NOTES 14. ABSTRACT The goal of this project is to sequence the exomes of single tumor cells from tumors in order to construct evolutionary trees...dissociation, tumor cell isolation, whole genome amplification, and exome sequencing. We have begun to sequence the exomes of single cells and to...of populations, the evolution of tumor cells within a tumor can be diagrammed on a phylogenetic tree. The more diverse a tumor’s phylogenetic tree

Whose Knowledge, Whose Development? Use and Role of Local and External Knowledge in Agroforestry Projects in Bolivia

NASA Astrophysics Data System (ADS)

Jacobi, Johanna; Mathez-Stiefel, Sarah-Lan; Gambon, Helen; Rist, Stephan; Altieri, Miguel

2017-03-01

Agroforestry often relies on local knowledge, which is gaining recognition in development projects. However, how local knowledge can articulate with external and scientific knowledge is little known. Our study explored the use and integration of local and external knowledge in agroforestry projects in Bolivia. In 42 field visits and 62 interviews with agroforestry farmers, civil society representatives, and policymakers, we found a diverse knowledge base. We examined how local and external knowledge contribute to livelihood assets and tree and crop diversity. Projects based predominantly on external knowledge tended to promote a single combination of tree and crop species and targeted mainly financial capital, whereas projects with a local or mixed knowledge base tended to focus on food security and increased natural capital (e.g., soil restoration) and used a higher diversity of trees and crops than those with an external knowledge base. The integration of different forms of knowledge can enable farmers to better cope with new challenges emerging as a result of climate change, fluctuating market prices for cash crops, and surrounding destructive land use strategies such as uncontrolled fires and aerial fumigation with herbicides. However, many projects still tended to prioritize external knowledge and undervalue local knowledge—a tendency that has long been institutionalized in the formal educational system and in extension services. More dialogue is needed between different forms of knowledge, which can be promoted by strengthening local organizations and their networks, reforming agricultural educational institutions, and working in close interaction with policymakers.

Whose Knowledge, Whose Development? Use and Role of Local and External Knowledge in Agroforestry Projects in Bolivia.

PubMed

Jacobi, Johanna; Mathez-Stiefel, Sarah-Lan; Gambon, Helen; Rist, Stephan; Altieri, Miguel

2017-03-01

Agroforestry often relies on local knowledge, which is gaining recognition in development projects. However, how local knowledge can articulate with external and scientific knowledge is little known. Our study explored the use and integration of local and external knowledge in agroforestry projects in Bolivia. In 42 field visits and 62 interviews with agroforestry farmers, civil society representatives, and policymakers, we found a diverse knowledge base. We examined how local and external knowledge contribute to livelihood assets and tree and crop diversity. Projects based predominantly on external knowledge tended to promote a single combination of tree and crop species and targeted mainly financial capital, whereas projects with a local or mixed knowledge base tended to focus on food security and increased natural capital (e.g., soil restoration) and used a higher diversity of trees and crops than those with an external knowledge base. The integration of different forms of knowledge can enable farmers to better cope with new challenges emerging as a result of climate change, fluctuating market prices for cash crops, and surrounding destructive land use strategies such as uncontrolled fires and aerial fumigation with herbicides. However, many projects still tended to prioritize external knowledge and undervalue local knowledge-a tendency that has long been institutionalized in the formal educational system and in extension services. More dialogue is needed between different forms of knowledge, which can be promoted by strengthening local organizations and their networks, reforming agricultural educational institutions, and working in close interaction with policymakers.

Spatial heterogeneity of radiocesium in the soil of a broadleaved deciduous forest: the marked role of stemflow

NASA Astrophysics Data System (ADS)

Levia, Delphis; Imamura, Naohiro; Toriyama, Jumpei; Kobayashi, Masahiro; Nanko, Kazuki

2017-04-01

This project amplifies our understanding of the transport of Cs-137 via stemflow in a konara oak forest by examining the spatial distribution of Cs-137 in the soil in both proximal (near-trunk) and distal ( > 1 m form tree trunk) stem areas. We report the Cs-137 concentrations and stocks for twenty-four soil samples harvested from the proximal and distal stem areas around individual trees in a radioactively contaminated konara oak forest in east-central Honshu, Japan. Preferential flowpaths of stemflow on the tree trunk and its point of infiltration into the forest floor was observed by conducting a dye tracer experiment. Experimental results showed that Cs-137 concentrations and stocks were higher in the soils of the proximal stem area as compared to the distal stem area when they corresponded with the preferential flowpaths of stemflow. Moreover, there was a significant relationship between the canopy projection area of individual trees and average soil Cs-137 concentrations and stocks, despite some canopy overlap among even trees. Our findings demonstrate that the spatial patterning of Cs-137 concentrations and stocks in the soil of the proximal stem area are governed (at least partially) by the preferential flowpaths of stemflow along the tree trunk. [Note: This presentation is currently under peer-review for journal publication.

The Interactions among Information Technology Organizational Learning, Project Learning, and Project Success

ERIC Educational Resources Information Center

McKay, Donald S., II

2012-01-01

Knowledge gained from completed information technology (IT) projects was not often shared with emerging project teams. Learning lessons from other project teams was not pursued because people lack time, do not see value in learning, fear a potentially painful process, and had concerns that sharing knowledge will hurt their career. Leaders could…

Psychological characteristics of eating disorders as evidenced by the combined administration of questionnaires and two projective methods: the Tree Drawing Test (Baum Test) and the Sentence Completion Test.

PubMed

Mizuta, Ichiro; Inoue, Yoichi; Fukunaga, Tomoko; Ishi, Ryohei; Ogawa, Asao; Takeda, Masatoshi

2002-02-01

The objective of this study is to examine psychological/psychopathological characteristics of eating disorders and their subtypes through a combined administration of questionnaires and projective tests. Three questionnaires (Eating Disorder Inventory - 2, Social Adaptation Scale, Southern California University Eating Disorder Inventory - Revised) and two projective tests (the Tree Drawing Test [TDT, Baum Test], and the Sentence Completion Test [SCT]) were administered to 126 female patients between the ages of 15 and 30 years, with eating disorders according to DSM-IV criteria at our outpatient clinic, and to 54 sex- and age-matched control subjects. The purging subtypes of eating disorders (anorexia nervosa - binge-eating/purging type [ANBP] and bulimia nervosa - purging type [BNP]) were clearly differentiated from the controls, both by the questionnaires and the projective tests. Compared with the controls, ANBP/BNP showed more problematic profiles across the three questionnaires, drew smaller and poorer trees in TDT to a more left location on the drawing paper, and gave fewer positive, and more negative responses in SCT. In contrast, few significant differences were found between anorexia nervosa- restricting type (ANR) and the controls, and between ANBP and BNP. As a trend, however, ANR was consistently located between the controls and ANBP/BNP across the whole questionnaires and projective tests.

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

USDA-ARS?s Scientific Manuscript database

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

A Lesson on Climate Change.

ERIC Educational Resources Information Center

Lewis, Jim

This cooperative learning activity, for grades 7-12, promotes critical thinking skills within the context of learning about the causes and effects of climate change. Objectives include: (1) understanding factors that reduce greenhouse gases; (2) understanding the role of trees in reducing greenhouse gases; (3) identifying foods that produce…

Cispus: Experiences in Green.

ERIC Educational Resources Information Center

Association of Washington School Principals, Olympia.

This document describes the facilities, grounds, and resources of the Cispus Learning Center in Randle, Washington, and presents lessons plans for outdoor and environmental education. The Cispus Learning Center is located on a tree-clad 45-acre site bordering Gifford Pinchot National Forest in southeast Washington. Constructed in 1964-65, the…

Finding the Forest Amid the Trees: Tools for Evaluating Astronomy Education and Public Outreach Projects

ERIC Educational Resources Information Center

Bailey, Janelle M.; Slater, Timothy F.

2004-01-01

The effective evaluation of educational projects is becoming increasingly important to funding agencies and to the individuals and organizations involved in the projects. This brief "how-to" guide provides an introductory description of the purpose and basic ideas of project evaluation, and uses authentic examples from four different astronomy and…

Drag forces of natural trees of different size: experiments in a towing tank

NASA Astrophysics Data System (ADS)

Jalonen, Johanna; Järvelä, Juha

2013-04-01

Reliable estimation of hydraulic resistance is of great importance in practical applications such as river and wetland restoration as well as flood prediction and management. Parameters describing riparian vegetation need to be physically sound and readily measurable. For these purposes, several researchers have studied the hydraulic resistance in flumes with living and artificial plants both in arrays and with isolated plants. However, due to the restrictions of flume size the experiments are often conducted with parts of trees, twigs or branches. Consequently, it is not clear how the size (parts of trees or small trees vs. full scale trees) affects the hydraulic resistance. We conducted direct drag force measurements for 23 tree individuals of different heights (0.9 m - 3.5 m) in a towing tank. The investigated species were Common Alder (Alnus glutinosa), Goat Willow (Salix caprea), Silver Birch (Betula pendula) and White Birch (Betula pubescens). The forces were measured at velocity ranges of 0.1-2.5 m/s and 0.1-2.0 m/s both in leafy and leafless conditions, respectively. The measurement system consisted of three load cells measuring the main flow direction. Two different load cell setups were used depending on the size of the specimen to allow for accurate force measurement. For the smaller trees the load cells were replaced with more sensitive sensors, and the resulting ranges of the load cells were from 1 to 1000 N and from 0.1 to 100 N. Frontal and side projected areas and bending of the specimens were recorded during the measurements using submerged video cameras. For all specimens, wet and dry biomass, projected area in still air, and one-sided leaf area were determined. In order to construct a 3D-model of the trees, the specimens were laser scanned from three directions with a terrestrial laser scanner (TLS). The resulting point cloud had a millimeter resolution, and provided detailed information about the plant characteristics, such as leaf area, projected area, and stem volume with the corresponding vertical distributions. The experiments provided information for improving understanding about the impact of tree size on drag (different plant properties such as flexibility and deformation), contribution of foliage to drag, and characterization of vegetation (laser scanning vs. biomass and photographs). The results showed that the contribution of leaves to the total drag decreased from 80% at the lowest velocity (0.1 m/s) to around 40% for velocities above 0.5 m/s. For the smaller trees, height 90-150 cm, the contribution of leaves to the total drag was 50% at the velocity of 0.5 m/s and higher. These differences may be attributed to the different tree morphology of the smaller trees compared to the taller trees. The differences in the flexibility and plant characteristics will be elaborated in the further analyses of the data.

The algorithm for duration acceleration of repetitive projects considering the learning effect

NASA Astrophysics Data System (ADS)

Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan

2018-03-01

Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.

Accounting Early for Life Long Learning: The AcE Project.

ERIC Educational Resources Information Center

University Coll. Worcester (England). Centre for Research in Early Childhood Education.

Building upon the work of the Effective Early Learning (EEL) Project in raising the quality of early learning for young children in the United Kingdom, the 3-year Accounting Early for Life Long Learning Project (AcE Project) focuses on enhancing in 3- to 6-year-olds those attitudes and dispositions that are important to life-long learning. This…

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzales, Antonio; Blazier, Nicholas Paul

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses onmore » a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.« less

Project-Based Learning in Programmable Logic Controller

NASA Astrophysics Data System (ADS)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

A Sweet Springtime Adventure.

ERIC Educational Resources Information Center

Carlone, Edward J.

1989-01-01

Describes how the tapping of maple trees can be used to teach lessons in science on boiling points, density, solubility, and other sugaring projects in the curricula. Outlines activities and precautions to follow when doing this project. (RT)

Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

PubMed

Deshwar, Amit G; Vembu, Shankar; Morris, Quaid

2015-01-01

Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…

The need for an assembly pilot project

USDA-ARS?s Scientific Manuscript database

Progress has been rapid since the June 2008 start of the cacao genome sequencing project with the completion of the physical map and the accumulation of approximately 10x coverage of the genome with Titanium 454 sequence data of Matina1-6, the highly homozygous Amelonado tree chosen for the project....

Growing Power?: Social Benefits From Urban Greening Projects

Treesearch

Lynne Westphal

1999-01-01

In this study I investigated practitioners claims for social benefits of urban greening projects (e.g., tree planting, community gardens). Practitioners' claims of increased neighborliness, reduced drug dealing and other social benefits have led to interest in using greening projects specifically to achieve these ends.Four sites that...

Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

NASA Astrophysics Data System (ADS)

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-06-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning, spiral learning and peer assessment. Namely, the course is articulated during a semester through the structured (progressive and incremental) development of a sequence of four projects, whose duration, scope and difficulty of management increase as the student gains theoretical and instrumental knowledge related to planning, monitoring and controlling projects. Moreover, the proposal is complemented using peer assessment. The proposal has already been implemented and validated for the last 3 years in two different universities. In the first year, project-based learning and spiral learning methods were combined. Such a combination was also employed in the other 2 years; but additionally, students had the opportunity to assess projects developed by university partners and by students of the other university. A total of 154 students have participated in the study. We obtain a gain in the quality of the subsequently projects derived from the spiral project-based learning. Moreover, this gain is significantly bigger when peer assessment is introduced. In addition, high-performance students take advantage of peer assessment from the first moment, whereas the improvement in poor-performance students is delayed.

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

ERIC Educational Resources Information Center

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

77 FR 55230 - Japan Lessons-Learned Project Directorate Interim Staff Guidance JLD-ISG-2012-01; Compliance With...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-07

... NUCLEAR REGULATORY COMMISSION [NRC-2012-0068] Japan Lessons-Learned Project Directorate Interim... Commission (NRC). ACTION: Japan Lessons-Learned Project Directorate interim staff guidance; issuance. SUMMARY...-Learned Project Directorate Interim Staff Guidance (JLD-ISG), JLD-ISG-2012-01, ``Compliance with Order EA...

77 FR 55232 - Japan Lessons-Learned Project Directorate Interim Staff Guidance JLD-ISG-2012-03; Compliance With...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-07

... NUCLEAR REGULATORY COMMISSION [NRC-2012-0067] Japan Lessons-Learned Project Directorate Interim...-Learned Project Directorate Interim Staff Guidance; issuance. SUMMARY: The U.S. Nuclear Regulatory Commission (NRC or the Commission) is issuing the Final Japan Lessons-Learned Project Directorate (JLD...

77 FR 55231 - Japan Lessons-Learned Project Directorate Interim Staff Guidance JLD-ISG-2012-02; Compliance With...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-07

... NUCLEAR REGULATORY COMMISSION [NRC-2012-0069] Japan Lessons-Learned Project Directorate Interim...-Learned Project Directorate interim staff guidance; issuance. SUMMARY: The U.S. Nuclear Regulatory Commission (NRC or the Commission) is issuing the Final Japan Lessons-Learned Project Directorate Interim...

Tree-space statistics and approximations for large-scale analysis of anatomical trees.

PubMed

Feragen, Aasa; Owen, Megan; Petersen, Jens; Wille, Mathilde M W; Thomsen, Laura H; Dirksen, Asger; de Bruijne, Marleen

2013-01-01

Statistical analysis of anatomical trees is hard to perform due to differences in the topological structure of the trees. In this paper we define statistical properties of leaf-labeled anatomical trees with geometric edge attributes by considering the anatomical trees as points in the geometric space of leaf-labeled trees. This tree-space is a geodesic metric space where any two trees are connected by a unique shortest path, which corresponds to a tree deformation. However, tree-space is not a manifold, and the usual strategy of performing statistical analysis in a tangent space and projecting onto tree-space is not available. Using tree-space and its shortest paths, a variety of statistical properties, such as mean, principal component, hypothesis testing and linear discriminant analysis can be defined. For some of these properties it is still an open problem how to compute them; others (like the mean) can be computed, but efficient alternatives are helpful in speeding up algorithms that use means iteratively, like hypothesis testing. In this paper, we take advantage of a very large dataset (N = 8016) to obtain computable approximations, under the assumption that the data trees parametrize the relevant parts of tree-space well. Using the developed approximate statistics, we illustrate how the structure and geometry of airway trees vary across a population and show that airway trees with Chronic Obstructive Pulmonary Disease come from a different distribution in tree-space than healthy ones. Software is available from http://image.diku.dk/aasa/software.php.

Blister rust in North America: What we have not learned in the past 100 years

Treesearch

Eugene P. Van Arsdel; Brian W. Geils

2011-01-01

Introduction of Cronartium ribicola (white pine blister rust) greatly motivated development of tree disease control and research in America. Although foresters and pathologists have learned much in the past 100 years, more remains to learn. The most important lesson is that fear of blister rust has reduced pine regeneration more than the disease itself. Based on six...

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

Learning from Experience: A Collection of Service-Learning Projects Linking Academic Standards to Curriculum.

ERIC Educational Resources Information Center

Babcock, Barbara, Ed.

Service-learning projects combine community service with student learning in a practical way that enhances academic knowledge and improves community environments and fellowship. This compilation is designed to show the service-learning process in action. The collection presents outstanding examples of successful service-learning projects as…

Archaeological Investigation in the Perry Lake Project Area, Northeastern Kansas National Register Evaluation of 17 Sites

DTIC Science & Technology

1989-01-01

Muscotah and Arrington marshes reveal the presence of open vegetation, with some pine, spruce, and birch trees and local stands of alder and willow...1977). Zone 4 pollen frequency curves demonstrate the dynamic nature of the prairie-forest ecotone. In zone 4a, grasses and deciduous trees are both...ecotone. Trees disappeared from the uplands and low values of some types of arboreal pollen suggest that the Delaware River floodplain "dried out over

The LEONARDO-DA-VINCI pilot project "e-learning-assistant" - Situation-based learning in nursing education.

PubMed

Pfefferle, Petra Ina; Van den Stock, Etienne; Nauerth, Annette

2010-07-01

E-learning will play an important role in the training portfolio of students in higher and vocational education. Within the LEONARDO-DA-VINCI action programme transnational pilot projects were funded by the European Union, which aimed to improve the usage and quality of e-learning tools in education and professional training. The overall aim of the LEONARDO-DA-VINCI pilot project "e-learning-assistant" was to create new didactical and technical e-learning tools for Europe-wide use in nursing education. Based on a new situation-oriented learning approach, nursing teachers enrolled in the project were instructed to adapt, develop and implement e- and blended learning units. According to the training contents nursing modules were developed by teachers from partner institutions, implemented in the project centers and evaluated by students. The user-package "e-learning-assistant" as a product of the project includes two teacher training units, the authoring tool "synapse" to create situation-based e-learning units, a student's learning platform containing blended learning modules in nursing and an open sourced web-based communication centre. Copyright 2009 Elsevier Ltd. All rights reserved.

How much does climate change threaten European forest tree species distributions?

PubMed

Dyderski, Marcin K; Paź, Sonia; Frelich, Lee E; Jagodziński, Andrzej M

2018-03-01

Although numerous species distribution models have been developed, most were based on insufficient distribution data or used older climate change scenarios. We aimed to quantify changes in projected ranges and threat level by the years 2061-2080, for 12 European forest tree species under three climate change scenarios. We combined tree distribution data from the Global Biodiversity Information Facility, EUFORGEN, and forest inventories, and we developed species distribution models using MaxEnt and 19 bioclimatic variables. Models were developed for three climate change scenarios-optimistic (RCP2.6), moderate (RCP4.5), and pessimistic (RPC8.5)-using three General Circulation Models, for the period 2061-2080. Our study revealed different responses of tree species to projected climate change. The species may be divided into three groups: "winners"-mostly late-successional species: Abies alba, Fagus sylvatica, Fraxinus excelsior, Quercus robur, and Quercus petraea; "losers"-mostly pioneer species: Betula pendula, Larix decidua, Picea abies, and Pinus sylvestris; and alien species-Pseudotsuga menziesii, Quercus rubra, and Robinia pseudoacacia, which may be also considered as "winners." Assuming limited migration, most of the species studied would face a significant decrease in suitable habitat area. The threat level was highest for species that currently have the northernmost distribution centers. Ecological consequences of the projected range contractions would be serious for both forest management and nature conservation. © 2017 John Wiley & Sons Ltd.

Cosmic string detection with tree-based machine learning

NASA Astrophysics Data System (ADS)

Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

2018-07-01

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9'-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

Cosmic String Detection with Tree-Based Machine Learning

NASA Astrophysics Data System (ADS)

Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

2018-05-01

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

Tree planting experiences in the eastern interior coal province

Treesearch

Charles Medvick

1980-01-01

Fruit trees were planted successfully in 1918 and organized afforestation began in 1928. Professional foresters had a hand in some of the very earliest planting projects. Formal reclamation research played an important role in applying science to early reclamation technology; however, considerable work has preceded the scientists. Some success has been experienced with...

Bacterial leaf scorch distribution and isothermal lines (PROJECT NC-EM-08-02)

Treesearch

Gerard C. Adams; Mursel Catall; James Walla; Ann B. Gould

2013-01-01

Bacterial leaf scorch (BLS) of shade trees is the common name for a disease caused by Xylella fastidiosa, a xylem-inhabiting bacterium that has fastidious nutritional requirements and is difficult to culture or verify by culturing. Forest trees including oak, sycamore, elm, planetree, sweetgum, mulberry and maple are species susceptible to ...

Simulation of Plant Physiological Process Using Fuzzy Variables

Treesearch

Daniel L. Schmoldt

1991-01-01

Qualitative modelling can help us understand and project effects of multiple stresses on trees. It is not practical to collect and correlate empirical data for all combinations of plant/environments and human/climate stresses, especially for mature trees in natural settings. Therefore, a mechanistic model was developed to describe ecophysiological processes. This model...

How effective are tree improvement programs in the 50 states?

Treesearch

Christopher D. Risbrudt; Stephen E. McDonald

1986-01-01

All 50 states were surveyed to determine the extent of their activities in producing genetically improved trees for timber production. Describes the funds expended, the species being improved, and the use of State and Private Forestry funds provided for genetic improvement. Projects future timber volumes attributable to genetic improvement, and estimates benefit cost...

FVS out of the box - assembly required

Treesearch

Don Vandendriesche

2010-01-01

The Forest Vegetation Simulator (FVS) is a prominent growth and yield model used for forecasting stand dynamics. However, users need to be aware of model behavior regarding stocking density, tree senescence, and understory recruitment; otherwise over long projections, FVS tends to concentrate substantial growth on few survivor trees. If the intent is to forecast...

A founder project: marketing the domestication baseline for forest trees

Treesearch

C. G. Williams; Floyd E. Bridgwater; C. Dana Nelson

2004-01-01

One of the most apparent benefits of forest genomics programmes is to provide genotypic information on the original selections of tree improvement programmes worldwide. In many breeding programmes, brances from these selections were grafted onto seedlings and the grafted seedlings composed the first seed orchards for planting programmes. with advanced generation...

Water economy of neotropical savanna trees: six paradigms revisited.

Treesearch

Guillermo Goldstein; Fredrick C. Meinzer; Sandra J. Bucci

2008-01-01

Biologists have long been puzzled by the striking morphological and anatomical characteristics of Neotropical savanna trees which have large scleromorphic leaves, allocate more than half of their total biomass to belowground structures and produce new leaves during the peak of the dry season. Based on results of ongoing interdisciplinary projects in the savannas of...

78 FR 21926 - Combined Notice of Filings #1

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-12

... Service Agreements with Expressway Solar A and B LLC to be effective 6/4/2013. Filed Date: 4/4/13...: ER13-1245-000. Applicants: Southern California Edison Company. Description: LGIA with Rising Tree Wind Farm LLC for Rising Tree Wind Farm Project to be effective 4/5/2013. Filed Date: 4/4/13. Accession...

YBYRÁ facilitates comparison of large phylogenetic trees.

PubMed

Machado, Denis Jacob

2015-07-01

The number and size of tree topologies that are being compared by phylogenetic systematists is increasing due to technological advancements in high-throughput DNA sequencing. However, we still lack tools to facilitate comparison among phylogenetic trees with a large number of terminals. The "YBYRÁ" project integrates software solutions for data analysis in phylogenetics. It comprises tools for (1) topological distance calculation based on the number of shared splits or clades, (2) sensitivity analysis and automatic generation of sensitivity plots and (3) clade diagnoses based on different categories of synapomorphies. YBYRÁ also provides (4) an original framework to facilitate the search for potential rogue taxa based on how much they affect average matching split distances (using MSdist). YBYRÁ facilitates comparison of large phylogenetic trees and outperforms competing software in terms of usability and time efficiency, specially for large data sets. The programs that comprises this toolkit are written in Python, hence they do not require installation and have minimum dependencies. The entire project is available under an open-source licence at http://www.ib.usp.br/grant/anfibios/researchSoftware.html .

''The control of lignin synthesis''

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlson, John E.

2005-04-07

In this project we tested the hypothesis that regulation of the synthesis of lignin in secondary xylem cells in conifer trees involves the transport of glucosylated lignin monomers to the wall of xylem cells, followed by de-glucosylation in the cell wall by monolignol-specific glucosidase enzymes, which activates the monomers for lignin polymerization. The information we gathered is relevant to the fundamental understanding of how trees make wood, and to the applied goal of more environmentally friendly pulp and paper production. We characterized the complete genomic structure of the Coniferin-specific Beta-glucosidase (CBG) gene family in the conifers loblolly pine (Pinus taeda)more » and lodgepole pine (Pinus contorta), and partial genomic sequences were obtained in several other tree species. Both pine species contain multiple CBG genes which raises the possibility of differential regulation, perhaps related to the multiple roles of lignin in development and defense. Subsequent projects will need to include detailed gene expression studies of each gene family member during tree growth and development, and testing the role of each monolignol-specific glucosidase gene in controlling lignin content.« less

"Theme" Bee

ERIC Educational Resources Information Center

Noel, Andrea M.; Cash, Julie Shornstein

2006-01-01

Thematic topics offer tremendous potential for science learning in the early grades and beyond. One second-grade class explored honeybees, a subject their teacher found both fascinating and easy to connect to a number of learning standards and science concepts. Her unit, "Honeybees and Apple Trees: A Close Look at Nature's Balancing Act," explored…

A Tree at Bedtime Investigation: Connecting Mathematics, Science, and Literature

ERIC Educational Resources Information Center

Kieff, Judith

2006-01-01

Activities that promote "active thinking" help children learn mathematics and science by allowing them to work at forming relationships, making connections, and integrating concepts and procedures. Dynamic and exciting children's books invite and motivate children to learn mathematics and science by responding to stories, characters, and their…

Place-Based Learning and Mobile Technology

ERIC Educational Resources Information Center

LaBelle, Chris

2011-01-01

When delivered on a mobile device, interpretive tours of a locale afford powerful learning experiences. As mobile devices become more powerful, content for these devices that is individualized and location-specific has become more common. In light of this trend, Oregon State University Extension developed a GPS-enabled iPhone tree tour…

Profiling Student Use of Calculators in the Learning of High School Mathematics

ERIC Educational Resources Information Center

Crowe, Cheryll E.; Ma, Xin

2010-01-01

Using data from the 2005 National Assessment of Educational Progress, students' use of calculators in the learning of high school mathematics was profiled based on their family background, curriculum background, and advanced mathematics coursework. A statistical method new to educational research--classification and regression trees--was applied…

Using Computer-Assisted Multiple Representations in Learning Geometry Proofs

ERIC Educational Resources Information Center

Wong, Wing-Kwong; Yin, Sheng-Kai; Yang, Hsi-Hsun; Cheng, Ying-Hao

2011-01-01

Geometry theorem proving involves skills that are difficult to learn. Instead of working with abstract and complicated representations, students might start with concrete, graphical representations. A proof tree is a graphical representation of a formal proof, with each node representing a proposition or given conditions. A computer-assisted…

Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa.

PubMed

Moncrieff, Glenn R; Scheiter, Simon; Bond, William J; Higgins, Steven I

2014-02-01

The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree- and grass-dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2 . Many sites where currently both tree-dominated and grass-dominated biomes are possible become deterministically tree-dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Improved FTA methodology and application to subsea pipeline reliability design.

PubMed

Lin, Jing; Yuan, Yongbo; Zhang, Mingyuan

2014-01-01

An innovative logic tree, Failure Expansion Tree (FET), is proposed in this paper, which improves on traditional Fault Tree Analysis (FTA). It describes a different thinking approach for risk factor identification and reliability risk assessment. By providing a more comprehensive and objective methodology, the rather subjective nature of FTA node discovery is significantly reduced and the resulting mathematical calculations for quantitative analysis are greatly simplified. Applied to the Useful Life phase of a subsea pipeline engineering project, the approach provides a more structured analysis by constructing a tree following the laws of physics and geometry. Resulting improvements are summarized in comparison table form.

Tree Death Study's Climate Change Connections

ScienceCinema

McDowell, Nate

2018-05-11

What are the exact physiological mechanisms that lead to tree death during prolonged drought and rising temperatures? These are the questions that scientists are trying to answer at a Los Alamos National Laboratory research project called SUMO. SUMO stands for SUrvival/MOrtality study; it's a plot of land on the Lab's southern border that features 18 climate controlled tree study chambers and a large drought structure that limits rain and snowfall. Scientists are taking a wide variety of measurements over a long period of time to determine what happens during drought and warming, and what the connections and feedback loops might be between tree death and climate change.

Improved FTA Methodology and Application to Subsea Pipeline Reliability Design

PubMed Central

Lin, Jing; Yuan, Yongbo; Zhang, Mingyuan

2014-01-01

An innovative logic tree, Failure Expansion Tree (FET), is proposed in this paper, which improves on traditional Fault Tree Analysis (FTA). It describes a different thinking approach for risk factor identification and reliability risk assessment. By providing a more comprehensive and objective methodology, the rather subjective nature of FTA node discovery is significantly reduced and the resulting mathematical calculations for quantitative analysis are greatly simplified. Applied to the Useful Life phase of a subsea pipeline engineering project, the approach provides a more structured analysis by constructing a tree following the laws of physics and geometry. Resulting improvements are summarized in comparison table form. PMID:24667681

Exploiting Non-sequence Data in Dynamic Model Learning

DTIC Science & Technology

2013-10-01

For our experiments here and in Section 3.5, we implement the proposed algorithms in MATLAB and use the maximum directed spanning tree solver...embarrassingly parallelizable, whereas PM’s maximum directed spanning tree procedure is harder to parallelize. In this experiment, our MATLAB ...some estimation problems, this approach is able to give unique and consistent estimates while the maximum- likelihood method gets entangled in

Second Cross-Sectional Study of Attainment of the Concepts "Equilateral Triangle,""Cutting Tool,""Noun," and "Tree" by Children Age 6 to 16 of City B. Technical Report No. 347.

ERIC Educational Resources Information Center

Klausmeier, Herbert J.; And Others

For this study, the second in the cross sectional series, based on the Conceptual Learning and Development (CLD) model, assessment batteries were developed to determine each child's level of attainment and related use of the concepts "equilateral triangle,""cutting tool,""noun," and "tree." Batteries were…

Diameter growth of individual hardwood trees

Treesearch

G.R., Jr. Trimble; G.R. Trimble

1969-01-01

Between 1959 and 1967 a study of d.b.h. growth rates was made on individual hardwood trees near Parsons, W. Va. From this study, we obtained information that will help foresters to predict growth. We learned that the correlation of the more easily used crown classification with d.b.h. growth is as good as or better than the correlation of vigor classes with d.b.h....

Modelling the effects of land cover and climate change on soil water partitioning in a boreal headwater catchment

NASA Astrophysics Data System (ADS)

Wang, Hailong; Tetzlaff, Doerthe; Soulsby, Chris

2018-03-01

Climate and land cover are two major factors affecting the water fluxes and balance across spatiotemporal scales. These two factors and their impacts on hydrology are often interlinked. The quantification and differentiation of such impacts is important for developing sustainable land and water management strategies. Here, we calibrated the well-known Hydrus-1D model in a data-rich boreal headwater catchment in Scotland to assess the role of two dominant vegetation types (shrubs vs. trees) in regulating the soil water partitioning and balance. We also applied previously established climate projections for the area and replaced shrubs with trees to imitate current land use change proposals in the region, so as to quantify the potential impacts of climate and land cover changes on soil hydrology. Under tree cover, evapotranspiration and deep percolation to recharge groundwater was about 44% and 57% of annual precipitation, whilst they were about 10% lower and 9% higher respectively under shrub cover in this humid, low energy environment. Meanwhile, tree canopies intercepted 39% of annual precipitation in comparison to 23% by shrubs. Soils with shrub cover stored more water than tree cover. Land cover change was shown to have stronger impacts than projected climate change. With a complete replacement of shrubs with trees under future climate projections at this site, evapotranspiration is expected to increase by ∼39% while percolation to decrease by 21% relative to the current level, more pronounced than the modest changes in the two components (<8%) with climate change only. The impacts would be particularly marked in warm seasons, which may result in water stress experienced by the vegetation. The findings provide an important evidence base for adaptive management strategies of future changes in low-energy humid environments, where vegetation growth is usually restricted by radiative energy and not water availability while few studies that quantify soil water partitioning exist.

Andragogical Modeling and the Success of the "EMPACTS" project-based learning model in the STEM disciplines: A decade of growth and learner success in the 2Y College Learning Environment.

NASA Astrophysics Data System (ADS)

Phillips, C. D.; Thomason, R.; Galloway, M.; Sorey, N.; Stidham, L.; Torgerson, M.

2014-12-01

EMPACTS (Educationally Managed Projects Advancing Curriculum, Technology/Teamwork and Service) is a project-based, adult learning modelthat is designed to enhance learning of course content through real-world application and problem solving self directed and collaborative learning use of technology service to the community EMPACTS students are self-directed in their learning, often working in teams to develop, implement, report and present final project results. EMPACTS faculty use community based projects to increase deeper learning of course content through "real-world" service experiences. Learners develop personal and interpersonal work and communication skills as they plan, execute and complete project goals together. Technology is used as a tool to solve problems and to publish the products of their learning experiences. Courses across a broad STEM curriculum integrate the EMPACTS project experience into the overall learning outcomes as part of the learning college mission of preparing 2Y graduates for future academic and/or workforce success. Since the program began in 2005, there have been over 200 completed projects/year. Student driven successes have led to the establishment of an EMPACTS Technology Corp, which is funded through scholarship and allows EMPACTS learners the opportunity to serve and learn from one another as "peer instructors." Engineering and 3D graphic design teams have written technology proposals and received funding for 3D printing replication projects, which have benefited the college as a whole through grant opportunities tied to these small scale successes. EMPACTS students engage in a variety of outreachprojects with area schools as they share the successes and joys of self directed, inquiry, project based learning. The EMPACTS Program has successfully trained faculty and students in the implementation of the model and conduct semester to semester and once a year workshops for college and K-12 faculty, who are interested in enhancing the learning experience and retention of course content through meaningful, engaging, character building projects. Learner Project successes are celebrated and archived within the framework of the EMPACTS Student Project website. http://faculty.nwacc.edu/EAST_original/Spring2014/Spring2014index.htm

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

Project CAPABLE: Model Unit.

ERIC Educational Resources Information Center

Madawaska School District, ME.

Project CAPABLE (Classroom Action Program: Aim: Basic Learning Effectiveness) is a classroom approach which integrates the basic learning skills with content. The goal of the project is to use basic learning skills to enhance the learning of content and at the same time use the content to teach basic learning skills. This manual illustrates how…

Challenges and approaches to projecting changes in forest distributions in complex mountain landscape

NASA Astrophysics Data System (ADS)

Kueppers, L. M.; Molotch, N. P.; Meromy, L.; Moyes, A. B.; Conlisk, E.; Castanha, C.

2015-12-01

The extent and density of forest trees in mountain landscapes is a first order control on watershed function, affecting patterns of snow accumulation, timing of snowmelt, and amount and quality of run-off, through alterations of surface energy and water fluxes and wind. Climate change is increasingly affecting the density and distribution of mature forests through changes to disturbance regimes, increases in physiological stress and increases in mortality due to warmer temperatures. In addition, climate change is likely altering patterns of regeneration and driving establishment of trees in high elevation meadows and alpine tundra. Though hard to detect in current forestry datasets, changes in tree establishment are critical to the future of forests. Experimental approaches, such as our climate warming experiment in the Colorado Front Range, can provide valuable data regarding seedling sensitivity to climate variability and change across important landscape positions. We've found that warming enhances negative effects of water stress across forest, treeline and alpine sites, reducing recruitment in the absence of additional summer moisture. At the lowest elevation, reductions with warming have reduced Engelmann spruce recruitment to zero. Species differ in their responses to warming in the alpine, but together confirm the importance of seed dispersal to upward forest shifts. The presence of trees or other vegetation can facilitate tree establishment by modifying microclimates, especially at and above treeline. Ultimately, these ecological and demographic processes govern the timescales of tree and forest responses to climate variability and change. For the long-lived species that dominate high elevation watersheds, these processes can take decades or centuries to play out, meaning many tree populations are and will continue to be out of equilibrium with a rapidly changing climate. Projecting changes in tree distributions and abundances across mountain landscapes requires integration of changes in hydroclimatic conditions across diverse topoclimatic settings; the sensitivity of recruitment, growth and mortality to climate; and feedbacks between trees and microclimate into modeling tools that represent time-explicit ecological and demographic processes.

Extreme Drought Event and Shrub Invasion Reduce Oak Trees Functioning and Resilience on Water-Limited Ecosystems

NASA Astrophysics Data System (ADS)

Caldeira, M. C.; Lobo-do-Vale, R.; Lecomte, X.; David, T. S.; Pinto, J. G.; Bugalho, M. N.; Werner, C.

2016-12-01

Extreme droughts and plant invasions are major drivers of global change that can critically affect ecosystem functioning. Shrub encroachment is increasing in many regions worldwide and extreme events are projected to increase in frequency and intensity, namely in the Mediterranean region. Nevertheless, little is known about how these drivers may interact and affect ecosystem functioning and resilience Using a manipulative shrub removal experiment and the co-occurrence of an extreme drought event in a Mediterranean oak woodland, we show that the combination of native shrub invasion and extreme drought reduced ecosystem transpiration and the resilience of the key-stone oak tree species. We established six 25 x 25 m paired plots in a shrub (Cistus ladanifer L.) encroached Mediterranean cork-oak (Quercus suber L.) woodland. We measured sapflow and pre-dawn leaf water potential of trees and shrubs and soil water content in all plots during four years. We determined the resilience of tree transpiration to evaluate to what extent trees recovered from the extreme drought event. From February to November 2011 we conducted baseline measurements for plot comparison. In November 2011 all the shrubs from one of all the paired plots were cut and removed. Ecosystem transpiration was dominated by the water use of the invasive shrub, which further increased after the extreme drought. Simultaneously, tree transpiration in invaded plots declined more sharply (67 ± 13 %) than in plots cleared from shrubs (31 ± 11%) relative to the pre-drought year (2011). Trees in invaded plots were not able to recover in the following wetter year showing lower resilience to the extreme drought event. Our results imply that in Mediterranean-type of climates invasion by water spending species coupled with the projected recurrent extreme droughts will cause critical drought tolerance thresholds of trees to be overcome, thus increasing the probability of tree mortality.

Predicting Tillage Patterns in the Tiffin River Watershed Using Remote Sensing Methods

NASA Astrophysics Data System (ADS)

Brooks, C.; McCarty, J. L.; Dean, D. B.; Mann, B. F.

2012-12-01

Previous research in tillage mapping has focused primarily on utilizing low to no-cost, moderate (30 m to 15 m) resolution satellite data. Successful data processing techniques published in the scientific literature have focused on extracting and/or classifying tillage patterns through manipulation of spectral bands. For instance, Daughtry et al. (2005) evaluated several spectral indices for crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. A weak to moderate relationship between Landsat Thematic Mapper (TM) indices and crop residue cover was found; similar results were reported in Minnesota. Building on the findings from the scientific literature and previous work done by MTRI in the heavily agricultural Tiffin watershed of northwest Ohio and southeast Michigan, a decision tree classifier approach (also referred to as a classification tree) was used, linking several satellite data to on-the-ground tillage information in order to boost classification results. This approach included five tillage indices and derived products. A decision tree methodology enabled the development of statistically optimized (i.e., minimizing misclassification rates) classification algorithms at various desired time steps: monthly, seasonally, and annual over the 2006-2010 time period. Due to their flexibility, processing speed, and availability within all major remote sensing and statistical software packages, decision trees can ingest several data inputs from multiple sensors and satellite products, selecting only the bands, band ratios, indices, and products that further reduce misclassification errors. The project team created crop-specific tillage pattern classification trees whereby a training data set (~ 50% of available ground data) was created for production of the actual decision tree and a validation data set was set aside (~ 50% of available ground data) in order to assess the accuracy of the classification. A seasonal time step was used, optimizing a decision tree based on seasonal ground data for tillage patterns and satellite data and products for years 2006 through 2010. Annual crop type maps derived by the project team and the USDA Cropland Data Layer project was used an input to understand locations of corn, soybeans, wheat, etc. on a yearly basis. As previously stated, the robustness of the decision tree approach is the ability to implement various satellite data and products across temporal, spectral, and spatial resolutions, thereby improving the resulting classification and providing a reliable method that is not sensor-dependent. Tillage pattern classification from satellite imagery is not a simple task and has proven a challenge to previous researchers investigating this remote sensing topic. The team's decision tree method produced a practical, usable output within a focused project time period. Daughtry, C.S.T., Hunt Jr., E.R., Doraiswamy, P.C., McMurtrey III, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97, 864-871.

Further Effects of Phylogenetic Tree Style on Student Comprehension in an Introductory Biology Course.

PubMed

Dees, Jonathan; Bussard, Caitlin; Momsen, Jennifer L

2018-06-01

Phylogenetic trees have become increasingly important across the life sciences, and as a result, learning to interpret and reason from these diagrams is now an essential component of biology education. Unfortunately, students often struggle to understand phylogenetic trees. Style (i.e., diagonal or bracket) is one factor that has been observed to impact how students interpret phylogenetic trees, and one goal of this research was to investigate these style effects across an introductory biology course. In addition, we investigated the impact of instruction that integrated diagonal and bracket phylogenetic trees equally. Before instruction, students were significantly more accurate with the bracket style for a variety of interpretation and construction tasks. After instruction, however, students were significantly more accurate only for construction tasks and interpretations involving taxa relatedness when using the bracket style. Thus, instruction that used both styles equally mitigated some, but not all, style effects. These results inform the development of research-based instruction that best supports student understanding of phylogenetic trees.

Uncertain decision tree inductive inference

NASA Astrophysics Data System (ADS)

Zarban, L.; Jafari, S.; Fakhrahmad, S. M.

2011-10-01

Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.

Collaborative project-based learning: an integrative science and technological education project

NASA Astrophysics Data System (ADS)

Baser, Derya; Ozden, M. Yasar; Karaarslan, Hasan

2017-04-01

Background: Blending collaborative learning and project-based learning (PBL) based on Wolff (2003) design categories, students interacted in a learning environment where they developed their technology integration practices as well as their technological and collaborative skills.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

PhyloTreePruner: A Phylogenetic Tree-Based Approach for Selection of Orthologous Sequences for Phylogenomics.

PubMed

Kocot, Kevin M; Citarella, Mathew R; Moroz, Leonid L; Halanych, Kenneth M

2013-01-01

Molecular phylogenetics relies on accurate identification of orthologous sequences among the taxa of interest. Most orthology inference programs available for use in phylogenomics rely on small sets of pre-defined orthologs from model organisms or phenetic approaches such as all-versus-all sequence comparisons followed by Markov graph-based clustering. Such approaches have high sensitivity but may erroneously include paralogous sequences. We developed PhyloTreePruner, a software utility that uses a phylogenetic approach to refine orthology inferences made using phenetic methods. PhyloTreePruner checks single-gene trees for evidence of paralogy and generates a new alignment for each group containing only sequences inferred to be orthologs. Importantly, PhyloTreePruner takes into account support values on the tree and avoids unnecessarily deleting sequences in cases where a weakly supported tree topology incorrectly indicates paralogy. A test of PhyloTreePruner on a dataset generated from 11 completely sequenced arthropod genomes identified 2,027 orthologous groups sampled for all taxa. Phylogenetic analysis of the concatenated supermatrix yielded a generally well-supported topology that was consistent with the current understanding of arthropod phylogeny. PhyloTreePruner is freely available from http://sourceforge.net/projects/phylotreepruner/.

A Multiscale Simulation Framework to Investigate Hydrobiogeochemical Processes in the Groundwater-Surface Water Interaction Zone

NASA Astrophysics Data System (ADS)

Scheibe, T. D.; Yang, X.; Song, X.; Chen, X.; Hammond, G. E.; Song, H. S.; Hou, Z.; Murray, C. J.; Tartakovsky, A. M.; Tartakovsky, G.; Yang, X.; Zachara, J. M.

2016-12-01

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.

Colloquium paper: how many tree species are there in the Amazon and how many of them will go extinct?

PubMed

Hubbell, Stephen P; He, Fangliang; Condit, Richard; Borda-de-Agua, Luís; Kellner, James; Ter Steege, Hans

2008-08-12

New roads, agricultural projects, logging, and mining are claiming an ever greater area of once-pristine Amazonian forest. The Millennium Ecosystems Assessment (MA) forecasts the extinction of a large fraction of Amazonian tree species based on projected loss of forest cover over the next several decades. How accurate are these estimates of extinction rates? We use neutral theory to estimate the number, relative abundance, and range size of tree species in the Amazon metacommunity and estimate likely tree-species extinctions under published optimistic and nonoptimistic Amazon scenarios. We estimate that the Brazilian portion of the Amazon Basin has (or had) 11,210 tree species that reach sizes >10 cm DBH (stem diameter at breast height). Of these, 3,248 species have population sizes >1 million individuals, and, ignoring possible climate-change effects, almost all of these common species persist under both optimistic and nonoptimistic scenarios. At the rare end of the abundance spectrum, however, neutral theory predicts the existence of approximately 5,308 species with <10,000 individuals each that are expected to suffer nearly a 50% extinction rate under the nonoptimistic deforestation scenario and an approximately 37% loss rate even under the optimistic scenario. Most of these species have small range sizes and are highly vulnerable to local habitat loss. In ensembles of 100 stochastic simulations, we found mean total extinction rates of 20% and 33% of tree species in the Brazilian Amazon under the optimistic and nonoptimistic scenarios, respectively.

Preparing Hispanic Students for the Real World: Benefits of Problem-Based Service Learning Projects

ERIC Educational Resources Information Center

West, Jean Jaymes; Simmons, Donna

2012-01-01

Student learning is enriched by problem-based service learning (PBSL) projects. For Hispanic students, the learning that takes place in PBSL projects may be even more significant, although the research published in academic journals about client-based projects for Hispanic students is limited. This article begins to advance an understanding of how…

Scheduling Projects with Multiskill Learning Effect

PubMed Central

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost. PMID:24683355

Scheduling projects with multiskill learning effect.

PubMed

Zha, Hong; Zhang, Lianying

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

PROJECT UPLIFT--REFLECTIONS ON A QUIET SUMMER.

ERIC Educational Resources Information Center

WOOCK, ROGER

PROJECT UPLIFT WAS ESTABLISHED IN THE SUMMER OF 1965 IN HARLEM IN PART TO AVOID POSSIBLE RIOTS. CONNECTED WITH HARYOU-ACT, AND FUNDED THROUGH THE OFFICE OF ECONOMIC OPPORTUNITY, THE PROJECT'S ACTIVITIES INCLUDED--CONSTRUCTION OF VEST POCKET PARKS, PLANTING OF TREES IN CENTRAL HARLEM, A REMEDIAL READING PROGRAM, BOOTHS TO GIVE INFORMATION ABOUT…

Comparing the performance of two CBIRS indexing schemes

NASA Astrophysics Data System (ADS)

Mueller, Wolfgang; Robbert, Guenter; Henrich, Andreas

2003-01-01

Content based image retrieval (CBIR) as it is known today has to deal with a number of challenges. Quickly summarized, the main challenges are firstly, to bridge the semantic gap between high-level concepts and low-level features using feedback, secondly to provide performance under adverse conditions. High-dimensional spaces, as well as a demanding machine learning task make the right way of indexing an important issue. When indexing multimedia data, most groups opt for extraction of high-dimensional feature vectors from the data, followed by dimensionality reduction like PCA (Principal Components Analysis) or LSI (Latent Semantic Indexing). The resulting vectors are indexed using spatial indexing structures such as kd-trees or R-trees, for example. Other projects, such as MARS and Viper propose the adaptation of text indexing techniques, notably the inverted file. Here, the Viper system is the most direct adaptation of text retrieval techniques to quantized vectors. However, while the Viper query engine provides decent performance together with impressive user-feedback behavior, as well as the possibility for easy integration of long-term learning algorithms, and support for potentially infinite feature vectors, there has been no comparison of vector-based methods and inverted-file-based methods under similar conditions. In this publication, we compare a CBIR query engine that uses inverted files (Bothrops, a rewrite of the Viper query engine based on a relational database), and a CBIR query engine based on LSD (Local Split Decision) trees for spatial indexing using the same feature sets. The Benchathlon initiative works on providing a set of images and ground truth for simulating image queries by example and corresponding user feedback. When performing the Benchathlon benchmark on a CBIR system (the System Under Test, SUT), a benchmarking harness connects over internet to the SUT, performing a number of queries using an agreed-upon protocol, the multimedia retrieval markup language (MRML). Using this benchmark one can measure the quality of retrieval, as well as the overall (speed) performance of the benchmarked system. Our Benchmarks will draw on the Benchathlon"s work for documenting the retrieval performance of both inverted file-based and LSD tree based techniques. However in addition to these results, we will present statistics, that can be obtained only inside the system under test. These statistics will include the number of complex mathematical operations, as well as the amount of data that has to be read from disk during operation of a query.

Flow around a Living Tree

NASA Astrophysics Data System (ADS)

Ishikawa, Hitoshi; Amano, Suguru; Yakushiji, Kenta

Flow around a living tree was investigated as basic research of a windbreak forest. A type of conifer, which is named “goldcrest, ” was used as the test piece in a wind tunnel experiment. The drag coefficient of the living tree was measured in the range of a mean flow velocity of 5˜15m/s. The drag coefficient of the living tree was less than that of a two-dimensional circular cylinder. Because flow passes through the tree’s crown which has the permeability of branches and leaves, the drag coefficient was decreased as the flow velocity was increased. Moreover, the flexibility is that the bole of a living tree also plays an important role in drag reduction, bending itself so as to decrease the projected area. In the wake behind the living tree, reverse flow was found at further downstream region than the case of a circular cylinder.

Working Notes of the 1990 Spring Symposium on Automated Abduction

DTIC Science & Technology

1990-09-27

possibilities for abstracting the leaf nodes in using apprenticeship learning techniques. In LTCAI.E the proof tree. Morgan Kaufmann, 1987. A detailed...ibm.com Abstract planation process and compute particular operational A major limitation of explanation-based learn - descriptions of the target...for the learning that would be difficult or impos- 3n educated, somewhat abstract guess at why the pro- sible using abduction. I position is likely to

7 CFR 3430.202 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Regulations of the Department of Agriculture (Continued) COOPERATIVE STATE RESEARCH, EDUCATION, AND EXTENSION... definitions applicable to the program under this subpart include: Integrated project means a project that... or activity. Specialty crop means fruits and vegetables, tree nuts, dried fruits, and horticulture...

Effects of Phylogenetic Tree Style on Student Comprehension

NASA Astrophysics Data System (ADS)

Dees, Jonathan Andrew

Phylogenetic trees are powerful tools of evolutionary biology that have become prominent across the life sciences. Consequently, learning to interpret and reason from phylogenetic trees is now an essential component of biology education. However, students often struggle to understand these diagrams, even after explicit instruction. One factor that has been observed to affect student understanding of phylogenetic trees is style (i.e., diagonal or bracket). The goal of this dissertation research was to systematically explore effects of style on student interpretations and construction of phylogenetic trees in the context of an introductory biology course. Before instruction, students were significantly more accurate with bracket phylogenetic trees for a variety of interpretation and construction tasks. Explicit instruction that balanced the use of diagonal and bracket phylogenetic trees mitigated some, but not all, style effects. After instruction, students were significantly more accurate for interpretation tasks involving taxa relatedness and construction exercises when using the bracket style. Based on this dissertation research and prior studies on style effects, I advocate for introductory biology instructors to use only the bracket style. Future research should examine causes of style effects and variables other than style to inform the development of research-based instruction that best supports student understanding of phylogenetic trees.

Project-Based Learning Involving Sensory Panelists Improves Student Learning Outcomes

ERIC Educational Resources Information Center

Lee, Yee Ming

2015-01-01

Project-based, collaborative learning is an effective teaching method when compared to traditional cognitive learning. The purpose of this study was to assess student learning after the completion of a final meal project that involved a group of sensory panelists. A paper survey was conducted among 73 senior nutrition and dietetics students…

Project-Based Learning around the World, Part 2

ERIC Educational Resources Information Center

Weatherby, Kristen

2007-01-01

In part 1 of this article, the author introduced Microsoft's worldwide K-12 education initiative, Partners in Learning, and discusses the partnership with ISTE in creating project-based learning curriculum as part of Partners in Learning. The project-based learning curriculum can be adapted for classrooms across the globe. This paper, the second…

Connecting Family Learning and Active Citizenship

ERIC Educational Resources Information Center

Flanagan, Mary

2009-01-01

In Ireland family learning and active citizenship has not been linked together until 2006. It was while the Clare Family Learning Project was involved in a family learning EU learning network project, that a suggestion to create a new partnership project linking both areas was made and FACE IT! was born (Families and Active Citizenship…

How Teaching Science Using Project-Based Learning Strategies Affects the Classroom Learning Environment

ERIC Educational Resources Information Center

Hugerat, Muhamad

2016-01-01

This study involved 458 ninth-grade students from two different Arab middle schools in Israel. Half of the students learned science using project-based learning strategies and the other half learned using traditional methods (non-project-based). The classes were heterogeneous regarding their achievements in the sciences. The adapted questionnaire…

Toward the Decision Tree for Inferring Requirements Maturation Types

NASA Astrophysics Data System (ADS)

Nakatani, Takako; Kondo, Narihito; Shirogane, Junko; Kaiya, Haruhiko; Hori, Shozo; Katamine, Keiichi

Requirements are elicited step by step during the requirements engineering (RE) process. However, some types of requirements are elicited completely after the scheduled requirements elicitation process is finished. Such a situation is regarded as problematic situation. In our study, the difficulties of eliciting various kinds of requirements is observed by components. We refer to the components as observation targets (OTs) and introduce the word “Requirements maturation.” It means when and how requirements are elicited completely in the project. The requirements maturation is discussed on physical and logical OTs. OTs Viewed from a logical viewpoint are called logical OTs, e.g. quality requirements. The requirements of physical OTs, e.g., modules, components, subsystems, etc., includes functional and non-functional requirements. They are influenced by their requesters' environmental changes, as well as developers' technical changes. In order to infer the requirements maturation period of each OT, we need to know how much these factors influence the OTs' requirements maturation. According to the observation of actual past projects, we defined the PRINCE (Pre Requirements Intelligence Net Consideration and Evaluation) model. It aims to guide developers in their observation of the requirements maturation of OTs. We quantitatively analyzed the actual cases with their requirements elicitation process and extracted essential factors that influence the requirements maturation. The results of interviews of project managers are analyzed by WEKA, a data mining system, from which the decision tree was derived. This paper introduces the PRINCE model and the category of logical OTs to be observed. The decision tree that helps developers infer the maturation type of an OT is also described. We evaluate the tree through real projects and discuss its ability to infer the requirements maturation types.

Conservation of ectomycorrhizal fungi: green-tree retention preserves species diversity

Treesearch

Joyce L. Eberhart; Daniel L. Luoma

2013-01-01

Th e Demonstration of Ecosystem Management Options (DEMO) project is a large, interdisciplinary study designed to test the biological and silvicultural eff ects of green-tree retention in Douglas-fi r (Pseudotsuga menziesii) forests. Six treatments were replicated on six blocks in Washington and Oregon, USA: no harvest, 75 percent aggregated, 40 percent (dispersed and...

Spatial continuity of tree attributes in bottomland hardwood forests in the Southeastern United States

Treesearch

Luben D. Dimov; Jim L. Chambers; Brian Roy Lockhart

2005-01-01

Sustainable forest management and conservation require understanding of underlying basic structural and competitive relationships. To gain insight into these relationships, we analyzed spatial continuity of tree basal area (BA) and crown projection area (CPA) on twelve 0.64-ha plots in four mixed bottomland hardwood stands in Louisiana, Arkansas, and Mississippi....

Positive effects of afforestation efforts on the health of urban soils

Treesearch

Emily E. Oldfield; Alexander J. Felson; Stephen A. Wood; Richard A. Hallett; Michael S. Strickland; Mark A. Bradford

2014-01-01

Large-scale tree planting projects in cities are increasingly implemented as a strategy to improve the urban environment. Trees provide multiple benefits in cities, including reduction of urban temperatures, improved air quality, mitigation of storm-water run-off, and provision of wildlife habitat. How urban afforestation affects the properties and functions of urban...

Chapter 12 - Impacts of laurel wilt disease on native Persea ecosystems (Project SO-EM-B-12-05).

Treesearch

Timothy M. Shearman; G. Geoff. Wang

2018-01-01

Although mostly occurring as associate tree species in forest communities, Persea has a wide native distribution in southeast coastal plains (Shearman and others 2015). Laurel wilt disease (LWD) is a lethal vascular infection of trees in the laurel family (Lauraceae) caused by the fungus Raffaelea lauricola (Fraedrich and...

Fire scars and tree vigor following prescribed fires in Missouri Ozark upland forests

Treesearch

Aaron P. Stevenson; Rose-Marie Muzika; Richard P. Guyette

2008-01-01

The goal of our project was to examine basal fire scars caused by prescribed fires and tree vigor in upland forests of the Missouri Ozarks. Fire scar data were collected in 100 plots from black oak (Quercus velutina Lam.), scarlet oak (Q. coccinea Muench.), Shumard oak (Q. shumardii Buckl.), post oak (Q...

Tree Seedlings Establishment Across a Hydrologic Gradient in a Bottomland Restoration

Treesearch

Randall K. Kolka; Carl C. Trettin; E.A. Nelson; W.H. Conner

1998-01-01

Seedling establishment and survival on the Savannah River Site in South Carolina is being monitored as part of the Pen Branch Bottomland Restoration Project. Bottomland tree species were planted from 1993-1995 across a hydrologic gradient which encompasses the drier upper floodplain corridor, the lower floodplain corridor and the continuously inundated delta. Twelve...

Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change

Treesearch

Wen J. Wang; Hong S. He; Frank R. Thompson; Martin A. Spetich; Jacob S. Fraser

2018-01-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are notwell represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts.We investigate how species biological...

Improving tree establishment with forage crops

Treesearch

Eric J. Holzmueller; Carl W. Mize

2003-01-01

Tree establishment in Iowa can be difficult without adequate weed control. Although herbicides are effective at controlling weeds, they may not be desirable in riparian settings and some landowners are opposed to using them. An alternative to herbicides is the use of forage crops to control weeds. A research project was established in 1998 to evaluate the influence of...

The Tree Drawing Test: A Measurement of Cognitive Development. Symposium III B.

ERIC Educational Resources Information Center

Yoshikawa, Kimio; And Others

Reported at this symposium were investigations using the projective "Tree Drawing" test in (1) a case study of the impact of natural and cultural environments on Japanese children attending elementary and junior high schools in Singapore by Kimio Yoshikawa and K. Loganathan Mutharayan; (2) a study comparing cognitive processes of normal,…

Seasonal recovery of chlorotic needles in Scotch pine

Treesearch

Jerry K. Jones; Jerry K. Jones

1971-01-01

As part of a research project on Christmas trees being carried on by the USDA Forest Service's Northeastern Forest Experiment Station, the author made a cooperative study of how discolored needles recover their normal color in February and April. Though this does not solve the Christmas tree growers' problem, it does shed some light on the process involved in...

Interdependence and Integration Learning in Student Project Teams: Do Team Project Assignments Achieve What We Want Them to?

ERIC Educational Resources Information Center

Skilton, Paul F.; Forsyth, David; White, Otis J.

2008-01-01

Building from research on learning in workplace project teams, the authors work forward from the idea that the principal condition enabling integration learning in student team projects is project complexity. Recognizing the challenges of developing and running complex student projects, the authors extend theory to propose that the experience of…

The NASA SCI Files[TM]: The Case of the Powerful Pulleys. A Lesson Guide with Activities in Mathematics, Science, and Technology.

ERIC Educational Resources Information Center

Ricles, Shannon

This teacher's guide, with accompanying videotape, presents an episode of the NASA SCI Files. In this episode, one of the tree house detectives has had an accident and cannot get into the tree house. Using problem-based learning, the rest of the gang investigates the world of simple machines and physical science and "pull" together to…

Problem Solving and Learning in a Natural Task Domain

DTIC Science & Technology

1988-09-01

algorithm is demonstrated in two further examples. For clarity, all examples present a single path through the causal model, rather than the tree ...Any implications are added to the list. EDSEL1 keeps recent chains of reasoning. The tree form of any explorations is retained for future reasoning...9) (CAUSE (CRANK CRANKSHAFT) (OVEVIENT CAMSHAFT ) 9) (CAUSE (CRANK CRANKSHAFT) (TUR:N DRIVESHAFT) 9) STARTER (CAUSE (SWITCH-ACTION SOLENOID ON

Integrating Technologies into Mathematics: Comparing the Cases of Square Roots and Integrals

ERIC Educational Resources Information Center

Kissane, Barry

2016-01-01

Two decades ago, in an award-winning paper, Dan Kennedy (1995) likened learning mathematics to climbing a tree, for which there was only one way to climb: up a large and solid trunk. In the limited time that is available, many students give up the climb, impede others, fall off the trunk, or fail to climb the tree sufficiently well. In the case of…

Is Knowledge Random? Introducing Sampling and Bias through Outdoor Inquiry

ERIC Educational Resources Information Center

Stier, Sam

2010-01-01

Sampling, very generally, is the process of learning about something by selecting and assessing representative parts of that population or object. In the inquiry activity described here, students learned about sampling techniques as they estimated the number of trees greater than 12 cm dbh (diameter at breast height) in a wooded, discrete area…

Localizing OER in Afghanistan: Developing a Multilingual Digital Library for Afghan Teachers

ERIC Educational Resources Information Center

Oates, Lauryn; Hashimi, Jamshid

2016-01-01

The Darakht-e Danesh ("knowledge tree") Online Library is the first open educational resource (OER) initiative in Afghanistan, established to enhance teacher subject-area knowledge, access and use of learning materials, and to foster more diverse teaching methodologies in order to improve learning outcomes in Afghan classrooms. This…

From the One-Hour Field Trip to a Nature Preschool: Partnering with Environmental Organizations

ERIC Educational Resources Information Center

Bailie, Patti Ensel

2010-01-01

Nature education is an important part of early childhood education. As young children develop, the natural world offers concrete and authentic learning experiences. Activities focused on nature support learning in all developmental domains. Children develop physically through hiking in natural terrain, climbing hills and trees, balancing on logs,…

Nature's Advice Book

ERIC Educational Resources Information Center

Mahlin, Kathryn; Robertson, Amy

2005-01-01

What do can people learn from the world around them? Can a tree really teach something about life? Many times teachers provide students with facts about nature but fail to consider what one can learn from the natural world around them. After many months of exploring various ecosystems such as the prairie, rain forest, and desert, one of the…

Student Self-Reported Learning Outcomes of Field Trips: The Pedagogical Impact

ERIC Educational Resources Information Center

Alon, Nirit Lavie; Tal, Tali

2015-01-01

In this study, we used the classification and regression trees (CART) method to draw relationships between student self-reported learning outcomes in 26 field trips to natural environments and various characteristics of the field trip that include variables associated with preparation and pedagogy. We wished to examine the extent to which the…

When Practice Doesn't Make Perfect: Effects of Task Goals on Learning Computing Concepts

ERIC Educational Resources Information Center

Miller, Craig S.; Settle, Amber

2011-01-01

Specifying file references for hypertext links is an elementary competence that nevertheless draws upon core computational thinking concepts such as tree traversal and the distinction between relative and absolute references. In this article we explore the learning effects of different instructional strategies in the context of an introductory…

Acquisition of Inductive Biconditional Reasoning Skills: Training of Simultaneous and Sequential Processing.

ERIC Educational Resources Information Center

Lee, Seong-Soo

1982-01-01

Tenth-grade students (n=144) received training on one of three processing methods: coding-mapping (simultaneous), coding only, or decision tree (sequential). The induced simultaneous processing strategy worked optimally under rule learning, while the sequential strategy was difficult to induce and/or not optimal for rule-learning operations.…

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

ERIC Educational Resources Information Center

Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

2016-01-01

This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

Combining Adaptive Hypermedia with Project and Case-Based Learning

ERIC Educational Resources Information Center

Papanikolaou, Kyparisia; Grigoriadou, Maria

2009-01-01

In this article we investigate the design of educational hypermedia based on constructivist learning theories. According to the principles of project and case-based learning we present the design rational of an Adaptive Educational Hypermedia system prototype named MyProject; learners working with MyProject undertake a project and the system…

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

Projects in Technology Education and Fostering Learning: The Potential and Its Realization

NASA Astrophysics Data System (ADS)

Barak, Moshe; Shachar, Ahron

2008-06-01

The current study aimed at examining the efficacy of technological projects as learning tools by exploring the following questions: the extent to which projects in technology develop students as independent learners; the types of knowledge the students deal with in working on their projects; the role of problem-solving in technological projects; and how projects integrate into traditional schooling. The subjects were 53 high school (12th grade) students who prepared graduating projects in technology under the supervision of nine teachers. Data were collected by observing the students in the laboratory, administrating two questionnaires to both the students and the teachers, and analyzing 25 portfolios prepared by the students of their projects. The findings indicate that projects in technology provide a good opportunity to engage students in challenging tasks that enhance their learning skills. To maximize this potential, it is necessary to employ the project method from the early stages of learning technology. It is especially important that teachers having a strong engineering orientation also acquire pedagogical knowledge on issues such as fostering independent learning, creativity, peer learning and reflective practice in the technological classroom.

Community assessment of tropical tree biomass: challenges and opportunities for REDD.

PubMed

Theilade, Ida; Rutishauser, Ervan; Poulsen, Michael K

2015-12-01

REDD+ programs rely on accurate forest carbon monitoring. Several REDD+ projects have recently shown that local communities can monitor above ground biomass as well as external professionals, but at lower costs. However, the precision and accuracy of carbon monitoring conducted by local communities have rarely been assessed in the tropics. The aim of this study was to investigate different sources of error in tree biomass measurements conducted by community monitors and determine the effect on biomass estimates. Furthermore, we explored the potential of local ecological knowledge to assess wood density and botanical identification of trees. Community monitors were able to measure tree DBH accurately, but some large errors were found in girth measurements of large and odd-shaped trees. Monitors with experience from the logging industry performed better than monitors without previous experience. Indeed, only experienced monitors were able to discriminate trees with low wood densities. Local ecological knowledge did not allow consistent tree identification across monitors. Future REDD+ programmes may benefit from the systematic training of local monitors in tree DBH measurement, with special attention given to large and odd-shaped trees. A better understanding of traditional classification systems and concepts is required for local tree identifications and wood density estimates to become useful in monitoring of biomass and tree diversity.

Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees.

PubMed

Letunic, Ivica; Bork, Peer

2016-07-08

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. The current version was completely redesigned and rewritten, utilizing current web technologies for speedy and streamlined processing. Numerous new features were introduced and several new data types are now supported. Trees with up to 100,000 leaves can now be efficiently displayed. Full interactive control over precise positioning of various annotation features and an unlimited number of datasets allow the easy creation of complex tree visualizations. iTOL 3 is the first tool which supports direct visualization of the recently proposed phylogenetic placements format. Finally, iTOL's account system has been redesigned to simplify the management of trees in user-defined workspaces and projects, as it is heavily used and currently handles already more than 500,000 trees from more than 10,000 individual users. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Distribution of radioactive Cesium in trees and effect of decontamination of forest contaminated by the Fukushima nuclear accident

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iijima, K.; Funaki, H.; Tokizawa, T.

In decontamination pilot projects conducted by Japan Atomic Energy Agency (JAEA), many different techniques were tested to determine their applicability to remediate areas evacuated after the Fukushima Daiichi nuclear accident following the Great Tohoku earthquake and tsunami of March 11, 2011. In addition to buildings, roads and farmland, the forest adjacent to living areas was one of the main decontamination targets. The projects evaluated the radioactive contamination of trees and the effectiveness of decontaminating a highly contaminated evergreen forest. This forest was located 1.3 km southwest of the Fukushima Daiichi Nuclear Power Plant and is dominated by Japanese cedar treesmore » and fir trees. As the first step, three Japanese cedar trees and three fir trees were cut down and the distributions of radioactive cesium (Cs) were measured in each. The total concentrations of {sup 134}Cs and {sup 137}Cs in the leaves and branches were about 1 MBq/kg for both cedar and fir trees, and were appreciably higher than in the bark for cedar. The concentrations in the outer part of the trunks (under the bark) were lower, on the order of 10 kBq/kg, and those in the core of the trunks were lower than 1 kBq/kg for both kinds of trees. The observation that the Cs concentrations are higher in the outer part of trees, is compatible with the assumption that radio-Cs was mostly adsorbed on the surface of trees and partly penetrated into the trunks through the bark. Evolution of air dose rates in a 100 x 60 m pasture adjacent to the forest was monitored during decontamination of the forest and of the pasture itself. The dose rates in the pasture decreased drastically after stripping contaminated topsoil from the pasture and decreased slightly more after stripping contaminated topsoil of the forest floor and pruning the trees. Cutting down and removing 84 trees in the outermost area (10- m width) of the forest also slightly decreased these dose rates. After decontamination, the residual dose rates around the highly contaminated forest were mostly attributed to radioactive Cs existing in or on trees and topsoil in the untouched forest beyond the decontaminated area. (authors)« less

Searching for evidence of curricular effect on the teaching and learning of mathematics: some insights from the LieCal project

NASA Astrophysics Data System (ADS)

Cai, Jinfa

2014-12-01

Drawing on evidence from the Longitudinal Investigation of the Effect of Curriculum on Algebra Learning (LieCal) Project, issues related to mathematics curriculum reform and student learning are discussed. The LieCal Project was designed to longitudinally investigate the impact of a reform mathematics curriculum called the Connected Mathematics Project (CMP) in the USA on teachers' teaching and students' learning. Using a three-level conceptualization of curriculum (intended, implemented, and attained), a variety of evidence from the LieCal Project is presented to show the impact of mathematics curriculum reform on teachers' teaching and students' learning. This paper synthesizes findings from the two longitudinal studies spanning 7 years of the LieCal Project both to show the kind of impact curriculum has on teachers' teaching and students' learning and to suggest powerful but feasible ways researchers can investigate curriculum effect on both teaching and learning.

Detection and Characterization of Stress Symptoms in Forest Vegetation

NASA Technical Reports Server (NTRS)

Heller, R. C.

1971-01-01

Techniques used at the Pacific Southwest Forest and Range Experiment Station to detect advanced and previsual symptoms of vegetative stress are discussed. Stresses caused by bark beetles in coniferous stands of timber are emphasized because beetles induce stress more rapidly than most other destructive agents. Bark beetles are also the most damaging forest insects in the United States. In the work on stress symptoms, there are two primary objectives: (1) to learn the best combination of films, scales, and filters to detect and locate injured trees from aircraft and spacecraft, and (2) to learn if stressed trees can be detected before visual symptoms of decline occur. Equipment and techniques used in a study of the epidemic of the Black Hills bark beetle are described.

Spam comments prediction using stacking with ensemble learning

NASA Astrophysics Data System (ADS)

Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang

2018-01-01

Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.

A Major E-Learning Project to Renovate Science Learning Environment in Taiwan

ERIC Educational Resources Information Center

Chang, Chun-Yen; Lee, Greg

2010-01-01

This article summarizes a major e-Learning project recently funded by the National Science Council of Taiwan and envisions some of the future research directions in this area. This project intends to initiate the "Center for excellence in e-Learning Sciences (CeeLS): i[superscript 4] future learning environment" at the National Taiwan…

Reliability database development for use with an object-oriented fault tree evaluation program

NASA Technical Reports Server (NTRS)

Heger, A. Sharif; Harringtton, Robert J.; Koen, Billy V.; Patterson-Hine, F. Ann

1989-01-01

A description is given of the development of a fault-tree analysis method using object-oriented programming. In addition, the authors discuss the programs that have been developed or are under development to connect a fault-tree analysis routine to a reliability database. To assess the performance of the routines, a relational database simulating one of the nuclear power industry databases has been constructed. For a realistic assessment of the results of this project, the use of one of existing nuclear power reliability databases is planned.

Assessment of the Old Red Rock Indian Line Sycamore Tree, Lake Red Rock, Marion County, Iowa

DTIC Science & Technology

1992-01-01

miles, when reduced to a straight line , from the junction of the White Breast and Des Moines (Stiles 1911:4). George W. Harrison was instructed to...AD-A255 372 Assessment of the Old Red Rock Indian Line Sycamore Tree, Lake Red Rock, Marion County, Iowa DACW25-92-M-0414 by Leah D. Rogers Project...portion of tree 22 9. Map showing location of Red Rock line within treaty cession area of 23 1842 10. Portion of 1844 map showing incorrect placement of

Field data analysis of asphalt road paving damages caused by tree roots

NASA Astrophysics Data System (ADS)

Weissteiner, Clemens; Rauch, Hans Peter

2015-04-01

Tree root damages are a frequent problem along paved cycling paths and service roads of rivers and streams. Damages occur mostly on streets with thin asphalt layers and especially in the upper part of the pavement structure. The maintainers of these roads are faced with frequent and high annual repair costs in order to guarantee traffic safety and pleasant cycling conditions. The focus of this research project is to get an insight in the processes governing the growth of the tree roots in asphalt layers and to develop test methods to avoid rood penetration into the road structure. Tree vegetation has been analysed selectively along a 300 km long cycle and service path of the Danube River in the region of Austria. Tree characteristics, topographic as well as hydrologic conditions have been analysed at 119 spots with different asphalt damage intensities. On 5 spots additional investigations on the root growth characteristics where performed. First results underline a high potential damage of pioneer trees which are growing naturally along rivers. Mostly, local occurring fast growing tree species penetrated the road layer structure. In a few cases other tree species where as well responsible for road structure damages. The age respectively the size of the trees didn't seem to influence significantly the occurrence of asphalt damages. Road structure damages were found to appear unaffected by hydrologic or topographic conditions. However, results have to be interpreted with care as the investigations represent a temporally limited view of the problem situation. The investigations of the root growth characteristics proved that tree roots penetrate the road structure mostly between the gravel sublayer and the asphalt layer as the layers it selves don't allow a penetration because of their high compaction. Furthermore roots appear to be attracted by condensed water at the underside of the asphalt layer. Further steps of the research project imply testing of different compositions of gravel size mixtures as sublayer material. A coarse gravel size mixture allows the condensed water to drain in deeper layers and inhibits root growth because of mechanical impedance and air pruning of roots.

Computational intelligence techniques for biological data mining: An overview

NASA Astrophysics Data System (ADS)

Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

2014-10-01

Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

PubMed

Ypma, Rolf J F; van Ballegooijen, W Marijn; Wallinga, Jacco

2013-11-01

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

Extensions and applications of ensemble-of-trees methods in machine learning

NASA Astrophysics Data System (ADS)

Bleich, Justin

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of violence during probation hearings in court systems.

Learning from Disaster: The Learning Environment of the 2006 Rutgers University Hurricane Katrina Relief Project and How Service Learning Generates Transformative Learning: A Case Study

ERIC Educational Resources Information Center

Heilman, Donald C.

2012-01-01

Problem: The study primarily focused on how a Service Learning project resulted in a Transformative Learning experience. The sample was drawn from 82 participants from Rutgers University who took part in a week-long alternative Spring Break community service project in New Orleans following Hurricane Katrina in 2006. Interviews were conducted…

Preliminary studies of elevated atmospheric CO/sub 2/ on conifers, May 1-December 30, 1985

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helms, J.A.

1985-01-01

The original scope of work consisted of two parts: Intensive physiological studies of Pinus ponderosa seedlings and saplings that were continuously exposed to various levels of CO/sub 2/ and SO/sub 2/ in open-topped chambers. Site selection and preparation in anticipation of DOE approval of a proposed 5-year project on effects of long-term exposure of forest vegetation to enhanced CO/sub 2/. Establishment of 5 Nelder-type plots utilizing 5 western conifers to permit fundamental studies on the physiological bases of tree-to-tree competition. Because the DOE project was not funded, site selection was abandoned.

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Basu, Saikat; Nemani, Ramakrishna R.; Mukhopadhyay, Supratik; Michaelis, Andrew; Votava, Petr

2016-01-01

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

DeepSAT: A Deep Learning Approach to Tree-cover Delineation in 1-m NAIP Imagery for the Continental United States

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Nemani, R. R.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

2016-12-01

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes.