WorldWideScience

Sample records for learning time bloom

  1. A New Bloom: Transforming Learning

    Science.gov (United States)

    Cochran, David; Conklin, Jack

    2007-01-01

    This article discusses a new design for the classic Bloom's Taxonomy developed by Anderson, L. W. & Krathwohl, D. (2001), which can be used to evaluate learners' technology-enhanced experience in more powerful and critical ways. The New Bloom's Taxonomy incorporates contemporary research on learning and human cognition into its model. The…

  2. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  3. Conception of Learning Outcomes in the Bloom's Taxonomy Affective Domain

    Science.gov (United States)

    Savickiene, Izabela

    2010-01-01

    The article raises a problematic issue regarding an insufficient base of the conception of learning outcomes in the Bloom's taxonomy affective domain. The search for solutions introduces the conception of teaching and learning in the affective domain as well as presents validity criteria of learning outcomes in the affective domain. The…

  4. Developing Learning Objectives for Accounting Ethics Using Bloom's Taxonomy

    Science.gov (United States)

    Kidwell, Linda A.; Fisher, Dann G.; Braun, Robert L.; Swanson, Diane L.

    2013-01-01

    The purpose of our article is to offer a set of core knowledge learning objectives for accounting ethics education. Using Bloom's taxonomy of educational objectives, we develop learning objectives in six content areas: codes of ethical conduct, corporate governance, the accounting profession, moral development, classical ethics theories, and…

  5. Bloom's Taxonomy: Improving Assessment and Teaching-Learning Process

    Science.gov (United States)

    Chandio, Muhammad Tufail; Pandhiani, Saima Murtaza; Iqbal, Rabia

    2016-01-01

    This research study critically analyzes the scope and contribution of Bloom's Taxonomy in both assessment and teaching-learning process. Bloom's Taxonomy consists of six stages, namely; remembering, understanding, applying, analyzing, evaluating and creating and moves from lower degree to the higher degree. The study applies Bloom's Taxonomy to…

  6. Technology and Bloom's Taxonomy: Tools to Facilitate Higher-Level Learning in Chemistry

    National Research Council Canada - National Science Library

    Morgan, Matthew

    1997-01-01

    This research project ties together chemistry data acquisition technology, introductory chemistry laboratory experiments, and Bloom's Taxonomy of Educational Objectives into a unified learning model...

  7. The Internationalization of Bloom's Learning for Mastery: A 25-Year Retrospective-Prospective View.

    Science.gov (United States)

    Hymel, Glenn M.; Dyck, Walter E.

    Twenty-five years have elapsed since the publication of Benjamin S. Bloom's article titled "Learning for Mastery." With approximately 2,000 master learning/testing citations in the ERIC data base alone, Bloom's 1968 piece is indeed one of the most generative works to appear in the educational psychology literature in decades. At this…

  8. Unpacking the Revised Bloom's Taxonomy: Developing Case-Based Learning Activities

    Science.gov (United States)

    Nkhoma, Mathews Zanda; Lam, Tri Khai; Sriratanaviriyakul, Narumon; Richardson, Joan; Kam, Booi; Lau, Kwok Hung

    2017-01-01

    Purpose: The purpose of this paper is to propose the use of case studies in teaching an undergraduate course of Internet for Business in class, based on the revised Bloom's taxonomy. The study provides the empirical evidence about the effect of case-based teaching method integrated the revised Bloom's taxonomy on students' incremental learning,…

  9. Timing of migratory baleen whales at the Azores in relation to the North Atlantic spring bloom

    NARCIS (Netherlands)

    Visser, F.; Hartman, K.L.; Pierce, G.J.; Valavanis, V.D.; Huisman, J.

    2011-01-01

    Each year, a phytoplankton spring bloom starts just north of the North Atlantic Subtropical Gyre, and then expands northwards across the entire North Atlantic. Here, we investigate whether the timing of the spring migration of baleen whales is related to the timing of the phytoplankton spring bloom,

  10. Mesoscale Eddies Control the Timing of Spring Phytoplankton Blooms: A Case Study in the Japan Sea

    Science.gov (United States)

    Maúre, E. R.; Ishizaka, J.; Sukigara, C.; Mino, Y.; Aiki, H.; Matsuno, T.; Tomita, H.; Goes, J. I.; Gomes, H. R.

    2017-11-01

    Satellite Chlorophyll a (CHL) data were used to investigate the influence of mesoscale anticyclonic eddies (AEs) and cyclonic eddies (CEs) on the timing of spring phytoplankton bloom initiation around the Yamato Basin (133-139°E and 35-39.5°N) in the Japan Sea, for the period 2002-2011. The results showed significant differences between AEs and CEs in the timing and initiation mechanism of the spring phytoplankton bloom. Blooms were initiated earlier in CEs which were characterized by shallow mixed-layer depths (mixed-layer depth. Conversely, blooms appeared in the AEs despite deeper mixed-layer depth (> 100 m) but close to the commencement of positive Q0. This suggests that the relaxation of turbulent mixing is crucial for the bloom initiation in AEs.

  11. Analysing learning outcomes in an Electrical Engineering curriculum using illustrative verbs derived from Bloom's Taxonomy

    Science.gov (United States)

    Meda, Lawrence; Swart, Arthur James

    2018-05-01

    Learning outcomes are essential to any curriculum in education, where they need to be clear, observable and measurable. However, some academics structure learning outcomes in a way that does not promote student learning. The purpose of this article is to present the analyses of learning outcomes of an Electrical Engineering curriculum offered at a University of Technology in South Africa, in order to determine if academics are structuring them in a way that enables student learning. A qualitative case study is used where the learning outcomes from 33 study guides are reviewed using illustrative verbs derived from Bloom's Taxonomy. Results indicate that 9% of all the learning outcomes are unclear, 10% are unobservable and 23% are unmeasurable. A key recommendation is to provide regular workshops to assist academics in reviewing their learning outcomes using the illustrative verbs derived from Bloom's Taxonomy, thereby ensuring that their learning outcomes promote student learning.

  12. Measurement of Learning Process by Semantic Annotation Technique on Bloom's Taxonomy Vocabulary

    Science.gov (United States)

    Yanchinda, Jirawit; Yodmongkol, Pitipong; Chakpitak, Nopasit

    2016-01-01

    A lack of science and technology knowledge understanding of most rural people who had the highest education at elementary education level more than others level is unsuccessfully transferred appropriate technology knowledge for rural sustainable development. This study provides the measurement of the learning process by on Bloom's Taxonomy…

  13. Sales Course Design Using Experiential Learning Principles and Bloom's Taxonomy

    Science.gov (United States)

    Healy, William J.; Taran, Zinaida; Betts, Stephen C.

    2011-01-01

    Practitioner concerns and the changing educational marketplace are pressuring colleges to provide more skills based learning. Among the newer skill based areas of study that is greatly in demand is professional sales. In this paper, two courses in a successful professional sales program are examined through the lenses of experiential learning…

  14. Harmful algal bloom smart device application: using image analysis and machine learning techniques for classification of harmful algal blooms

    Science.gov (United States)

    Northern Kentucky University and the U.S. EPA Office of Research Development in Cincinnati Agency are collaborating to develop a harmful algal bloom detection algorithm that estimates the presence of cyanobacteria in freshwater systems by image analysis. Green and blue-green alg...

  15. [Problem based learning: achievement of educational goals in the information and comprehension sub-categories of Bloom cognitive domain].

    Science.gov (United States)

    Montecinos, P; Rodewald, A M

    1994-06-01

    The aim this work was to assess and compare the achievements of medical students, subjected to problem based learning methodology. The information and comprehension categories of Bloom were tested in 17 medical students in four different occasions during the physiopathology course, using a multiple choice knowledge test. There was a significant improvement in the number of correct answers towards the end of the course. It is concluded that these medical students obtained adequate learning achievements in the information subcategory of Bloom using problem based learning methodology, during the physiopathology course.

  16. Harmful algal blooms and climate change: Learning from the past and present to forecast the future

    CSIR Research Space (South Africa)

    Wells, ML

    2015-11-01

    Full Text Available Harmful algal blooms and climate change: Learning from the past and present to forecast the future Mark L. Wellsa,*, Vera L. Trainerb, Theodore J. Smaydac, Bengt S.O. Karlsond, Charles G. Tricke, Raphael M. Kudelaf, Akira Ishikawag, Stewart Bernardh... and Atmospheric Administration, 2725 Montlake Blvd. E., Seattle, WA 98112, USA c Graduate School of Oceanography, University of Rhode Island, Kingston, RI 02881, USA d SMHI Research & Development, Oceanography, Sven Ka¨llfelts gata 15, 426 71 Va¨stra Fro...

  17. Winter atmospheric circulation signature for the timing of the spring bloom of diatoms in the North Sea

    Science.gov (United States)

    Lohmann, Gerrit; Wiltshire, Karen

    2015-04-01

    Analysing long-term diatom data from the German Bight and observational climate data for the period 1962-2005, we found a close connection of the inter-annual variation of the timing of the spring bloom with the boreal winter atmospheric circulation. We examined the fact that high diatom counts of the spring bloom tended to occur later when the atmospheric circulation was characterized by winter blocking over Scandinavia. The associated pattern in the sea level pressure showed a pressure dipole with two centres located over the Azores and Norway and was tilted compared to the North Atlantic Oscillation. The bloom was earlier when the cyclonic circulation over Scandinavia allowed an increased inflow of Atlantic water into the North Sea which is associated with clearer, more marine water, and warmer conditions. The bloom was later when a more continental atmospheric flow from the east was detected. At Helgoland Roads, it seems that under turbid water conditions (= low light) zooplankton grazing can affect the timing of the phytoplankton bloom negatively. Warmer water temperatures will facilitate this. Under clear water conditions, light will be the main governing factor with regard to the timing of the spring bloom. These different water conditions are shown here to be mainly related to large-scale weather patterns. We found that the mean diatom bloom could be predicted from the sea level pressure one to three months in advance. Using historical pressure data, we derived a proxy for the timing of the spring bloom over the last centuries, showing an increased number of late (proxy-) blooms during the eighteenth century when the climate was considerably colder than today. We argue that these variations are important for the interpretation of inter-annual to centennial variations of biological processes. This is of particular interest when considering future scenarios, as well to considerations on past and future effects on the primary production and food webs.

  18. Harmful algal blooms and climate change: Learning from the past and present to forecast the future

    Science.gov (United States)

    Wells, Mark L.; Trainer, Vera L.; Smayda, Theodore J.; Karlson, Bengt S.O.; Trick, Charles G.; Kudela, Raphael M.; Ishikawa, Akira; Bernard, Stewart; Wulff, Angela; Anderson, Donald M.; Cochlan, William P.

    2015-01-01

    barriers, how stratification may enhance or diminish HAB events, how trace nutrients (metals, vitamins) influence cell toxicity, and how grazing pressures may leverage, or mitigate HAB development. There is an absence of high quality time-series data in most regions currently experiencing HAB outbreaks, and little if any data from regions expected to develop HAB events in the future. A subset of observer sites is recommended to help develop stronger linkages among global, national, and regional climate change and HAB observation programs, providing fundamental datasets for investigating global changes in the prevalence of harmful algal blooms. Forecasting changes in HAB patterns over the next few decades will depend critically upon considering harmful algal blooms within the competitive context of plankton communities, and linking these insights to ecosystem, oceanographic and climate models. From a broader perspective, the nexus of HAB science and the social sciences of harmful algal blooms is inadequate and prevents quantitative assessment of impacts of future HAB changes on human well-being. These and other fundamental changes in HAB research will be necessary if HAB science is to obtain compelling evidence that climate change has caused alterations in HAB distributions, prevalence or character, and to develop the theoretical, experimental, and empirical evidence explaining the mechanisms underpinning these ecological shifts. PMID:27011761

  19. The Evaluation of the Cognitive Learning Process of the Renewed Bloom Taxonomy Using a Web Based Expert System

    Science.gov (United States)

    Goksu, Idris

    2016-01-01

    The aim of this study is to develop the Web Based Expert System (WBES) which provides analyses and reports based on the cognitive processes of Renewed Bloom Taxonomy (RBT), and to put forward the impact of the supportive education provided in line with these reports, on the academic achievement and mastery learning state of the students. The study…

  20. Harmful algal bloom smart device application: using image analysis and machine learning techniques for early classification of harmful algal blooms (SETAC presentation)

    Science.gov (United States)

    Reports of toxic cyanobacterial blooms, also known as Harmful Algal Blooms (HABS) have increased drastically in recent years. HABS impact human health from causing mild allergies to liver damage and death. The Ecological Stewardship Institute (ESI) at Northern Kentucky Universi...

  1. Retention time generates short-term phytoplankton blooms in a shallow microtidal subtropical estuary

    Science.gov (United States)

    Odebrecht, Clarisse; Abreu, Paulo C.; Carstensen, Jacob

    2015-09-01

    In this study it was hypothesised that increasing water retention time promotes phytoplankton blooms in the shallow microtidal Patos Lagoon estuary (PLE). This hypothesis was tested using salinity variation as a proxy of water retention time and chlorophyll a for phytoplankton biomass. Submersible sensors fixed at 5 m depth near the mouth of PLE continuously measured water temperature, salinity and pigments fluorescence (calibrated to chlorophyll a) between March 2010 and 12th of December 2011, with some gaps. Salinity variations were used to separate alternating patterns of outflow of lagoon water (salinity 24; 35% of the time). The two transition phases represented a rapid change from lagoon water outflow to marine water inflow and a more gradually declining salinity between the dominating inflow and outflow conditions. During the latter of these, a significant chlorophyll a increase relative to that expected from a linear mixing relationship was observed at intermediate salinities (10-20). The increase in chlorophyll a was positively related to the duration of the prior coastal water inflow in the PLE. Moreover, chlorophyll a increase was significantly higher during austral spring-summer than autumn-winter, probably due to higher light and nutrient availability in the former. Moreover, the retention time process operating on time scales of days influences the long-term phytoplankton variability in this ecosystem. Comparing these results with monthly data from a nearby long-term water quality monitoring station (1993-2011) support the hypothesis that chlorophyll a accumulations occur after marine inflow events, whereas phytoplankton does not accumulate during high water outflow, when the water residence time is short. These results suggest that changing hydrological pattern is the most important mechanism underlying phytoplankton blooms in the PLE.

  2. Short time series analysis of Didymosphenia geminata blooming in the Oreti River, New Zealand

    Science.gov (United States)

    Garcia, T.; Kilroy, C.; Larned, S.; Packman, A. I.; Kumar, P.

    2010-12-01

    The mat-forming diatom Didymosphenia geminata was introduced to New Zealand in 2004, and subsequently spread to many rivers on the south island. D geminata mats are exceptionally dense and thick. Extensive blooms of this introduced organism have substantially modified the benthic environment in many New Zealand rivers, but the factors that contribute to D. geminata blooming are not well understood. We synthesized a sequence of observations of D. geminata areal coverage and thickness to examine physical and chemical controls on the growth and persistence of D germinata. We analyzed the best available time series on the distribution of this organism in New Zealand, observations in the Oreti River every 15 days spanning April 2006 to May 2007. During this period, mean D. geminata coverage of the river bed was ~52% and the mean mat thickness was ~6 mm. Relationships between time-series observations of D. geminata and 13 different physical and chemical variables were analyzed using linear and nonlinear methods. Areal cover and thickness of D geminata mats were found to be influenced by both slow and fast dynamic processes. The spread of the organism, in terms of % cover, was highly correlated with conductivity, ammonium, nitrate, dissolved oxygen, and total nitrogen with short time lags (fast dynamics). Moreover, water clarity, cloud cover, and flow were highly correlated with % cover with long time lags, indicating that these conditions exert long-term control on D. geminata growth. Areal coverage and thickness were found to be highly correlated, but the variables associated with slow and fast dynamics of these two measures were not identical. The variables found to be highly correlated with D. germinata thickness and represented fast dynamics were temperature, dissolved oxygen, conductivity, nitrate, and total nitrogen. Additionally, the variables influencing the slow dynamics of D. germinata thickness were flow, water clarity, turbidity and total phosphorous.

  3. Categorization of ber varieties in relation to blooming period, fruit setting and harvesting time

    International Nuclear Information System (INIS)

    Sharif, N.; Abbas, M.M.; Ishfaq, M.; Memon, N.U.N.

    2013-01-01

    Thirty four local Ber varieties were evaluated at Horticultural Research Institute AARI, Faisalabad, Horticultural Research Station Bahawalpur (Punjab) and Jujube Research Station, Tandojam (Sindh). Traits viz. total period of blooming (dates), peak period of blooming (dates), total period of fruit set (dates), peak period of fruit set (dates), total period of fruit harvest (dates), peak period of fruit harvest (dates), total flowering days, peak flowering days, total fruit setting days, peak fruit setting days, total harvesting days and peak harvesting days were studied. The results revealed significant differences in parameters studied except total period of blooming under Tandojam, Sindh conditions. Varieties were classified as early, mid and late season for both provinces. Local varieties had potential for further manipulation in terms of variety improvement to attract growers for extensive ber cultivations under changing global climatic scenario. (author)

  4. Learning During Stressful Times

    Science.gov (United States)

    Shors, Tracey J.

    2012-01-01

    Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

  5. Switching from Bloom to the Medicine Wheel: Creating Learning Outcomes That Support Indigenous Ways of Knowing in Post-Secondary Education

    Science.gov (United States)

    LaFever, Marcella

    2016-01-01

    Based on a review of works by Indigenous educators, this paper suggests a four-domain framework for developing course outcome statements that will serve all students, with a focus on better supporting the educational empowerment of Indigenous students. The framework expands the three domains of learning, pioneered by Bloom to a four-domain…

  6. Learning Time and Educational Effectiveness.

    Science.gov (United States)

    Anderson, Lorin W.

    1980-01-01

    To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…

  7. Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Weilong Song

    2015-10-01

    Full Text Available This paper describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large ocean region and formulate strategies for deploying robots in the coastal ocean for more detailed in situ exploration. The difficulty is that there are insufficient in situ data to create a direct statistical machine learning model with satellite data inputs. To solve this problem, we built a Random Forest model using MODIS and MERIS satellite data and applied a threshold filter to balance the training inputs and labels. To build this model, several features of remote sensing satellites were tested to obtain the most suitable features for the system. After building the model, we compared our random forest model with previous trials based on a Support Vector Machine (SVM using satellite data from 221 days, and our approach performed significantly better. Finally, we used the latest in situ data from a September 2014 field experiment to validate our model.

  8. Climate Change and Algal Blooms =

    Science.gov (United States)

    Lin, Shengpan

    Algal blooms are new emerging hazards that have had important social impacts in recent years. However, it was not very clear whether future climate change causing warming waters and stronger storm events would exacerbate the algal bloom problem. The goal of this dissertation was to evaluate the sensitivity of algal biomass to climate change in the continental United States. Long-term large-scale observations of algal biomass in inland lakes are challenging, but are necessary to relate climate change to algal blooms. To get observations at this scale, this dissertation applied machine-learning algorithms including boosted regression trees (BRT) in remote sensing of chlorophyll-a with Landsat TM/ETM+. The results show that the BRT algorithm improved model accuracy by 15%, compared to traditional linear regression. The remote sensing model explained 46% of the total variance of the ground-measured chlorophyll- a in the first National Lake Assessment conducted by the US Environmental Protection Agency. That accuracy was ecologically meaningful to study climate change impacts on algal blooms. Moreover, the BRT algorithm for chlorophyll- a would not have systematic bias that is introduced by sediments and colored dissolved organic matter, both of which might change concurrently with climate change and algal blooms. This dissertation shows that the existing atmospheric corrections for Landsat TM/ETM+ imagery might not be good enough to improve the remote sensing of chlorophyll-a in inland lakes. After deriving long-term algal biomass estimates from Landsat TM/ETM+, time series analysis was used to study the relations of climate change and algal biomass in four Missouri reservoirs. The results show that neither temperature nor precipitation was the only factor that controlled temporal variation of algal biomass. Different reservoirs, even different zones within the same reservoir, responded differently to temperature and precipitation changes. These findings were further

  9. Prime Time for Learning.

    Science.gov (United States)

    Leidy, Vivian; And Others

    1981-01-01

    Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)

  10. Time and Associative Learning.

    Science.gov (United States)

    Balsam, Peter D; Drew, Michael R; Gallistel, C R

    2010-01-01

    In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

  11. Establishing Time for Professional Learning

    Science.gov (United States)

    Journal of Staff Development, 2013

    2013-01-01

    Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…

  12. Real-time monitoring of harmful algal blooms in the southern ...

    African Journals Online (AJOL)

    A half-hourly acquisition regime collects data from the instruments, which are transmitted in real time using cellular phone telemetry. A website is updated with these data, when available, along with satellite data and shellfish warnings, to provide near real-time information on conditions in the area. Demonstration data from ...

  13. Harmful algal blooms

    Digital Repository Service at National Institute of Oceanography (India)

    Bhat, S.R.; PrabhaDevi; DeSouza, L.; Verlecar, X.N.; Naik, C.G.

    as harmful algal bloom. Bloom formation is a natural process and it enhances biological productivity, but turns worrisome when caused by toxic species, leading to massive fish mortalities and hazards to human health. Incidences of'red tide' are increasing...

  14. Hydrodynamic control of microphytoplankton bloom in a coastal sea

    Indian Academy of Sciences (India)

    Hydrodynamic control of microphytoplankton bloom in a coastal sea ... many times more than what could be accounted for by solar insolation and nutrient levels. ... and stable water column and weak winds left undisturbed, the transient bloom.

  15. If You Don't Know Who Wrote it, You Won't Understand It: Lessons Learned from Benjamin S. Bloom.

    Science.gov (United States)

    Anderson, Lorin W.

    1996-01-01

    Describes how one college professor's relationship with author Benjamin Bloom influenced his thinking about education and his reading of education. Explains how the professor's personal and professional understandings of Bloom influenced his reading of virtually all of Bloom's writings, and discusses three points that are central to understanding…

  16. Analysing Learning Outcomes in an Electrical Engineering Curriculum Using Illustrative Verbs Derived from Bloom's Taxonomy

    Science.gov (United States)

    Meda, Lawrence; Swart, Arthur James

    2018-01-01

    Learning outcomes are essential to any curriculum in education, where they need to be clear, observable and measurable. However, some academics structure learning outcomes in a way that does not promote student learning. The purpose of this article is to present the analyses of learning outcomes of an Electrical Engineering curriculum offered at a…

  17. Making Culture Bloom

    Directory of Open Access Journals (Sweden)

    Iain McCalman

    2013-08-01

    Full Text Available On 16 June 1904, exactly one hundred years before the establishment of CHASS, an Irish Jew of Hungarian extraction called Leopold Bloom set off on a twenty-four hour perambulation around the streets and bars of Dublin. This fictional incident is the basis of James Joyce’s Ulysses, the greatest novel of modern times. It has also given rise to Bloomsday, a kind of Irish literary holy day celebrated in cities all around the world. It was a specially appropriate moment for us to celebrate the birth of our new peak body, because Bloomsday provides a perfect parable for why the Australian public and government should cherish our sector.

  18. Timepiece: Extending and Enhancing Learning Time.

    Science.gov (United States)

    Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.

    This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…

  19. A Preliminary Bloom's Taxonomy Assessment of End-of-Chapter Problems in Business School Textbooks

    Science.gov (United States)

    Marshall, Jennings B.; Carson, Charles M.

    2008-01-01

    This article examines textbook problems used in a sampling of some of the most common core courses found in schools of business to ascertain what level of learning, as defined by Bloom's Taxonomy, is required to provide a correct answer. A set of working definitions based on Bloom's Taxonomy (Bloom & Krathwohl, 1956) was developed for the six…

  20. A Pilot Study of Students' Learning Outcomes Using Didactic and Socratic Instructional Methods: An Assessment Based on Bloom's Taxonomy

    Science.gov (United States)

    Akinde, Oluwatoyin Adenike

    2015-01-01

    This work is a pilot study on the learning outcomes of students, who were taught a research course for seven weeks, using didactic and Socratic instruction methods. The course was taught in two sessions concurrently. The students were divided into two groups (A and B) and both groups were taught either with Socratic instruction method or didactic…

  1. E-Learning, Time and Unconscious Thinking

    Science.gov (United States)

    Mathew, David

    2014-01-01

    This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…

  2. An Automatic Monitoring System for High-Frequency Measuring and Real-Time Management of Cyanobacterial Blooms in Urban Water Bodies

    Directory of Open Access Journals (Sweden)

    Viet Tran Khac

    2018-01-01

    Full Text Available Urban lakes mitigate the negative impacts on the hydrological cycle and improve the quality of life in cities. Worldwide, the concern increases for the protection and management of urban water bodies. Since the physical-chemical and biological conditions of a small aquatic ecosystem can vary rapidly over time, traditional low frequency measurement approaches (weekly or monthly sampling limits the knowledge and the transfer of research outcomes to management decision-making. In this context, this paper presents an automatic monitoring system including a full-scale experimental site and a data transfer platform for high-frequency observations (every 5 min in a small and shallow urban lake (Lake Champs-sur-Marne, Paris, France, 10.3 ha. Lake stratification and mixing periods can be clearly observed, these periods are compared with the dynamic patterns of chlorophyll-a, phycocyanin, dissolved oxygen and pH. The results indicate that the phytoplankton growth corresponds with dissolved oxygen cycles. However, thermal stratification cannot totally explain the entire dynamic patterns of different physical-chemical and ecological variables. Besides, the cyanobacteria is one of the dominating groups of phytoplankton blooms during the lake stratification periods (8 August–29 September 2016. During the cooling mixed period (29 September–19 October 2016, the high concentration of chlorophyll-a is mainly caused by the other phytoplankton species, such as diatoms. Perspectives are discussed in order to apply this observation system for real-time management of water bodies and lakes.

  3. Benjamin Bloom: His Research and Influence on Education.

    Science.gov (United States)

    Anderson, Lorin W.

    1988-01-01

    A student of the University of Chicago's Measurement, Evaluation, and Statistical Analysis Program reflects upon Benjamin S. Bloom's professional and personal educational contributions, including mastery learning, educational equity, and educational excellence. (CB)

  4. Communication, timing, and common learning

    Czech Academy of Sciences Publication Activity Database

    Steiner, Jakub; Stewart, C.

    2011-01-01

    Roč. 146, č. 1 (2011), s. 230-247 ISSN 0022-0531 Institutional research plan: CEZ:AV0Z70850503 Keywords : common knowledge * learning * communication Subject RIV: AH - Economics Impact factor: 1.235, year: 2011

  5. Leading Learning in Our Times

    Science.gov (United States)

    Trilling, Bernie

    2010-01-01

    Important tools that schools need to support a 21st century approach to teaching and learning include the usual suspects: the Internet, pen and paper, cell phones, educational games, tests and quizzes, good teachers, caring communities, educational funding, and loving parents. All of these items and more contribute to a 21st century education, but…

  6. Timing of the inhibitory effect of fruit on return bloom of 'Valencia' sweet orange (Citrus sinensis (L.) Osbeck).

    Science.gov (United States)

    Martínez-Fuentes, Amparo; Mesejo, Carlos; Reig, Carmina; Agustí, Manuel

    2010-08-30

    In Citrus the inhibitory effect of fruit on flower formation is the main cause of alternate bearing. Although there are some studies reporting the effect on flowering of the time of fruit removal in a well-defined stage of fruit development, few have investigated the effect throughout the entire fruit growth stage from early fruitlet growth to fruit maturity. The objective of this study was to determine the phenological fruit developmental stage at which the fruit begins its inhibitory effect on flowering in sweet orange by manual removal of fruits, and the role of carbohydrates and nitrogen in the process. Fruit exerted its inhibitory effect from the time it was close to reaching its maximum weight, namely 90% of its final size (November) in the present experiments, to bud sprouting (April). The reduction in flowering paralleled the reduction in bud sprouting. This reduction was due to a decrease in the number of generative sprouted buds, whereas mixed-typed shoots were largely independent of the time of fruit removal, and vegetative shoots increased in frequency. The number of leaves and/or flowers per sprouted shoot was not significantly modified by fruit load. In 'Valencia' sweet orange, fruit inhibits flowering from the time it completes its growth. Neither soluble sugar content nor starch accumulation in leaves due to fruit removal was related to flowering intensity, but some kind of imbalance in nitrogen metabolism was observed in trees tending to flower scarcely. Copyright (c) 2010 Society of Chemical Industry.

  7. A numerical investigation of phytoplankton and Pseudocalanus elongatus dynamics in the spring bloom time in the Gdańsk Gulf

    Science.gov (United States)

    Dzierzbicka-Głowacka, Lidia

    2005-01-01

    A nutrient-phytoplankton-zooplankton-detritus (1D-NPZD) `phytoplankton {Phyt} and Pseudocalanus elongatus {Zoop} dynamics in the spring bloom time in the Gdańsk Gulf. The 1D-NPZD model consists of three coupled, partial second-order differential equations of the diffusion type for phytoplankton {Phyt}, zooplankton {Zoop}, nutrients {Nutr} and one ordinary first-order differential equation for benthic detritus pool {Detr}, together with initial and boundary conditions. In this model, the {Zoop} is presented by only one species of copepod ( P. elongatus) and {Zoop} is composed of six cohorts of copepods with weights ( Wi) and numbers ( Zi); where Zoop= limit∑i=16W iZ i. The calculations were made for 90 days (March, April, May) for two stations at Gdańsk Gulf with a vertical space step of 0.5m and a time step of 900 s. The flow field and water temperature used as the inputs in the biological model 1D-NPZD were reproduced by the prognostic numerical simulation technique using hydrographic climatological data. The results of the numerical investigations described here were compared with the mean observed values of surface chlorophyll- a and depth integrated P. elongatus biomass for 10 years, 1980-1990. The slight differences between the calculated and mean observed values of surface chlorophyll- a and zooplankton biomass are ca. 10-60 mg C m -3 and ca. 5-23 mg C m -2, respectively, depending on the location of the hydrographic station. The 1D-NPZD model with a high-resolution zooplankton module for P. elongatus can be used to describe the temporal patterns for phytoplankton biomass and P. elongatus in the centre of the Gdańsk Gulf.

  8. Byatt versus Bloom

    DEFF Research Database (Denmark)

    Børch, Marianne

    2016-01-01

    Antonia Byatt's Possession takes issue with Harold Bloom's famous claim that creation - including an author's creative reading of an intertext - entails a violent encounter. Byatt's book suggests a more positive Construction of the process by which tradition is transformed in transmission....

  9. Algae Bloom in a Lake

    Directory of Open Access Journals (Sweden)

    David Sanabria

    2008-01-01

    Full Text Available The objective of this paper is to determine the likelihood of an algae bloom in a particular lake located in upstate New York. The growth of algae in this lake is caused by a high concentration of phosphorous that diffuses to the surface of the lake. Our calculations, based on Fick's Law, are used to create a mathematical model of the driving force of diffusion for phosphorous. Empirical observations are also used to predict whether the concentration of phosphorous will diffuse to the surface of this lake within a specified time and under specified conditions.

  10. Learned Interval Time Facilitates Associate Memory Retrieval

    Science.gov (United States)

    van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

    2017-01-01

    The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

  11. Harmful Algal Blooms

    Science.gov (United States)

    Graham, Jennifer L.

    2007-01-01

    What are Harmful Algal Blooms (HABs)? Freshwater and marine harmful algal blooms (HABs) can occur anytime water use is impaired due to excessive accumulations of algae. HAB occurrence is affected by a complex set of physical, chemical, biological, hydrological, and meteorological conditions making it difficult to isolate specific causative environmental factors. Potential impairments include reduction in water quality, accumulation of malodorous scums in beach areas, algal production of toxins potent enough to poison both aquatic and terrestrial organisms, and algal production of taste-and-odor compounds that cause unpalatable drinking water and fish. HABs are a global problem, and toxic freshwater and (or) marine algae have been implicated in human and animal illness and death in over 45 countries worldwide and in at least 27 U.S. States (Yoo and others, 1995; Chorus and Bartram, 1999; Huisman and others, 2005).

  12. Learning about Learning: Action Learning in Times of Organisational Change

    Science.gov (United States)

    Hill, Robyn

    2009-01-01

    This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

  13. Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

    Science.gov (United States)

    Warhurst, Russell

    2013-01-01

    Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

  14. WATER BLOOM OF BLUEGREEN ALGE IN CARP FISHPOUNDS

    Directory of Open Access Journals (Sweden)

    Melita Mihaljević

    1996-03-01

    Full Text Available The massive development of bluegreen algae (Cyanophyta/Cyanobacteria, the so--called water bloom, is a frequent phenomenon in fishpond ecosystems. This study analyses water bloom development in three carp fishponds owned by a fishbreeding company at Donji Miholjac (Croatia, where one-year-old carps (Cyprinus carpio , were bred in defferent fishstock densities. Analyses of physicallychemical properties of water and phytoplankton biomass were per- formed in fortnight intervals from May till October, 1992. In all there investigated fishponds the water bloom of bluegreen algae developed, but at a different time and showing a different qualitative composition. In the fishpond with fishstock density of 250 kg/ha water bloom consisted of the species Aphanizomenon flos-aquae, and the biggest biomass (131.92 mg/I was found in August. In the fishpond with fishstock density of 437 kg/ha a water bloom consisting of species from the genues Anabaena and species Aphanizomenon flos-aquae developed at the end of July. In the fishpond with the so--called intensive breeding (fishstock density of 750 kg/ha water bloom of the species Microcystis aeruginosa developed as late as September. The beginning of water bloom development was caused by the low value (lower than 7 of the ratio between the quantities of total phosphorus and total nitrogen. However, the qualitative composition of water bloom was influenced by one-year-old carp fingerlings density.

  15. Seasonal and interannual variabilities of coccolithophore blooms in the Bay of Biscay and the Celtic Sea observed from a 18-year time-series of non-algal Suspended Particulate Matter images

    Science.gov (United States)

    Perrot, Laurie; Gohin, Francis; Ruiz-Pino, Diana; Lampert, Luis

    2016-04-01

    Coccolithophores belong to the nano-phytoplankton size-class and produce CaCO3 scales called coccoliths which form the «shell» of the algae cell. Coccoliths are in the size range of a few μm and can also be detached from the cell in the water. This phytoplankton group has an ubiquitous distribution in all oceans but blooms only in some oceanic regions, like the North East Atlantic ocean and the South Western Atlantic (Patagonian Sea). At a global scale coccolithopore blooms are studied in regard of CaCO3 production and three potential feedback on climate change: albedo modification by the way of dimethylsulfide (DMS) production and atmospheric CO2 source by calcification and a CO2 pump by photosynthesis. As the oceans are more and more acidified by anthropogenic CO2 emissions, coccolithophores generally are expected to be negatively affected. However, recent studies have shown an increase in coccolithophore occurrence in the North Atlantic. A poleward expansion of the coccolithophore Emiliana Huxleyi has also been pointed out. By using a simplified fuzzy method applied to a 18-year time series of SeaWiFS (1998-2002) and MODIS (2003-2015) spectral reflectance, we assessed the seasonal and inter-annual variability of coccolithophore blooms in the vicinity of the shelf break in the Bay of Biscay and the Celtic Sea After identification of the coccolith pixels by applying the fuzzy method, the abundance of coccoliths is assessed from a database of non-algal Suspended Particulate Matter (SPM). Although a regular pattern in the phenology of the blooms is observed, starting south in April in Biscay and moving northwards until July in Ireland, there is a high seasonal and interannual variability in the extent of the blooms. Year 2014 shows very low concentrations of detached coccoliths (twice less than average) from space and anomalies point out the maximum level in 2001. Non-algal SPM, derived from a procedure defined for the continental shelf, appears to be well

  16. Surface layer and bloom dynamics observed with the Prince William Sound Autonomous Profiler

    Science.gov (United States)

    Campbell, R. W.

    2016-02-01

    As part of a recent long term monitoring effort, deployments of a WETLabs Autonomous Moored Profiler (AMP) began Prince William Sound (PWS) in 2013. The PWS AMP consists of a positively buoyant instrument frame, with a winch and associated electronics that profiles the frame from a park depth (usually 55 m) to the surface by releasing and retrieving a thin UHMWPE tether; it generally conducts a daily cast and measures temperature, salinity, chlorophyll-a fluorescence, turbidity, and oxygen and nitrate concentrations. Upward and downward looking ADCPs are mounted on a float below the profiler, and an in situ plankton imager is in development and will be installed in 2016. Autonomous profilers are a relatively new technology, and early deployments experienced a number of failures from which valuable lessons may be learned. Nevertheless, an unprecedented time series of the seasonal biogeochemical procession in the surface waters coastal Gulf of Alaska was collected in 2014 and 2015. The northern Gulf of Alaska has experienced a widespread warm anomaly since early 2014, and surface layer temperature anomalies in PWS were strongly positive during winter 2014. The spring bloom observed by the profiler began 2-3 weeks earlier than average, with surface nitrate depleted by late April. Although surface temperatures were still above average in 2015, bloom timing was much later, with a short vigorous bloom in late April and a subsurface bloom in late May that coincided with significant nitrate drawdown. As well as the vernal blooms, wind-driven upwelling events lead to several small productivity pulses that were evident in changes in nitrate and oxygen concentrations, and chlorophyll-a fluorescence. As well as providing a mechanistic understanding of surface layer biogeochemistry, high frequency observations such as these put historical observations in context, and provide new insights into the scales of variability in the annual cycles of the surface ocean in the North

  17. Students' Pressure, Time Management and Effective Learning

    Science.gov (United States)

    Sun, Hechuan; Yang, Xiaolin

    2009-01-01

    Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…

  18. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  19. Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qiuwen, E-mail: qchen@rcees.ac.cn [RCEES, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 10085 (China); China Three Gorges University, Daxuelu 8, Yichang 443002 (China); CEER, Nanjing Hydraulics Research Institute, Guangzhoulu 223, Nanjing 210029 (China); Rui, Han; Li, Weifeng; Zhang, Yanhui [RCEES, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 10085 (China)

    2014-06-01

    Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004–2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial–temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial–temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. - Highlights: • An innovative method is developed to analyze algal bloom risks with uncertainties. • The algal blooms in Taihu Lake showed obvious spatial and temporal patterns. • The lake is mainly characterized as moderate bloom but with high uncertainty. • Severe bloom with low uncertainty appeared occasionally in the northwest part. • The results provide important information to bloom monitoring and management.

  20. Blooms of phytoplankton along the west coast of India associated with nutrient enrichment and the response of zooplankton

    Digital Repository Service at National Institute of Oceanography (India)

    Nair, S.R.S.; Devassy, V.P.; Madhupratap, M.

    and dinoflagellates are also common Spectacular bloom formations of Trichodesmium is a regular phenomenon during the later part of the NE monsoon season At times, these blooms cover hundreds of kilometres Very often successions of phyto- and zooplankton communities...

  1. Linear time relational prototype based learning.

    Science.gov (United States)

    Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

    2012-10-01

    Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

  2. Overcoming Learning Time And Space Constraints Through Technological Tool

    Directory of Open Access Journals (Sweden)

    Nafiseh Zarei

    2015-08-01

    Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog

  3. Phytoplankton blooms in estuarine and coastal waters: Seasonal patterns and key species

    Science.gov (United States)

    Carstensen, Jacob; Klais, Riina; Cloern, James E.

    2015-01-01

    Phytoplankton blooms are dynamic phenomena of great importance to the functioning of estuarine and coastal ecosystems. We analysed a unique (large) collection of phytoplankton monitoring data covering 86 coastal sites distributed over eight regions in North America and Europe, with the aim of investigating common patterns in the seasonal timing and species composition of the blooms. The spring bloom was the most common seasonal pattern across all regions, typically occurring early (February–March) at lower latitudes and later (April–May) at higher latitudes. Bloom frequency, defined as the probability of unusually high biomass, ranged from 5 to 35% between sites and followed no consistent patterns across gradients of latitude, temperature, salinity, water depth, stratification, tidal amplitude or nutrient concentrations. Blooms were mostly dominated by a single species, typically diatoms (58% of the blooms) and dinoflagellates (19%). Diatom-dominated spring blooms were a common feature in most systems, although dinoflagellate spring blooms were also observed in the Baltic Sea. Blooms dominated by chlorophytes and cyanobacteria were only common in low salinity waters and occurred mostly at higher temperatures. Key bloom species across the eight regions included the diatoms Cerataulina pelagica and Dactyliosolen fragilissimus and dinoflagellates Heterocapsa triquetra and Prorocentrum cordatum. Other frequent bloom-forming taxa were diatom genera Chaetoceros, Coscinodiscus, Skeletonema, and Thalassiosira. Our meta-analysis shows that these 86 estuarine-coastal sites function as diatom-producing systems, the timing of that production varies widely, and that bloom frequency is not associated with environmental factors measured in monitoring programs. We end with a perspective on the limitations of conclusions derived from meta-analyses of phytoplankton time series, and the grand challenges remaining to understand the wide range of bloom patterns and

  4. An Empirical Investigation of Individual Differences in Time to Learn

    Science.gov (United States)

    Anderson, Lorin W.

    1976-01-01

    Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)

  5. Algal Bloom: Boon or Bane?

    Digital Repository Service at National Institute of Oceanography (India)

    LokaBharathi, P.A.

    Algal blooms occur in response to nutrient deplete or replete conditions. Nitrogen fixing forms proliferate under oligotrophic conditions when nutrient levels are low. Replete conditions in response to upwelling creates the most biologically...

  6. OSU MODIS FLH Bloom Product

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Two bloom products were developed for the Oregon coast based on the observed change between running 8-day composite chlorophyll-a (CHL) and fluorescence line-height...

  7. Effect of Zeolite Treatment on the Blooming Behavior of Paraffin Wax in Natural Rubber Composites

    Directory of Open Access Journals (Sweden)

    Bryan B. Pajarito

    2016-06-01

    Full Text Available The blooming behavior of paraffin wax in natural rubber (NR composites was studied as function of zeolite treatment. Three types of zeolite treatment were treated as factors: acid activation using hydrochloric acid (HCl solution, ion exchange using tetradecyldimethyl amine (TDA chloride salt, and organic modification using glycerol monostearate (GMS. The zeolite was treated according to a 23 full factorial design of experiment. Attenuated total reflectance – Fourier transform infrared (ATR-FTIR spectroscopy was used to characterize the chemical structure of treated zeolite. Treated zeolite was applied as filler to NR composites deliberately compounded with high amount of paraff in wax. The amount of bloomed wax in surface of NR composite sheets was monitored with time at 50oC. Results show the bloom amount to be linear with the square root of time. NR composites reinforced with untreated, acid-activated, and ion-exchanged zeolite fillers indicate reduction in wax blooming as compared to unfilled NR. The bloom rate (slope and initial bloom (y-intercept were determined from the experimental plots. Analysis of variance (ANOVA shows the bloom rate to be signif icantly increased when zeolite fillers are treated with GMS. Meanwhile, initial bloom was significantly enhanced when zeolite fillers are treated with TDA chloride salt and GMS. The significant increase in bloom rate and initial bloom can be attributed to the softening of the NR matrix at high amounts of TDA chloride salt and GMS.

  8. Time factor in e-learning and assessment

    OpenAIRE

    Romero Velasco, Margarida

    2010-01-01

    Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.

  9. Blended learning pedagogy: the time is now!

    Science.gov (United States)

    Pizzi, Michael A

    2014-07-01

    Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.

  10. Learning to stay ahead of time

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Raffnsøe, Sverre

    2014-01-01

    In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

  11. Precarious Learning and Labour in Financialized Times

    Directory of Open Access Journals (Sweden)

    Jamie Magnusson

    2013-07-01

    Full Text Available Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession, particularly among youth, indigenous, working class, and racialized women. Presently there is surprisingly little discussion on the relevance of financialization for adult educators. Transnational resistances organizing against neoliberal restructuring, austerity policies, and debt crises are emerging at the same time that massive investments are being made into homeland security and the carceral state. This paper opens up discussion on the implications of financialized times for educators, and develops an analytic framework for examining how these global realities are best addressed at local sites of adult and higher education.

  12. Distribution and recurrence of phytoplankton blooms around South Georgia, Southern Ocean

    Directory of Open Access Journals (Sweden)

    I. Borrione

    2013-01-01

    Full Text Available South Georgia phytoplankton blooms are amongst the largest of the Southern Ocean and are associated with a rich ecosystem and strong atmospheric carbon drawdown. Both aspects depend on the intensity of blooms, but also on their regularity. Here we use data from 12 yr of SeaWiFS (Sea-viewing Wide Field-of-view Sensor ocean colour imagery and calculate the frequency of bloom occurrence (FBO to re-examine spatial and temporal bloom distributions. We find that upstream of the island and outside the borders of the Georgia Basin, blooms occurred in less than 4 out of the 12 yr (FBO < 4. In contrast, FBO was mostly greater than 8 downstream of the island, i.e., to the north and northwest, and in places equal to 12, indicating that blooms occurred every year. The typical bloom area, defined as the region where blooms occurred in at least 8 out of the 12 yr, covers the entire Georgia Basin and the northern shelf of the island. The time series of surface chlorophyll a (Chl a concentrations averaged over the typical bloom area shows that phytoplankton blooms occurred in every year between September 1997 and September 2010, and that Chl a values followed a clear seasonal cycle, with concentration peaks around December followed in many years by a second peak during late austral summer or early autumn, suggesting a bi-modal bloom pattern. The bloom regularity we describe here is in contrast with results of Park et al. (2010 who used a significantly different study area including regions that almost never exhibit bloom conditions.

  13. Identification of Phytoplankton Blooms under the Index of Inherent Optical Properties (IOP Index in Optically Complex Waters

    Directory of Open Access Journals (Sweden)

    Jesús A. Aguilar-Maldonado

    2018-01-01

    Full Text Available Phytoplankton blooms are sporadic events in time and are isolated in space. This complex phenomenon is produced by a variety of both natural and anthropogenic causes. Early detection of this phenomenon, as well as the classification of a water body under conditions of bloom or non-bloom, remains an unresolved problem. This research proposes the use of Inherent Optical Properties (IOPs in optically complex waters to detect the bloom or non-bloom state of the phytoplankton community. An IOP index is calculated from the absorption coefficients of the colored dissolved organic matter (CDOM, the phytoplankton (phy and the detritus (d, using the wavelength (λ 443 nm. The effectiveness of this index is tested in five bloom events in different places and with different characteristics from Mexican seas: 1. Dzilam (Caribbean Sea, Atlantic Ocean, a diatom bloom (Rhizosolenia hebetata; 2. Holbox (Caribbean Sea, Atlantic Ocean, a mixed bloom of dinoflagellates (Scrippsiella sp. and diatoms (Chaetoceros sp.; 3. Campeche Bay in the Gulf of Mexico (Atlantic Ocean, a bloom of dinoflagellates (Karenia brevis; 4. Upper Gulf of California (UGC (Pacific Ocean, a diatom bloom (Coscinodiscus and Pseudo-nitzschia and 5. Todos Santos Bay, Ensenada (Pacific Ocean, a dinoflagellate bloom (Lingulodinium polyedrum. The diversity of sites show that the IOP index is a suitable method to determine the phytoplankton bloom conditions.

  14. In-Time On-Place Learning

    Science.gov (United States)

    Bauters, Merja; Purma, Jukka; Leinonen, Teemu

    2014-01-01

    The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…

  15. An algorithm for learning real-time automata

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe

  16. Allan Bloom, America, and Education.

    Science.gov (United States)

    West, Thomas

    2000-01-01

    Refutes the claims of Allan Bloom that the source of the problem with today's universities is modern philosophy, that the writings and ideas of Hobbes and Locke planted the seeds of relativism in American culture, and that the cure is Great Books education. Suggests instead that America's founding principles are the only solution to the failure of…

  17. Service discovery using Bloom filters

    NARCIS (Netherlands)

    Goering, P.T.H.; Heijenk, Geert; Lelieveldt, B.P.F.; Haverkort, Boudewijn R.H.M.; de Laat, C.T.A.M.; Heijnsdijk, J.W.J.

    A protocol to perform service discovery in adhoc networks is introduced in this paper. Attenuated Bloom filters are used to distribute services to nodes in the neighborhood and thus enable local service discovery. The protocol has been implemented in a discrete event simulator to investigate the

  18. Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task

    Directory of Open Access Journals (Sweden)

    Margarida Romero

    2011-06-01

    Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.

  19. Unsteady thermal blooming of intense laser beams

    Science.gov (United States)

    Ulrich, J. T.; Ulrich, P. B.

    1980-01-01

    A four dimensional (three space plus time) computer program has been written to compute the nonlinear heating of a gas by an intense laser beam. Unsteady, transient cases are capable of solution and no assumption of a steady state need be made. The transient results are shown to asymptotically approach the steady-state results calculated by the standard three dimensional thermal blooming computer codes. The report discusses the physics of the laser-absorber interaction, the numerical approximation used, and comparisons with experimental data. A flowchart is supplied in the appendix to the report.

  20. Working and Learning in Times of Uncertainty

    DEFF Research Database (Denmark)

    This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways...... do individuals, organisations, political actors and education systems respond to these challenges? Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school......). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making....

  1. Monitoring of ocean surface algal blooms in coastal and oceanic waters around India.

    Digital Repository Service at National Institute of Oceanography (India)

    Tholkapiyan, M.; Shanmugam, P.; Suresh, T.

    of the MODIS-Aqua-derived OSABI (ocean surface algal bloom index) and its seasonal composite images report new information and comprehensive pictures of these blooms and their evolution stages in a wide variety of events occurred at different times of the years...

  2. Learning Styles of Medical Students Change in Relation to Time

    Science.gov (United States)

    Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

    2011-01-01

    The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

  3. Bloom 認知與技能教育目標應用於快速數位教材製作流程與設計研究 The Development of Procedure and Design Principle of Using Rapid E-learning Tools in Bloom’s Taxonomy

    Directory of Open Access Journals (Sweden)

    David Tawei Ku

    2011-07-01

    Full Text Available 近年來,數位學習(E-learning在網路應用領域中快速成長,成為主流。然而,許多組織顧及節省人力資源與縮短時程的因素,利用簡易的教材製作工具來解決問題,因此「快速數位學習」順應產生,將教材製作的時間縮短,其中教學設計仍是開發過程中重要的一環。 有鑑於此,本研究目的旨在經由文獻分析之歸納,提出 Bloom 教育目標分類與教學設計原則、快速數位學習教材設計內涵與快速學習製作工具分析,藉此發展出快速數位學習教材製作流程。另透過教材開發者問卷調查與專家訪談,進行流程的修正,建立流程之可行性與實用度。本研究結果為縮短教學設計分析階段之流程,將教學目標分析與資源分析整合,直接運用 Bloom 教育目標將教材內容分類後,找出合適的呈現方式,再根據每項工具之特性與專有的功能,挑選出適用的工具來進行教材製作。本流程的建置能協助學科內容專家加快尋找製作工具之作業,提供快速數位學習教材製作之應用與參考。As the development of internet, the speed of information update is faster than ever. E-learning has rapidly become the mainstream for the corporation training and instruction. In order to reduce the cost of human resources and time spending, “repaid e-learning” has been developed for the simple and easy to produce training materials. Therefore, this study reviews the related literature and analyzed the Bloom’s taxonomy, rapid e-learning instructional design, and rapid e-learning production tools to develop a procedure and design principle of rapid e-learning materials by using rapid e-learning tools. The procedure is revised and established the feasibility via the questionnaires and interview. It is expected that the results can provide great support and information for the future design and development of rapid e-learning training materials.

  4. Real-time Color Codes for Assessing Learning Process

    OpenAIRE

    Dzelzkalēja, L; Kapenieks, J

    2016-01-01

    Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

  5. Dinoflagellate blooms in upwelling systems: Seeding, variability, and contrasts with diatom bloom behaviour

    Science.gov (United States)

    Smayda, T. J.; Trainer, V. L.

    2010-04-01

    The influence of diatom bloom behaviour, dinoflagellate life cycles, propagule type and upwelling bloom cycles on the seeding of dinoflagellate blooms in eastern boundary current upwelling systems is evaluated. Winter-spring diatom bloom behaviour is contrasted with upwelling bloom behaviour because their phenology impacts dinoflagellate blooms. The winter-spring diatom bloom is usually sustained, whereas the classical upwelling diatom bloom occurs as a series of separate, recurrent mini-blooms intercalated by upwelling-relaxation periods, during which dinoflagellates often bloom. Four sequential wind-regulated phases characterize upwelling cycles, with each phase having different effects on diatom and dinoflagellate bloom behaviour: bloom “spin up”, bloom maximum, bloom “spin down”, and upwelling relaxation. The spin up - bloom maximum is the period of heightened diatom growth; the spin down - upwelling-relaxation phases are the periods when dinoflagellates often bloom. The duration, intensity and ratio of the upwelling and relaxation periods making up upwelling cycles determine the potential for dinoflagellate blooms to develop within a given upwelling cycle and prior to the subsequent “spin up” of upwelling that favours diatom blooms. Upwelling diatoms and meroplanktonic dinoflagellates have three types of propagules available to seed blooms: vegetative cells, resting cells and resting cysts. However, most upwelling dinoflagellates are holoplanktonic, which indicates that the capacity to form resting cysts is not an absolute requirement for growth and survival in upwelling systems. The long-term (decadal) gaps in bloom behaviour of Gymnodinium catenatum and Lingulodinium polyedrum, and the unpredictable bloom behaviour of dinoflagellates generally, are examined from the perspective of seeding strategies. Mismatches between observed and expected in situ bloom behaviour and resting cyst dynamics are common among upwelling dinoflagellates. This

  6. A novel earth observation based ecological indicator for cyanobacterial blooms

    Science.gov (United States)

    Anttila, Saku; Fleming-Lehtinen, Vivi; Attila, Jenni; Junttila, Sofia; Alasalmi, Hanna; Hällfors, Heidi; Kervinen, Mikko; Koponen, Sampsa

    2018-02-01

    Cyanobacteria form spectacular mass occurrences almost annually in the Baltic Sea. These harmful algal blooms are the most visible consequences of marine eutrophication, driven by a surplus of nutrients from anthropogenic sources and internal processes of the ecosystem. We present a novel Cyanobacterial Bloom Indicator (CyaBI) targeted for the ecosystem assessment of eutrophication in marine areas. The method measures the current cyanobacterial bloom situation (an average condition of recent 5 years) and compares this to the estimated target level for 'good environmental status' (GES). The current status is derived with an index combining indicative bloom event variables. As such we used seasonal information from the duration, volume and severity of algal blooms derived from earth observation (EO) data. The target level for GES was set by using a remote sensing based data set named Fraction with Cyanobacterial Accumulations (FCA; Kahru & Elmgren, 2014) covering years 1979-2014. Here a shift-detection algorithm for time series was applied to detect time-periods in the FCA data where the level of blooms remained low several consecutive years. The average conditions from these time periods were transformed into respective CyaBI target values to represent target level for GES. The indicator is shown to pass the three critical factors set for marine indicator development, namely it measures the current status accurately, the target setting can be scientifically proven and it can be connected to the ecosystem management goal. An advantage of the CyaBI method is that it's not restricted to the data used in the development work, but can be complemented, or fully applied, by using different types of data sources providing information on cyanobacterial accumulations.

  7. Harmful Freshwater Algal Blooms, With an Emphasis on Cyanobacteria

    Directory of Open Access Journals (Sweden)

    Hans W. Paerl

    2001-01-01

    zooplankton to further up the food chain. Both N2- and non-N2-fixing genera participate in mutualistic and symbiotic associations with microorganisms, higher plants, and animals. These associations appear to be of great benefit to their survival and periodic dominance. In this review, we address the ecological impacts and environmental controls of harmful blooms, with an emphasis on the ecology, physiology, and management of cyanobacterial bloom taxa. Combinations of physical, chemical, and biotic features of natural waters function in a synergistic fashion to determine the sensitivity of water bodies. In waters susceptible to blooms, human activities in water- and airsheds have been linked to the extent and magnitudes of blooms. Control and management of cyanobacterial and other phytoplankton blooms invariably includes nutrient input constraints, most often focused on nitrogen (N and/or phosphorus (P. The types and amount of nutrient input constraints depend on hydrologic, climatic, geographic, and geologic factors, which interact with anthropogenic and natural nutrient input regimes. While single nutrient input constraints may be effective in some water bodies, dual N and P input reductions are usually required for effective long-term control and management of harmful blooms. In some systems where hydrologic manipulations (i.e., plentiful water supplies are possible, reducing the water residence time by enhanced flushing and artificial mixing (in conjunction with nutrient input constraints can be particularly effective alternatives. Implications of various management strategies, based on combined ecophysiological and environmental considerations, are discussed.

  8. Hydrodynamic control of microphytoplankton bloom in a coastal sea

    Science.gov (United States)

    Murty, K. Narasimha; Sarma, Nittala S.; Pandi, Sudarsana Rao; Chiranjeevulu, Gundala; Kiran, Rayaprolu; Muralikrishna, R.

    2017-08-01

    The influence of hydrodynamics on phytoplankton bloom occurrence/formation has not been adequately reported. Here, we document diurnal observations in the tropical Bay of Bengal's mid-western shelf region which reveal microphytoplankton cell density maxima in association with neap tide many times more than what could be accounted for by solar insolation and nutrient levels. When in summer, phytoplankton cells were abundant and the cell density of Guinardia delicatula reached critical value by tide caused zonation, aggregation happened to an intense bloom. Mucilaginous exudates from the alga due to heat and silicate stress likely promoted and stable water column and weak winds left undisturbed, the transient bloom. The phytoplankton aggregates have implication as food resource in the benthic region implying higher fishery potential, in carbon dioxide sequestration (carbon burial) and in efforts towards improving remote sensing algorithms for chlorophyll in the coastal region.

  9. Time will tell: The role of mobile learning analytics in self-regulated learning

    NARCIS (Netherlands)

    Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus

    2015-01-01

    This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of

  10. Margalef's mandala and phytoplankton bloom strategies

    Science.gov (United States)

    Wyatt, Timothy

    2014-03-01

    Margalef's mandala maps phytoplankton species into a phase space defined by turbulence (A) and nutrient concentrations (Ni); these are the hard axes. The permutations of high and low A and high and low Ni divide the space into four domains. Soft axes indicate some ecological dynamics. A main sequence shows the normal course of phytoplankton succession; the r-K axis of MacArthur and Wilson runs parallel to it. An alternative successional sequence leads to the low A-high Ni domain into which many red tide species are mapped. Astronomical and biological time are implicit. A mathematical transformation of the mandala (rotation) links it to the classical bloom models of Sverdrup (time) and Kierstead and Slobodkin (space). Both rarity and the propensity to form red tides are considered to be species characters, meaning that maximum population abundance can be a target of natural selection. Equally, both the unpredictable appearance of bloom species and their short-lived appearances may be species characters. There may be a correlation too between these features and long-lived dormant stages in the life-cycle; then the vegetative planktonic phase is the 'weak link' in the life-cycle. Red tides are thus due to species which have evolved suites of traits which result in specific demographic strategies.

  11. Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay

    Science.gov (United States)

    Anderson, Clarissa R.; Sapiano, Mathew R. P.; Prasad, M. Bala Krishna; Long, Wen; Tango, Peter J.; Brown, Christopher W.; Murtugudde, Raghu

    2010-11-01

    Harmful algal blooms are now recognized as a significant threat to the Chesapeake Bay as they can severely compromise the economic viability of important recreational and commercial fisheries in the largest estuary of the United States. This study describes the development of empirical models for the potentially domoic acid-producing Pseudo-nitzschia species complex present in the Bay, developed from a 22-year time series of cell abundance and concurrent measurements of hydrographic and chemical properties. Using a logistic Generalized Linear Model (GLM) approach, model parameters and performance were compared over a range of Pseudo-nitzschia bloom thresholds relevant to toxin production by different species. Small-threshold blooms (≥10 cells mL -1) are explained by time of year, location, and variability in surface values of phosphate, temperature, nitrate plus nitrite, and freshwater discharge. Medium- (100 cells mL -1) to large- threshold (1000 cells mL -1) blooms are further explained by salinity, silicic acid, dissolved organic carbon, and light attenuation (Secchi) depth. These predictors are similar to other models for Pseudo-nitzschia blooms on the west coast, suggesting commonalities across ecosystems. Hindcasts of bloom probabilities at a 19% bloom prediction point yield a Heidke Skill Score of ~53%, a Probability of Detection ˜ 75%, a False Alarm Ratio of ˜ 52%, and a Probability of False Detection ˜9%. The implication of possible future changes in Baywide nutrient stoichiometry on Pseudo-nitzschia blooms is discussed.

  12. Seasonal cooling and blooming in tropical oceans

    Science.gov (United States)

    Longhurst, Alan

    1993-11-01

    The relative importance of tropical pelagic algal blooms in not yet fully appreciated and the way they are induced not well understood. The tropical Atlantic supports pelagic blooms together equivalent to the North Atlantic spring bloom. These blooms are driven by thermocline tilting, curl of wind stress and eddy upwelling as the ocean responds to intensified basin-scale winds in boreal summer. The dimensions of the Pacific Ocean are such that seasonal thermocline tilting does not occur, and nutrient conditions are such that tilting might not induce bloom, in any case. Divergence at the equator is a separate process that strengthens the Atlantic bloom, is more prominent in the eastern Pacific, and in the Indian Ocean induces a bloom only in the western part of the ocean. Where western jet currents are retroflected from the coast off Somalia and Brazil, eddy upwelling induces prominent blooms. In the eastward flow of the northern equatorial countercurrents, positive wind curl stress induces Ekman pumping and the induction of algal blooms aligned with the currents. Some apparent algal bloom, such as that seen frequently in CZCS images westwards from Senegal, must be due to interference from airborne dust.

  13. Evaluating ILI Advanced Series through Bloom's Revised Taxonomy

    OpenAIRE

    MAHDIPOUR, Nasim; SADEGHI, Bahador

    2015-01-01

    Abstract. This study investigated Iran Language Institute Advanced Series in terms of learning objectives based on Bloom's Revised Taxonomy. It examined the cognitive, affective and psychomotor domains to see how the critical thinking skills are used and to what extent these books are different from each other. For these purposes, the frequencies, percentages and Standard Residual were analyzed. Results revealed that the lower-order cognitive skills (i.e. remembering, understanding and applyi...

  14. Precarious Learning and Labour in Financialized Times

    Science.gov (United States)

    Magnusson, Jamie

    2013-01-01

    Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession,…

  15. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  16. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  17. The impacts of a massive harmful algal bloom along the US west coast in 2015

    Science.gov (United States)

    Kudela, R. M.; Trainer, V. L.; McCabe, R. M.; Hickey, B. M.; Negrey, K.

    2016-02-01

    In 2015, a massive bloom of the marine diatom Pseudo-nitzschia, stretching from southern California to southern Alaska, resulted in significant impacts to coastal resources and marine life. This bloom was first detected in early May 2015, when Washington closed its scheduled razor clam digs on coastal beaches. It is the largest and longest-lasting bloom in at least the past 15 years, and concentrations of domoic acid in seawater, some forage fish, and crab samples have been among the highest ever reported for this region. By mid-May, domoic acid concentrations in Monterey Bay, California were 10 to 30 times the level that would be considered high for a normal Pseudo-nitzschia bloom. Impacts to coastal communities and marine life include shellfish and Dungeness crab closures in multiple states, impacting commercial, recreational and subsistence harvesters, anchovy and sardine fishery health advisories in some areas of California, and sea lion strandings in California and Washington. Other marine mammal and bird mortalities have been reported in multiple states, and domoic acid poisoning is a suspected cause. In addition to the spatial extent and toxicity, the bloom has also lasted for many months (ongoing as of September 2015). While the exact causes of the bloom's severity and early onset are not yet known, unusually warm surface water in the Pacific Ocean may be a contributing factor. Here we present an overview of the bloom dynamics and impacts, and preliminary analysis about the bloom initiation and relationship to unusual ocean conditions in 2014-2015.

  18. Gulf of Maine Harmful Algal Bloom in summer 2005 - Part 1: In Situ Observations of Coastal Hydrography and Circulation

    OpenAIRE

    He, Ruoying; McGillicuddy, Dennis J.

    2008-01-01

    An extensive Alexandrium fundyense bloom occurred along the coast of the Gulf of Maine in late spring and early summer, 2005. To understand the physical aspects of bloom?s initiation and development, in-situ observations from both a gulf-wide ship survey and the coastal observing network were used to characterize coastal circulation and hydrography during that time period. Comparisons between these in-situ observations and their respective long term means revealed anomalous ocean conditions d...

  19. Active learning reduces annotation time for clinical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Time to rethink the neural mechanisms of learning and memory.

    Science.gov (United States)

    Gallistel, Charles R; Balsam, Peter D

    2014-02-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Fast lossless compression via cascading Bloom filters.

    Science.gov (United States)

    Rozov, Roye; Shamir, Ron; Halperin, Eran

    2014-01-01

    Data from large Next Generation Sequencing (NGS) experiments present challenges both in terms of costs associated with storage and in time required for file transfer. It is sometimes possible to store only a summary relevant to particular applications, but generally it is desirable to keep all information needed to revisit experimental results in the future. Thus, the need for efficient lossless compression methods for NGS reads arises. It has been shown that NGS-specific compression schemes can improve results over generic compression methods, such as the Lempel-Ziv algorithm, Burrows-Wheeler transform, or Arithmetic Coding. When a reference genome is available, effective compression can be achieved by first aligning the reads to the reference genome, and then encoding each read using the alignment position combined with the differences in the read relative to the reference. These reference-based methods have been shown to compress better than reference-free schemes, but the alignment step they require demands several hours of CPU time on a typical dataset, whereas reference-free methods can usually compress in minutes. We present a new approach that achieves highly efficient compression by using a reference genome, but completely circumvents the need for alignment, affording a great reduction in the time needed to compress. In contrast to reference-based methods that first align reads to the genome, we hash all reads into Bloom filters to encode, and decode by querying the same Bloom filters using read-length subsequences of the reference genome. Further compression is achieved by using a cascade of such filters. Our method, called BARCODE, runs an order of magnitude faster than reference-based methods, while compressing an order of magnitude better than reference-free methods, over a broad range of sequencing coverage. In high coverage (50-100 fold), compared to the best tested compressors, BARCODE saves 80-90% of the running time while only increasing space

  2. Professional Learning in Part-time University Study

    DEFF Research Database (Denmark)

    Rasmussen, Palle

    2007-01-01

    The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...

  3. Radiologists' preferences for just-in-time learning.

    Science.gov (United States)

    Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P

    2006-09-01

    Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

  4. Antioxidative response of the three macrophytes Ceratophyllum demersum, Egeria densa, and Hydrilla verticillata to a time dependent exposure of cell-free crude extracts containing three microcystins from cyanobacterial blooms of Lake Amatitlán, Guatemala.

    Science.gov (United States)

    Romero-Oliva, Claudia Suseth; Contardo-Jara, Valeska; Pflugmacher, Stephan

    2015-06-01

    Microcystins (MCs) produced by cyanobacteria in natural environments are a potential risk to the integrity of ecosystems. In this study, the effects of cyanobacterial cell-free crude extracts from a Microcystis aeruginosa bloom containing three MC-congeners MC-LR, -RR, and -YR at environmental relevant concentrations of 49.3±2.9, 49.8±5.9, and 6.9±3.8μg/L, respectively, were evaluated on Ceratophyllum demersum (L.), Egeria densa (Planch.), and Hydrilla verticillata (L.f.). Effects on photosynthetic pigments (total chlorophyll (chl), chl a, chl b, and carotenoids), enzymatic defense led by catalase (CAT), peroxidase (POD) and glutathione reductase (GR), and biotransformation enzyme glutathione S-transferase (GST) were measured after 1, 4, and 8h and after 1, 3, 7, and 14 days of exposure. Results show that in all exposed macrophytes, photosynthetic pigments were negatively affected. While chl a and total chl decreased with increasing exposure time, a parallel increase in chl b was observed after 8h. Concomitant increase of ∼5, 16, and 34% of antioxidant carotenoid concentration in exposed C. demersum, E. densa, and H. verticillata, respectively, was also displayed. Enzymatic antioxidant defense systems in all exposed macrophytes were initiated within the first hour of exposure. In exposed E. densa, highest values of CAT and GR activities were observed after 4 and 8h, respectively, while in exposed H. verticillata highest value of POD activity was observed after 8h. An early induction with a significant increase of biotransformation enzyme GST was observed in E. densa after 4h and in C. demersum and H. verticillata after 8h. These results are the first to show rapid induction of stress and further possible MC biotransformation (based on the activation of GST enzymatic activity included in MC metabolization during the biotransformation mechanism) in macrophytes exposed to crude extract containing a mixture of MCs. Copyright © 2015 Elsevier B.V. All rights

  5. Mastery Learning and the Decreasing Variability Hypothesis.

    Science.gov (United States)

    Livingston, Jennifer A.; Gentile, J. Ronald

    1996-01-01

    This report results from studies that tested two variations of Bloom's decreasing variability hypothesis using performance on successive units of achievement in four graduate classrooms that used mastery learning procedures. Data do not support the decreasing variability hypothesis; rather, they show no change over time. (SM)

  6. Georges Bank: a leaky incubator of Alexandrium fundyense blooms.

    Science.gov (United States)

    McGillicuddy, D J; Townsend, D W; Keafer, B A; Thomas, M A; Anderson, D M

    2014-05-01

    A series of oceanographic surveys on Georges Bank document variability of populations of the toxic dinoflagellate Alexandrium fundyense on time scales ranging from synoptic to seasonal to interannual. Blooms of A. fundyense on Georges Bank can reach concentrations on the order of 10 4 cells l -1 , and are generally bank-wide in extent. Georges Bank populations of A. fundyense appear to be quasi-independent of those in the adjacent coastal Gulf of Maine, insofar as they occupy a hydrographic niche that is colder and saltier than their coastal counterparts. In contrast to coastal populations that rely on abundant resting cysts for bloom initiation, very few cysts are present in the sediments on Georges Bank. Bloom dynamics must therefore be largely controlled by the balance between growth and mortality processes, which are at present largely unknown for this population. Based on correlations between cell abundance and nutrient distributions, ammonium appears to be an important source of nitrogen for A. fundyense blooms on Georges Bank.

  7. Increasing instruction time in school does increase learning

    DEFF Research Database (Denmark)

    Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink

    2016-01-01

    Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....

  8. Summer heatwaves promote blooms of harmful cyanobacteria

    NARCIS (Netherlands)

    K.D Joehnk; J. Huisman; J. Sharples; B.P. Sommeijer (Ben); P.M. Visser (Petra); J.M. Stroom

    2008-01-01

    htmlabstractDense surface blooms of toxic cyanobacteria in eutrophic lakes may lead to mass mortalities of fish and birds, and provide a serious health threat for cattle, pets, and humans. It has been argued that global warming may increase the incidence of harmful algal blooms. Here, we report on a

  9. Summer heatwaves promote blooms of harmful cyanobacteria

    NARCIS (Netherlands)

    Jöhnk, K.D.; Huisman, J.; Sharples, J.; Sommeijer, B.; Visser, P.M.; Stroom, J.M.

    2008-01-01

    Dense surface blooms of toxic cyanobacteria in eutrophic lakes may lead to mass mortalities of fish and birds, and provide a serious health threat for cattle, pets, and humans. It has been argued that global warming may increase the incidence of harmful algal blooms. Here, we report on a lake

  10. Distance Sensitive Bloom Filters Without False Negatives

    DEFF Research Database (Denmark)

    Goswami, Mayank; Pagh, Rasmus; Silvestri, Francesco

    2017-01-01

    A Bloom filter is a widely used data-structure for representing a set S and answering queries of the form “Is x in S?”. By allowing some false positive answers (saying ‘yes’ when the answer is in fact ‘no’) Bloom filters use space significantly below what is required for storing S. In the distanc...

  11. Heterosigma bloom and associated fish kill

    Science.gov (United States)

    Hershberger, P.K.; Rensel, J.E.; Postel, J.R.; Taub, F.B.

    1997-01-01

    A bloom of the harmful marine phytoplankton, Heterosigma carterae occurred in upper Case Inlet, south Puget Sound, Washington in late September, 1994, correlating with the presence of at least 35 dead salmon. This marks the first time that this alga has been closely correlated with a wild fish kill; in the past it was thought to be associated with kills of penned fish at fish farms only. We were informed of the presence of a possible harmful algal bloom and dead salinois Ilear the town of Allyn on 27 September and a team was formed to investigate. We arrived at the Allyn waterfront at 17:30 hours the same day. Prior to our arrival, state agency personnel walked approximatcly two miles of shoreline from the powerlines north of the dock, to the mouth of Sherwood Creek and conducted the only official count of dead fish present along the shore consisting of 12 coho salmon (Oncorhynchus kisutch), 11 chum salmon (Oncorhynchus keta), 12 chinook salmon (Oncorhynchus tschawytscha), one flat fish, and one sculpin on the morning of 9/27. Since previous harmful blooms of Heterosigma have resultedin the majority of net penreared salmon sinking to the bottom of pens, and only approximately two miles of shoreline were sampled, it is suspected that many more exposed fish may have succumbed than were counted. Witnesses who explored the east side of the bay reported seeing many dead salmon there as well, but no counts were made. State agency personnel who observed the fish kill reported seeing “dying fish coming to the beach, gulping at the surface, trying to get out of the water” Scavengers were seen consuming the salmon carcasses; these included two harbor seals, a house cat, and Hymenopteran insects. None suffered any noticeable acute ill effects. Although precise cause of death has not been ascertained, visual inspection of the reproductive organs from a deceased male chum salmon found on the shore at Allyn confirmed that the fish was not yet reproductively mature and

  12. Online Quiz Time Limits and Learning Outcomes in Economics

    Science.gov (United States)

    Evans, Brent; Culp, Robert

    2015-01-01

    In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…

  13. Time for Learning: An Exploratory Analysis of NAEP Data

    Science.gov (United States)

    Ginsburg, Alan; Chudowsky, Naomi

    2012-01-01

    This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…

  14. A Model for Learning Over Time: The Big Picture

    Science.gov (United States)

    Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

    2002-01-01

    Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

  15. Timing of quizzes during learning: Effects on motivation and retention.

    Science.gov (United States)

    Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

    2017-06-01

    This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  16. Human learning: Power laws or multiple characteristic time scales?

    Directory of Open Access Journals (Sweden)

    Gottfried Mayer-Kress

    2006-09-01

    Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

  17. Under Sea Ice phytoplankton bloom detection and contamination in Antarctica

    Science.gov (United States)

    Zeng, C.; Zeng, T.; Xu, H.

    2017-12-01

    Previous researches reported compelling sea ice phytoplankton bloom in Arctic, while seldom reports studied about Antarctic. Here, lab experiment showed sea ice increased the visible light albedo of the water leaving radiance. Even a new formed sea ice of 10cm thickness increased water leaving radiance up to 4 times of its original bare water. Given that phytoplankton preferred growing and accumulating under the sea ice with thickness of 10cm-1m, our results showed that the changing rate of OC4 estimated [Chl-a] varied from 0.01-0.5mg/m3 to 0.2-0.3mg/m3, if the water covered by 10cm sea ice. Going further, varying thickness of sea ice modulated the changing rate of estimating [Chl-a] non-linearly, thus current routine OC4 model cannot estimate under sea ice [Chl-a] appropriately. Besides, marginal sea ice zone has a large amount of mixture regions containing sea ice, water and snow, where is favorable for phytoplankton. We applied 6S model to estimate the sea ice/snow contamination on sub-pixel water leaving radiance of 4.25km spatial resolution ocean color products. Results showed that sea ice/snow scale effectiveness overestimated [Chl-a] concentration based on routine band ratio OC4 model, which contamination increased with the rising fraction of sea ice/snow within one pixel. Finally, we analyzed the under sea ice bloom in Antarctica based on the [Chl-a] concentration trends during 21 days after sea ice retreating. Regardless of those overestimation caused by sea ice/snow sub scale contamination, we still did not see significant under sea ice blooms in Antarctica in 2012-2017 compared with Arctic. This research found that Southern Ocean is not favorable for under sea ice blooms and the phytoplankton bloom preferred to occur in at least 3 weeks after sea ice retreating.

  18. State of knowledge and concerns on cyanobacterial blooms and cyanotoxins.

    Science.gov (United States)

    Merel, Sylvain; Walker, David; Chicana, Ruth; Snyder, Shane; Baurès, Estelle; Thomas, Olivier

    2013-09-01

    Cyanobacteria are ubiquitous microorganisms considered as important contributors to the formation of Earth's atmosphere and nitrogen fixation. However, they are also frequently associated with toxic blooms. Indeed, the wide range of hepatotoxins, neurotoxins and dermatotoxins synthesized by these bacteria is a growing environmental and public health concern. This paper provides a state of the art on the occurrence and management of harmful cyanobacterial blooms in surface and drinking water, including economic impacts and research needs. Cyanobacterial blooms usually occur according to a combination of environmental factors e.g., nutrient concentration, water temperature, light intensity, salinity, water movement, stagnation and residence time, as well as several other variables. These environmental variables, in turn, have promoted the evolution and biosynthesis of strain-specific, gene-controlled metabolites (cyanotoxins) that are often harmful to aquatic and terrestrial life, including humans. Cyanotoxins are primarily produced intracellularly during the exponential growth phase. Release of toxins into water can occur during cell death or senescence but can also be due to evolutionary-derived or environmentally-mediated circumstances such as allelopathy or relatively sudden nutrient limitation. Consequently, when cyanobacterial blooms occur in drinking water resources, treatment has to remove both cyanobacteria (avoiding cell lysis and subsequent toxin release) and aqueous cyanotoxins previously released. Cells are usually removed with limited lysis by physical processes such as clarification or membrane filtration. However, aqueous toxins are usually removed by both physical retention, through adsorption on activated carbon or reverse osmosis, and chemical oxidation, through ozonation or chlorination. While the efficient oxidation of the more common cyanotoxins (microcystin, cylindrospermopsin, anatoxin and saxitoxin) has been extensively reported, the chemical

  19. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman

    2014-01-01

    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  20. Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory.

    Science.gov (United States)

    Chen, Qiuwen; Rui, Han; Li, Weifeng; Zhang, Yanhui

    2014-06-01

    Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004-2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial-temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial-temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. E-learning for Part-Time Medical Studies

    Directory of Open Access Journals (Sweden)

    Półjanowicz Wiesław

    2016-12-01

    Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.

  2. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  3. Importance of a winter dinoflagellate-microflagellate bloom in the Patuxent River estuary

    Science.gov (United States)

    Sellner, K. G.; Lacouture, R. V.; Cibik, S. J.; Brindley, A.; Brownlee, S. G.

    1991-01-01

    A dense bloom of Katodinium rotundatum was observed in the Patuxent River estuary from December to February 1989. The dinoflagellate dominated phytoplankton densities reaching 10 8 cells l -1 and contributed up to 1900 μgC l -1 in near-surface depths. The bloom maintained a distinct patch extending over 10-25 km of the estuary or approximately one-third to one-half of the total estuary (salinities from 5-13 ppt) and was restricted to regions immediately upriver of the transition between the shallow upriver (3-4 m) and deeper lower estuary (10 m). Daily measurements collected in the primary bloom area at the same time each day in the study area indicated 80- and 120-fold variations in chlorophyll and cell densities from day to day. Densities of potential grazers in the region were high with rotifers, primarily Synchaeta baltica, reaching densities of 1000 l -1 in early winter, and the copepod Eurytemora affinis reaching levels exceeding 1·15 × 10 5 m -3 in February. Estimates of grazing pressure by these planktonic herbivores indicated substantial grazing losses for the bloom, with up to 67% of bloom biomass consumed day -1 in February. Nutrient concentrations and ratios of N/P during the bloom suggested potentially N-limited conditions; bloom demise was coincident with a shift to high N/P ratios and high river flows. These data as well as other historical data suggest that dinoflagellate blooms in the lower Patuxent River estuary could be the primary source of carbon to the system during the winter and supply a large reservoir of labile organic matter to planktonic secondary producers prior to annual spring diatom blooms in the region.

  4. Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

    Science.gov (United States)

    Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

    2012-01-01

    This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

  5. Learning and Teaching Problems in Part-Time Higher Education.

    Science.gov (United States)

    Trotman-Dickenson, D. I.

    1988-01-01

    Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)

  6. Dynamics of a cyanobacterial bloom in a hypereutrophic reservoir ...

    African Journals Online (AJOL)

    Blooming and non-blooming periods between 2004 and 2006 in a hypereutrophic reservoir, where cyanobacterial blooms have previously been reported to be permanent, presented an opportunity to characterise factors that may favour cyanobacterial dominance. As a bloom developed in May 2004, a shift to dominance by ...

  7. Reduced river discharge intensifies phytoplankton bloom in Godavari estuary, India

    Digital Repository Service at National Institute of Oceanography (India)

    Acharyya, T.; Sarma, V.V.S.S.; Sridevi, B.; Venkataramana, V.; Bharathi, M.D.; Naidu, S.A.; Kumar, B.S.K.; Prasad, V.R.; Bandyopadhyay, D.; Reddy, N.P.C.; DileepKumar, M.

    et al., 2009; Sarma et al., 2011), behaviour of different 3 elements (Sarma et al., 1993) and heavy metals (Somayajulu et al., 1993).Virtually no systematic studies have been undertaken so far in these estuaries focussing on spatial and... their class specific marker pigment fucoxanthin (Jeffry et al., 1997; Mantoura and Llewellyn, 1983; Wright et al., 1991). To confirm the influence of SPM and flushing time on phytoplankton bloom a laboratory- based incubation experiment was conducted...

  8. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  9. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  10. Climate Adaptation and Harmful Algal Blooms

    Science.gov (United States)

    EPA supports local, state and tribal efforts to maintain water quality. A key element of its efforts is to reduce excess nutrient pollution and the resulting adverse impacts, including harmful algal blooms.

  11. Detecting the Killer Toxin (Harmful Algal Blooms)

    International Nuclear Information System (INIS)

    Quevenco, Rodolfo

    2011-01-01

    IAEA is stepping up efforts to help countries understand the phenomenon and use more reliable methods for early detection and monitoring so as to limit harmful algal blooms (HABs) adverse effects on coastal communities everywhere.

  12. Satellite Evidence that E. huxleyi Phytoplankton Blooms Weaken Marine Carbon Sinks

    Science.gov (United States)

    Kondrik, D. V.; Pozdnyakov, D. V.; Johannessen, O. M.

    2018-01-01

    Phytoplankton blooms of the coccolithophore Emiliania huxleyi are known to produce CO2, causing less uptake of atmospheric CO2 by the ocean, but a global assessment of this phenomenon has so far not been quantified. Therefore, here we quantify the increase in CO2 partial pressure (ΔpCO2) at the ocean surface within E. huxleyi blooms for polar and subpolar seas using an 18 year ocean color time series (1998-2015). When normalized to pCO2 in the absence of bloom, the mean and maximum ΔpCO2 values within the bloom areas varied between 21.0%-43.3% and 31.6%-62.5%, respectively. These results might have appreciable implications for climatology, marine chemistry, and ecology.

  13. Investigation of the 2006 Alexandrium fundyense Bloom in the Gulf of Maine: In situ Observations and Numerical Modeling.

    Science.gov (United States)

    Li, Yizhen; He, Ruoying; McGillicuddy, Dennis J; Anderson, Donald M; Keafer, Bruce A

    2009-09-30

    In situ observations and a coupled bio-physical model were used to study the germination, initiation, and development of the Gulf of Maine (GOM) Alexandrium fundyense bloom in 2006. Hydrographic measurements and comparisons with GOM climatology indicate that 2006 was a year with normal coastal water temperature, salinity, current and river runoff conditions. A. fundyense cyst abundance in bottom sediments preceding the 2006 bloom was at a moderate level compared to other recent annual cyst survey data. We used the coupled bio-physical model to hindcast coastal circulation and A. fundyense cell concentrations. Field data including water temperature, salinity, velocity time series and surface A. fundyense cell concentration maps were applied to gauge the model's fidelity. The coupled model is capable of reproducing the hydrodynamics and the temporal and spatial distributions of A. fundyense cell concentration reasonably well. Model hindcast solutions were further used to diagnose physical and biological factors controlling the bloom dynamics. Surface wind fields modulated the bloom's horizontal and vertical distribution. The initial cyst distribution was found to be the dominant factor affecting the severity and the interannual variability of the A. fundyense bloom. Initial cyst abundance for the 2006 bloom was about 50% of that prior to the 2005 bloom. As the result, the time-averaged gulf-wide cell concentration in 2006 was also only about 60% of that in 2005. In addition, weaker alongshore currents and episodic upwelling-favorable winds in 2006 reduced the spatial extent of the bloom as compared with 2005.

  14. The sedimentary record of dinoflagellate cysts: looking back into the future of phytoplankton blooms

    Directory of Open Access Journals (Sweden)

    Barrie Dale

    2001-12-01

    Full Text Available Marine systems are not as well understood as terrestrial systems, and there is still a great need for more primary observations, in the tradition of the old-time naturalists, before newer methods such as molecular genetics and modeling can be fully utilized. The scientific process whereby the smaller, detailed building blocks of observation are ultimately linked towards better understanding natural systems is illustrated from my own career experience, especially with regard to the dinoflagellates and plankton blooms. Some dinoflagellates produce a fossilizable resting stage (cyst in their life cycle, and dinoflagellate cysts have become one of the most important groups of microfossils used in geological exploration (e.g. oil and gas. This has stimulated both paleontological and biological research producing detailed building blocks of information, currently scattered throughout the respective literature. Here, I attempt to bring together the present day perspective, from biology, with the past, from paleontology, as the most comprehensive basis for future work on the group. This shows the cysts to be the critical link needed for focusing future molecular genetics studies towards a more verifiable view of evolutionary pathways, and it also suggests new integrated methods for studying past, present, and future blooms. The large, rapidly growing field of harmful algal bloom studies is producing many different building blocks, but plankton blooms as episodic phenomena are still poorly understood. This is largely due to the general lack of long-term datasets allowing identification of the changing environmental factors that permit certain species to bloom at unpredictable intervals of time. Cysts in sediments are useful environmental indicators today, e.g. reflecting aspects of climate and pollution, and provide information directly relevant to some dinoflagellate blooms. They therefore may be used for obtaining retrospective information from the

  15. Polysynchronous: Dialogic Construction of Time in Online Learning

    Science.gov (United States)

    Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim

    2014-01-01

    Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…

  16. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  17. Instructional Advice, Time Advice and Learning Questions in Computer Simulations

    Science.gov (United States)

    Rey, Gunter Daniel

    2010-01-01

    Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

  18. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  19. Online gaming for learning optimal team strategies in real time

    Science.gov (United States)

    Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

    2010-04-01

    This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

  20. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  1. Blooming reduces the antioxidant capacity of dark chocolate in rats without lowering its capacity to improve lipid profiles.

    Science.gov (United States)

    Shadwell, Naomi; Villalobos, Fatima; Kern, Mark; Hong, Mee Young

    2013-05-01

    Dark chocolate contains high levels of antioxidants which are linked to a reduced risk of cardiovascular disease. Chocolate blooming occurs after exposure to high temperatures. Although bloomed chocolate is safe for human consumption, it is not known whether or not the biological function of bloomed chocolate is affected. We hypothesized that bloomed chocolate would reduce the antioxidant potential and lipid-lowering properties of chocolate through altered expression of related genes. Thirty Sprague-Dawley rats were divided into 3 groups and fed either the control (CON), regular dark chocolate (RDC), or bloomed dark chocolate (BDC) diet. After 3 weeks, serum lipid levels and antioxidant capacity were measured. Hepatic expression of key genes was determined by real time polymerase chain reaction (PCR). Sensory characteristics of bloomed versus regular chocolate were assessed in 28 semi-trained panelists. Rats fed RDC exhibited greater serum antioxidant capacities compared to the CON (P chocolate compared to bloomed chocolate (P chocolate, these results suggest that bloomed dark chocolate yields similarly beneficial effects on most blood lipid parameters or biomarkers. However, regular dark chocolate may be more beneficial for the improvement of antioxidant status and modulation of gene expression involved in lipid metabolism and promoted greater sensory ratings. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. Consortial brown tide - picocyanobacteria blooms in Guantánamo Bay, Cuba.

    Science.gov (United States)

    Hall, Nathan S; Litaker, R Wayne; Kenworthy, W Judson; Vandersea, Mark W; Sunda, William G; Reid, James P; Slone, Daniel H; Butler, Susan

    2018-03-01

    A brown tide bloom of Aureoumbra lagunensis developed in Guantánamo Bay, Cuba during a period of drought in 2013 that followed heavy winds and rainfall from Hurricane Sandy in late October 2012. Based on satellite images and water turbidity measurements, the bloom appeared to initiate in January 2013. The causative species (A. lagunensis) was confirmed by microscopic observation, and pigment and genetic analyses of bloom samples collected on May 28 of that year. During that time, A. lagunensis reached concentrations of 900,000 cells ml -1 (28 ppm by biovolume) in the middle portion of the Bay. Samples could not be collected from the northern (Cuban) half of the Bay because of political considerations. Subsequent sampling of the southern half of the Bay in November 2013, April 2014, and October 2014 showed persistent lower concentrations of A. lagunensis, with dominance shifting to the cyanobacterium Synechococcus (up to 33 ppm in April), an algal group that comprised a minor bloom component on May 28. Thus, unlike the brown tide bloom in Laguna Madre, which lasted 8 years, the bloom in Guantánamo Bay was short-lived, much like recent blooms in the Indian River, Florida. Although hypersaline conditions have been linked to brown tide development in the lagoons of Texas and Florida, observed euhaline conditions in Guantánamo Bay (salinity 35-36) indicate that strong hypersalinity is not a requirement for A. lagunensis bloom formation. Microzooplankton biomass dominated by ciliates was high during the observed peak of the brown tide, and ciliate abundance was high compared to other systems not impacted by brown tide. Preferential grazing by zooplankton on non-brown tide species, as shown in A. lagunensis blooms in Texas and Florida, may have been a factor in the development of the Cuban brown tide bloom. However, subsequent selection of microzooplankton capable of utilizing A. lagunensis as a primary food source may have contributed to the short-lived duration

  3. Consortial brown tide − picocyanobacteria blooms in Guantánamo Bay, Cuba

    Science.gov (United States)

    Hall, Nathan S; Litaker, R. Wayne; Kenworthy, W. Judson; Vandersea, Mark W.; Sunda, William G.; Reid, James P.; Slone, Daniel H.; Butler, Susan M.

    2018-01-01

    A brown tide bloom of Aureoumbra lagunensis developed in Guantánamo Bay, Cuba during a period of drought in 2013 that followed heavy winds and rainfall from Hurricane Sandy in late October 2012. Based on satellite images and water turbidity measurements, the bloom appeared to initiate in January 2013. The causative species (A. lagunensis) was confirmed by microscopic observation, and pigment and genetic analyses of bloom samples collected on May 28 of that year. During that time, A. lagunensis reached concentrations of 900,000 cells ml−1 (28 ppm by biovolume) in the middle portion of the Bay. Samples could not be collected from the northern (Cuban) half of the Bay because of political considerations. Subsequent sampling of the southern half of the Bay in November 2013, April 2014, and October 2014 showed persistent lower concentrations of A. lagunensis, with dominance shifting to the cyanobacterium Synechococcus (up to 33 ppm in April), an algal group that comprised a minor bloom component on May 28. Thus, unlike the brown tide bloom in Laguna Madre, which lasted 8 years, the bloom in Guantánamo Bay was short-lived, much like recent blooms in the Indian River, Florida. Although hypersaline conditions have been linked to brown tide development in the lagoons of Texas and Florida, observed euhaline conditions in Guantánamo Bay (salinity 35–36) indicate that strong hypersalinity is not a requirement for A. lagunensis bloom formation. Microzooplankton biomass dominated by ciliates was high during the observed peak of the brown tide, and ciliate abundance was high compared to other systems not impacted by brown tide. Preferential grazing by zooplankton on non-brown tide species, as shown in A. lagunensis blooms in Texas and Florida, may have been a factor in the development of the Cuban brown tide bloom. However, subsequent selection of microzooplankton capable of utilizing A. lagunensis as a primary food source may have contributed to the

  4. Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness

    Directory of Open Access Journals (Sweden)

    Garzia Maeca

    2016-06-01

    Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.

  5. A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

    Directory of Open Access Journals (Sweden)

    Lun Zhai

    2014-01-01

    Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

  6. Factors affecting outbreaks of Cochlodinium polykrikoides blooms in coastal areas of Korea

    International Nuclear Information System (INIS)

    Lee, Young Sik . E-mail leeyodk@hanmail.net; Lee, Sang Yong

    2006-01-01

    We evaluated the causes of the first outbreak of Cochlodinium polykrikoides blooms in Narodo and the Southern coast of Namhaedo on the South Sea, as well as the outbreak of C. polykrikoides blooms in the East Sea and around Wando. From the results of AGP tests using diverse seawater types, we identified seawaters in which C. polykrikoides grow well and those in which they do not, depending on the sampling time and location. The reason for C. polykrikoides blooms initially occurring in Narodo, Namhaedo, and Gujaedo seems to be because the seawater that promotes the growth of C. polykrikoides is transported to the areas of primary generation, such as these three areas, by the influence of the Tsushima Warm Current. The reason that C. polykrikoides blooms occur in the coastal area of Wando and the East Sea is because after the seawater promoting the growth of C. polykrikoides is transported to these areas, the amount of sun radiation increases, and abundant nutrients flow in from heavy rains, resulting in mass propagation of C. polykrikoides. The origin of the seawater that promotes the growth of C. polykrikoides is assumed to be the southern section of the southern coastal area of Narodo, Namhaedo, and Gujaedo, in which C. polykrikoides blooms were initially discovered. The components of the f/2 medium (N, P, Fe, Mn, Co, Cu, Zn, Mo, B12, biotin, thiamine) do not seem to trigger the occurrence of C. polykrikoides blooms

  7. Potentially harmful microalgae and algal blooms in a eutrophic estuary in Turkey

    Directory of Open Access Journals (Sweden)

    S. TAS

    2015-07-01

    Full Text Available Distribution of potentially harmful microalgae and algal blooms were investigated at monthly and weekly time scales between October 2009 and September 2010 in the Golden Horn, a eutrophic estuary in the Sea of Marmara (Turkey. Several physical and chemical parameters were analysed together with phytoplankton composition and abundance. A total number of 23 potentially harmful and/or bloom-forming microalgae (14 dinoflagellates, 4 diatoms and 5 phytoflagellates were identified throughout this study period, of which nine taxa have been confirmed to be toxic elsewhere in the world. Most harmful species and algal blooms were observed in late spring and summer particularly in the middle and upper estuaries, and nine taxa formed dense and successive algal blooms causing water discoloration. Nutrient concentrations increased significantly from the lower to the upper estuary. Additionally, high organic matter loads in the upper estuary could also have benefited by mixotrophic species. The increasing number of potentially harmful and bloom-forming species and algal blooms indicated that the GHE is a potential risk area for future HABs.

  8. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  9. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  10. Modelling the production of dimethylsulfide during a phytoplankton bloom

    Science.gov (United States)

    Gabric, Albert; Murray, Nicholas; Stone, Lewi; Kohl, Manfred

    1993-12-01

    Dimethylsulfide (DMS) is an important sulfur-containing atmospheric trace gas of marine biogenic origin. DMS emitted from the oceans may be a precursor of tropospheric aerosols and cloud condensation nuclei (CCN), thereby affecting the Earth's radiative balance and possibly constituting a negative feedback to global warming, although this hypothesis is still somewhat controversial. The revised conceptual model of the marine pelagic food web gives a central role to planktonic bacteria. Recent experiments have shown that consumption of dissolved DMS by microbial metabolism may be more important than atmospheric exchange in controlling its concentration in surface waters and hence its ventilation to the atmosphere. In this paper we investigate the effect of the marine food web on cycling of dissolved DMS in surface waters during a phytoplankton bloom episode. A nitrogen-based flow network simulation model has been used to analyze the relative importance of the various biological and chemical processes involved. The model predictions suggest that the concentration of DMS in marine surface waters is indeed governed by bacterial metabolism. Environmental factors that affect the bacterial compartment are thus likely to have a relatively large influence on dissolved DMS concentrations. The ecological succession is particularly sensitive to the ratio of phytoplankton to bacterial nutrient uptake rates as well the interaction between herbivore food chain and the microbial loop. Importantly for the design of field studies, the model predicts that peak DMS concentrations are achieved during the decline of the phytoplankton bloom with a typical time lag between peak DMS and peak phytoplankton biomass of 1 to 2 days. Significantly, the model predicts a relatively high DMS concentration persisting after the phytoplankton bloom due to excretion from large protozoa and zooplankton, which may be an additional explanation for the lack of correlation between DMS and chlorophyll a

  11. Weather during bloom affects pollination and yield of highbush blueberry.

    Science.gov (United States)

    Tuell, Julianna K; Isaacs, Rufus

    2010-06-01

    Weather plays an important role in spring-blooming fruit crops due to the combined effects on bee activity, flower opening, pollen germination, and fertilization. To determine the effects of weather on highbush blueberry, Vaccinium corymbosum L., productivity, we monitored bee activity and compared fruit set, weight, and seed number in a field stocked with honey bees, Apis mellifera L., and common eastern bumble bees, Bombus impatiens (Cresson). Flowers were subjected to one of five treatments during bloom: enclosed, open, open during poor weather only, open during good weather only, or open during poor and good weather. Fewer bees of all types were observed foraging and fewer pollen foragers returned to colonies during poor weather than during good weather. There were also changes in foraging community composition: honey bees dominated during good weather, whereas bumble bees dominated during poor weather. Berries from flowers exposed only during poor weather had higher fruit set in 1 yr and higher berry weight in the other year compared with enclosed clusters. In both years, clusters exposed only during good weather had > 5 times as many mature seeds, weighed twice as much, and had double the fruit set of those not exposed. No significant increase over flowers exposed during good weather was observed when clusters were exposed during good and poor weather. Our results are discussed in terms of the role of weather during bloom on the contribution of bees adapted to foraging during cool conditions.

  12. Finite time convergent learning law for continuous neural networks.

    Science.gov (United States)

    Chairez, Isaac

    2014-02-01

    This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Influence of learning strategy on response time during complex value-based learning and choice.

    Directory of Open Access Journals (Sweden)

    Shiva Farashahi

    Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

  14. Real-time individualized training vectors for experiential learning.

    Energy Technology Data Exchange (ETDEWEB)

    Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan

    2011-01-01

    Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

  15. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  16. Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

    Directory of Open Access Journals (Sweden)

    Chunmei Ma

    2016-01-01

    Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

  17. Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

    Science.gov (United States)

    Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

    2018-05-01

    One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

  18. Fixation and escape times in stochastic game learning

    International Nuclear Information System (INIS)

    Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

    2012-01-01

    Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

  19. The 3 R's of Learning Time: Rethink, Reshape, Reclaim

    Science.gov (United States)

    Sackey, Shera Carter

    2012-01-01

    The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

  20. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    Science.gov (United States)

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  1. Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

    Science.gov (United States)

    Cheron, G; Márquez-Ruiz, J; Dan, B

    2016-04-01

    The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

  2. A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

    Science.gov (United States)

    Tatkovic, Nevenka; Ruzic, Maja

    2004-01-01

    The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

  3. The extended Kalman filter for forecast of algal bloom dynamics.

    Science.gov (United States)

    Mao, J Q; Lee, Joseph H W; Choi, K W

    2009-09-01

    A deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Deltat=1 day, 2h, 1h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 microg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r=0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r=0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the

  4. Overlay improvements using a real time machine learning algorithm

    Science.gov (United States)

    Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

    2014-04-01

    While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

  5. Algal blooms: a perspective from the coasts of India

    Digital Repository Service at National Institute of Oceanography (India)

    DeSilva, M.S.; Anil, A.C.; Naik, R.K.; DeCosta, P.M.

    Algal blooms have been documented along the west and east coasts of India. A review of bloom occurrences in Indian waters from 1908 to 2009 points out that a total of 101 cases have been reported. A comparison of the bloom cases reported before...

  6. Designing Internet Learning for Novice Users -Paper Based on a Action Research Project In India

    DEFF Research Database (Denmark)

    Purushothaman, Aparna

    2012-01-01

    The paper centre on an Action Research project undertaken in India for enabling the female students empowered through Internet use. The paper will discuss the design elements of Internet training for the first time users with limited Internet access based on Blooms Digital Taxonomy of Learning...... Domains.The paper also illustrates the identity formation of students, through learning to use Internet, using wengers social theory of learning with the empirical data....

  7. A winter dinoflagellate bloom drives high rates of primary production in a Patagonian fjord ecosystem

    Science.gov (United States)

    Montero, P.; Pérez-Santos, I.; Daneri, G.; Gutiérrez, M. H.; Igor, G.; Seguel, R.; Purdie, D.; Crawford, D. W.

    2017-12-01

    A dense winter bloom of the dinoflagellate Heterocapsa triquetra was observed at a fixed station (44°35.3‧S; 72°43.6‧W) in the Puyuhuapi Fjord in Chilean Patagonia during July 2015. H. triquetra dominated the phytoplankton community in the surface waters between 2 and 15 m (13-58 × 109 cell m-2), with abundances some 3 to 15 times higher than the total abundance of the diatom assemblage, which was dominated by Skeletonema spp. The high abundance of dinoflagellates was reflected in high rates of gross primary production (GPP; 0.6-1.6 g C m-2 d-1) and chlorophyll-a concentration (Chl-a; 70-199.2 mg m-2) that are comparable to levels reported in spring diatom blooms in similar Patagonian fjords. We identify the main forcing factors behind a pulse of organic matter production during the non-productive winter season, and test the hypothesis that low irradiance levels are a key factor limiting phytoplankton blooms and subsequent productivity during winter. Principal Component Analysis (PCA) indicated that GPP rates were significantly correlated (r = -0.8, p bloom. The bloom occurred under low surface irradiance levels characteristic of austral winter and was accompanied by strong northern winds, associated with the passage of a low-pressure system, and a water column dominated by double diffusive layering. To our knowledge, this is the first report of a dense dinoflagellate bloom during deep austral winter in a Patagonian fjord, and our data challenge the paradigm of light limitation as a factor controlling phytoplankton blooms in this region in winter.

  8. Dissection of Microbial Community Functions during a Cyanobacterial Bloom in the Baltic Sea via Metatranscriptomics

    Directory of Open Access Journals (Sweden)

    Carlo Berg

    2018-02-01

    Full Text Available Marine and brackish surface waters are highly dynamic habitats that undergo repeated seasonal variations in microbial community composition and function throughout time. While succession of the various microbial groups has been well investigated, little is known about the underlying gene-expression of the microbial community. We investigated microbial interactions via metatranscriptomics over a spring to fall seasonal cycle in the brackish Baltic Sea surface waters, a temperate brackish water ecosystem periodically promoting massive cyanobacterial blooms, which have implications for primary production, nutrient cycling, and expansion of hypoxic zones. Network analysis of the gene expression of all microbes from 0.22 to 200 μm in size and of the major taxonomic groups dissected the seasonal cycle into four components that comprised genes peaking during different periods of the bloom. Photoautotrophic nitrogen-fixing Cyanobacteria displayed the highest connectivity among the microbes, in contrast to chemoautotrophic ammonia-oxidizing Thaumarchaeota, while heterotrophs dominated connectivity among pre- and post-bloom peaking genes. The network was also composed of distinct functional connectivities, with an early season balance between carbon metabolism and ATP synthesis shifting to a dominance of ATP synthesis during the bloom, while carbon degradation, specifically through the glyoxylate shunt, characterized the post-bloom period, driven by Alphaproteobacteria as well as by Gammaproteobacteria of the SAR86 and SAR92 clusters. Our study stresses the exceptionally strong biotic driving force executed by cyanobacterial blooms on associated microbial communities in the Baltic Sea and highlights the impact cyanobacterial blooms have on functional microbial community composition.

  9. Cyanobacterial blooms in lake Atitlan, Guatemala

    Czech Academy of Sciences Publication Activity Database

    Rejmánková, E.; Komárek, Jiří; Dix, M.; Komárková, Jaroslava; Girón, N.

    2011-01-01

    Roč. 41, č. 4 (2011), s. 296-302 ISSN 0075-9511 Institutional research plan: CEZ:AV0Z60050516; CEZ:AV0Z60170517 Keywords : water blooms * plancton * Guatemala Subject RIV: EH - Ecology, Behaviour Impact factor: 1.527, year: 2011

  10. Plankton bloom controlled by horizontal stirring

    Science.gov (United States)

    McKiver, W.; Neufeld, Z.; Scheuring, I.

    2009-10-01

    Here we show a simple mechanism in which changes in the rate of horizontal stirring by mesoscale ocean eddies can trigger or suppress plankton blooms and can lead to an abrupt change in the average plankton density. We consider a single species phytoplankton model with logistic growth, grazing and a spatially non-uniform carrying capacity. The local dynamics have multiple steady states for some values of the carrying capacity that can lead to localized blooms as fluid moves across the regions with different properties. We show that for this model even small changes in the ratio of biological timescales relative to the flow timescales can greatly enhance or reduce the global plankton productivity. Thus, this may be a possible mechanism in which changes in horizontal mixing can trigger plankton blooms or cause regime shifts in some oceanic regions. Comparison between the spatially distributed model and Lagrangian simulations considering temporal fluctuations along fluid trajectories, demonstrates that small scale transport processes also play an important role in the development of plankton blooms with a significant influence on global biomass.

  11. The Negative Impact of Community Stressors on Learning Time: Examining Inequalities between California High Schools

    Science.gov (United States)

    Mirra, Nicole; Rogers, John

    2015-01-01

    Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…

  12. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index SABI)

    OpenAIRE

    Alawadi, Fahad

    2010-01-01

    Quantifying ocean colour properties has evolved over the past two decades from being able to merely detect their biological activity to the ability to estimate chlorophyll concentration using optical satellite sensors like MODIS and MERIS. The production of chlorophyll spatial distribution maps is a good indicator of plankton biomass (primary production) and is useful for the tracing of oceanographic currents, jets and blooms, including harmful algal blooms (HABs). Depending on the type of HA...

  13. What time is it? Deep learning approaches for circadian rhythms.

    Science.gov (United States)

    Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

    2016-06-15

    Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  14. Forecasting air quality time series using deep learning.

    Science.gov (United States)

    Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

    2018-04-13

    This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

  15. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  16. Evidence for an alternation strategy in time-place learning.

    Science.gov (United States)

    Pizzo, Matthew J; Crystal, Jonathon D

    2004-11-30

    Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

  17. A new insight into black blooms: Synergies between optical and chemical factors

    Science.gov (United States)

    Duan, Hongtao; Loiselle, Steven Arthur; Li, Zuochen; Shen, Qiushi; Du, Yingxun; Ma, Ronghua

    2016-06-01

    Black blooms have been associated with fish-kills and the loss of benthic fauna as well as closure of potable water supplies. Their frequency and duration has increased in recent decades in rivers, inland lakes and reservoirs, and has often been associated with the decay and release of organic matter (planktonic algae, aquatic macrophytes, sediment release, etc.). However, the interactions between microbial, chemical, hydrodynamic and optical conditions necessary for black blooms are poorly understood. The present study combines field investigations and laboratory mesocosm studies to show that black blooms are caused by a combination of high CDOM (chromophoric dissolved organic matter) absorption, the formation of CDOM-Fe complexes and low backscattering. Mesocosm experiments showed that black bloom conditions occur after 4 days, with a significant increase in the concentrations of Fe2+ and ∑S2-. Total absorption (excluding absorption due to water) at 440 nm increased by 30% over this time to 7.3 m-1. In addition, the relative contribution of CDOM absorption to the non-water total absorption increased from 18% to 50%. Regression analyses between chemical and bio-optical data in both field and mesocosm experiments indicated that the concentrations of Fe2+ co-varied positively with CDOM absorption ag(440) (R2 > 0.70), and the specific CDOM absorption (ag(440)/DOC). Conditions that favored the development of black blooms were elevated algal or macrophyte biomass and limited water column mixing.

  18. Cyanobacteria of the 2016 Lake Okeechobee and Okeechobee Waterway harmful algal bloom

    Science.gov (United States)

    Rosen, Barry H.; Davis, Timothy W.; Gobler, Christopher J.; Kramer, Benjamin J.; Loftin, Keith A.

    2017-05-31

    The Lake Okeechobee and the Okeechobee Waterway (Lake Okeechobee, the St. Lucie Canal and River, and the Caloosahatchee River) experienced an extensive harmful algal bloom within Lake Okeechobee, the St. Lucie Canal and River and the Caloosahatchee River in 2016. In addition to the very visible bloom of the cyanobacterium Microcystis aeruginosa, several other cyanobacteria were present. These other species were less conspicuous; however, they have the potential to produce a variety of cyanotoxins, including anatoxins, cylindrospermopsins, and saxitoxins, in addition to the microcystins commonly associated with Microcystis. Some of these species were found before, during, and 2 weeks after the large Microcystis bloom and could provide a better understanding of bloom dynamics and succession. This report provides photographic documentation and taxonomic assessment of the cyanobacteria present from Lake Okeechobee and the Caloosahatchee River and St. Lucie Canal, with samples collected June 1st from the Caloosahatchee River and Lake Okeechobee and in July from the St. Lucie Canal. The majority of the images were of live organisms, allowing their natural complement of pigmentation to be captured. The report provides a digital image-based taxonomic record of the Lake Okeechobee and the Okeechobee Waterway microscopic flora. It is anticipated that these images will facilitate current and future studies on this system, such as understanding the timing of cyanobacteria blooms and their potential toxin production.

  19. Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

    Science.gov (United States)

    Lekki, John; Leshkevich, George; Nguyen, Quang-Viet; Flatico, Joseph; Prokop, Norman; Kojima, Jun; Anderson, Robert; Demers, James; Krasowski, Michael

    2007-01-01

    NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

  20. Subsurface phytoplankton blooms fuel pelagic production in the North Sea

    DEFF Research Database (Denmark)

    Richardson, Kathrine; Visser, Andre; Pedersen, Flemming

    2000-01-01

    The seasonal phytoplankton biomass distribution pattern in stratified temperate marine waters is traditionally depicted as consisting of spring and autumn blooms. The energy source supporting pelagic summer production is believed to be the spring bloom. However, the spring bloom disappears...... relatively quickly from the water column and a large proportion of the material sedimenting to the bottom following the spring bloom is often comprised of intact phytoplankton cells. Thus, it is easy to argue that the spring bloom is fueling the energy demands of the benthos, but more difficult to argue...... convincingly that energy fixed during the spring bloom is fueling the pelagic production occurring during summer months. We argue here that periodic phytoplankton blooms are occurring during the summer in the North Sea at depths of >25 m and that the accumulated new production [sensu (Dugdale and Goering...

  1. Jellyfish blooms in China: Dominant species, causes and consequences

    International Nuclear Information System (INIS)

    Dong Zhijun; Liu Dongyan; Keesing, John K.

    2010-01-01

    Three jellyfish species, Aurelia aurita, Cyanea nozakii and Nemopilema nomurai, form large blooms in Chinese seas. We report on the distribution and increasing incidence of jellyfish blooms and their consequences in Chinese coastal seas and analyze their relationship to anthropogenically derived changes to the environment in order to determine the possible causes. A. aurita, C. nozakii and N. nomurai form blooms in the temperate Chinese seas including the northern East China Sea, Yellow Sea and Bohai Sea. N. nomurai forms offshore blooms while the other two species bloom mainly in inshore areas. Eutrophication, overfishing, habitat modification for aquaculture and climate change are all possible contributory factors facilitating plausible mechanisms for the proliferation of jellyfish blooms. In the absence of improvement in coastal marine ecosystem health, jellyfish blooms could be sustained and may even spread from the locations in which they now occur.

  2. The Impact of Students' Temporal Perspectives on Time-on-Task and Learning Performance in Game Based Learning

    Science.gov (United States)

    Romero, Margarida; Usart, Mireia

    2013-01-01

    The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…

  3. Wind-driven marine phytoplank blooms: Satellite observation and analysis

    Science.gov (United States)

    Tang, DanLing

    2016-07-01

    Algal bloom is defined as a rapid increase or accumulation in biomass in an aquatic system. It not only can increase the primary production but also could result in negative ecological consequence, e.g.,Harmful Algal Blooms (HABs). According to the classic theory for the formation of algal blooms "critical depth" and "eutrophication", oligotrophic sea area is usually difficult to form a large area of algal blooms, and actuallythe traditional observation is only sporadic capture to the existence of algal blooms.Taking full advantage of multiple data of satellite remote sensing , this study introduces "Wind-driven algal blooms in open oceans: observation and mechanisms" It explained except classic coastal Ekman transport, the wind through a variety of mechanisms affecting the formation of algal blooms. Proposed a conceptual model of "Strong wind -upwelling-nutrient-phytoplankton blooms" in Western South China Sea (SCS) to assess role of wind-induced advection transport in phytoplankton bloom formation. It illustrates the nutrient resources that support long-term offshore phytoplankton blooms in the western SCS; (2)Proposal of the theory that "typhoons cause vertical mixing, induce phytoplankton blooms", and quantify their important contribution to marine primary production; Proposal a new ecological index for typhoon. Proposed remote sensing inversion models. (3)Finding of the spatial and temporaldistributions pattern of harmful algal bloom (HAB)and species variations of HAB in the South Yellow Sea and East China Sea, and in the Pearl River estuary, and their oceanic dynamic mechanisms related with monsoon; The project developed new techniques and generated new knowledge, which significantly improved understanding of the formation mechanisms of algal blooms. The proposed "wind-pump" mechanism integrates theoretical system combined "ocean dynamics, development of algal blooms, and impact on primary production", which will benefit fisheries management. These

  4. Learning the language of time: Children's acquisition of duration words.

    Science.gov (United States)

    Tillman, Katharine A; Barner, David

    2015-05-01

    Children use time words like minute and hour early in development, but take years to acquire their precise meanings. Here we investigate whether children assign meaning to these early usages, and if so, how. To do this, we test their interpretation of seven time words: second, minute, hour, day, week, month, and year. We find that preschoolers infer the orderings of time words (e.g., hour>minute), but have little to no knowledge of the absolute durations they encode. Knowledge of absolute duration is learned much later in development - many years after children first start using time words in speech - and in many children does not emerge until they have acquired formal definitions for the words. We conclude that associating words with the perception of duration does not come naturally to children, and that early intuitive meanings of time words are instead rooted in relative orderings, which children may infer from their use in speech. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Site fidelity by bees drives pollination facilitation in sequentially blooming plant species.

    Science.gov (United States)

    Ogilvie, Jane E; Thomson, James D

    2016-06-01

    Plant species can influence the pollination and reproductive success of coflowering neighbors that share pollinators. Because some individual pollinators habitually forage in particular areas, it is also possible that plant species could influence the pollination of neighbors that bloom later. When flowers of a preferred forage plant decline in an area, site-fidelity may cause individual flower feeders to stay in an area and switch plant species rather than search for preferred plants in a new location. A newly blooming plant species may quickly inherit a set of visitors from a prior plant species, and therefore experience higher pollination success than it would in an area where the first species never bloomed. To test this, we manipulated the placement and timing of two plant species, Delphinium barbeyi and later-blooming Gentiana parryi. We recorded the responses of individually marked bumble bee pollinators. About 63% of marked individuals returned repeatedly to the same areas to forage on Delphinium. When Delphinium was experimentally taken out of bloom, most of those site-faithful individuals (78%) stayed and switched to Gentiana. Consequently, Gentiana flowers received more visits in areas where Delphinium had previously flowered, compared to areas where Delphinium was still flowering or never occurred. Gentiana stigmas received more pollen in areas where Delphinium disappeared than where it never bloomed, indicating that Delphinium increases the pollination of Gentiana when they are separated in time. Overall, we show that individual bumble bees are often site-faithful, causing one plant species to increase the pollination of another even when separated in time, which is a novel mechanism of pollination facilitation.

  6. Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

    Directory of Open Access Journals (Sweden)

    David Afolabi

    2017-11-01

    Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting

  7. Siderophores: The special ingredient to cyanobacterial blooms

    Science.gov (United States)

    Du, Xue; Creed, Irena; Trick, Charles

    2013-04-01

    Freshwater lakes provide a number of significant ecological services including clean drinking water, habitat for aquatic biota, and economic benefits. The provision of these ecological services, as well as the health of these aquatic systems, is threatened by the excessive growth of algae, specifically, cyanobacteria. Historically, blooms have been linked to eutrophication but recent occurrences indicate that there are less dramatic changes that induce these blooms. Iron is an essential micronutrient required for specific essential metabolic pathways; however, the amount of biologically available iron in naturally occurring lake ranges from saturation to much lower than cell transport affinities. To assist in the modulation of iron availabilities, cyanobacteria in culture produce low molecular weight compounds that function in an iron binding and acquisition system; nevertheless, this has yet to be confirmed in naturally occurring lakes. This project explored the relationship of P, N and in particular, Fe, in the promotion of cyanobacteria harmful algal blooms in 30 natural freshwater lakes located in and around the Elk Island National Park, Alberta. It is hypothesized that cyanobacteria produce and utilize iron chelators called siderophores in low Fe and nitrogen (N) conditions, creating a competitive advantage over other algae in freshwater lakes. Lakes were selected to represent a range of iron availability to explore the nutrient composition of lakes that propagated cyanobacteria harmful algal blooms (cHABs) compared to lakes that did not. Lake water was analyzed for nutrients, microbial composition, siderophore concentration, and toxin concentration. Modifications were made to optimize the Czaky and Arnow tests for hydroxamate- and catecholate-type siderophores, respectively, for field conditions. Preliminary results indicate the presence of iron-binding ligands (0.11-2.34 mg/L) in freshwater lakes characterized by widely ranging Fe regimes (0.04-2.74 mg

  8. Using Online Lectures to Make Time for Active Learning

    Science.gov (United States)

    Prunuske, Amy J.; Batzli, Janet; Howell, Evelyn; Miller, Sarah

    2012-01-01

    To make time in class for group activities devoted to critical thinking, we integrated a series of short online lectures into the homework assignments of a large, introductory biology course at a research university. The majority of students viewed the online lectures before coming to class and reported that the online lectures helped them to complete the in-class activity and did not increase the amount of time they devoted to the course. In addition, students who viewed the online lecture performed better on clicker questions designed to test lower-order cognitive skills. The in-class activities then gave the students practice analyzing the information in groups and provided the instructor with feedback about the students’ understanding of the material. On the basis of the results of this study, we support creating hybrid course models that allow students to learn the fundamental information outside of class time, thereby creating time during the class period to be dedicated toward the conceptual understanding of the material. PMID:22714412

  9. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  10. Bloom 認知與技能教育目標應用於快速數位教材製作流程與設計研究 The Development of Procedure and Design Principle of Using Rapid E-learning Tools in Bloom’s Taxonomy

    OpenAIRE

    David Tawei Ku; Yung-Hsin Huang

    2011-01-01

    近年來,數位學習(E-learning)在網路應用領域中快速成長,成為主流。然而,許多組織顧及節省人力資源與縮短時程的因素,利用簡易的教材製作工具來解決問題,因此「快速數位學習」順應產生,將教材製作的時間縮短,其中教學設計仍是開發過程中重要的一環。 有鑑於此,本研究目的旨在經由文獻分析之歸納,提出 Bloom 教育目標分類與教學設計原則、快速數位學習教材設計內涵與快速學習製作工具分析,藉此發展出快速數位學習教材製作流程。另透過教材開發者問卷調查與專家訪談,進行流程的修正,建立流程之可行性與實用度。本研究結果為縮短教學設計分析階段之流程,將教學目標分析與資源分析整合,直接運用 Bloom 教育目標將教材內容分類後,找出合適的呈現方式,再根據每項工具之特性與專有的功能,挑選出適用的工具來進行教材製作。本流程的建置能協助學科內容專家加快尋找製作工具之作業,提供快速數位學習教材製作之應用與參考。As the development of internet, the speed of information update is faster than ever. E-learning ha...

  11. New Coccolithophore Bloom in Bering Sea

    Science.gov (United States)

    2002-01-01

    For the fourth year in a row it appears as if there is a bloom of coccolithophores-marine single-celled plants with calcite scales-in the Bering Sea off the coast of Alaska. Similar blooms were rare before 1997, but they have appeared every year since then. Scientists believe the coccolithophore blooms are the result of changing wind patterns in the region. Weaker than normal winds fail to mix the water of the Bering Sea, resulting in the growth of coccolithophores instead of other types of phytoplankton. Seabird populations have also been changing as a result of this climate change. The Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), flying aboard the OrbView-2 satellite, saw the coccolith-brightened waters of the Bering Sea in 1997, 1998, and 1999. The waters have looked fairly bright again this winter and spring, as seen in this SeaWiFS image acquired April 29, 2000. But scientists are unsure whether this year's phenomenon is caused by living coccolithophorids, re-suspended coccoliths, or something else. Like all phytoplankton, coccolithophores contain chlorophyll and have the tendency to multiply rapidly near the surface. Yet, in large numbers, coccolithophores periodically shed their tiny scales, called 'coccoliths,' by the bucketful into the surrounding waters. The calcium-rich coccoliths turn the normally dark water a bright, milky aquamarine, making coccolithophore blooms easy to spot in satellite imagery. The edge of the whitish cloud in the water seen in this image is roughly 50 kilometers off the West Coast of Alaska. For more information see: SeaWiFS home page Changing Currents Color the Bering Sea a New Shade of Blue Image courtesy SeaWiFS project

  12. EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

    Directory of Open Access Journals (Sweden)

    Abdel Rahman IBRAHIM SULEIMAN

    2014-07-01

    Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

  13. Assessment of Public Schools' Out-of-School Time Academic Support Programs with Participant-Oriented Evaluation

    Science.gov (United States)

    Berk, Saban

    2018-01-01

    Using the participants-oriented approach, this study evaluated public schools' out-of-school time academic support programs, corresponding to the corrective/enrichment stage of Bloom's Mastery Learning Model and offered outside formal education's weekday hours and on weekends. Study participants included 50 principals, 110 teachers, 170 students…

  14. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  15. Mental Time Travel, Memory and the Social Learning Strategies Tournament

    Science.gov (United States)

    Fogarty, L.; Rendell, L.; Laland, K. N.

    2012-01-01

    The social learning strategies tournament was an open computer-based tournament investigating the best way to learn in a changing environment. Here we present an analysis of the impact of memory on the ability of strategies entered into the social learning strategies tournament (Rendell, Boyd, et al., 2010) to modify their own behavior to suit a…

  16. Detection of Coccolithophore Blooms in Ocean Color Satellite Imagery: a Generalized Approach for Use with Multiple Sensors

    Science.gov (United States)

    Moore, Timothy; Dowell, Mark; Franz, Bryan A.

    2012-01-01

    A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cellsmL and 43,000 and 78,000 lithsmL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 19972010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemi- sphere with values of 2.00 106 km2 and 0.75 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75106 km2 is dominated by contributions from the Southern Ocean.

  17. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  18. A Lecture Supporting System Based on Real-Time Learning Analytics

    Science.gov (United States)

    Shimada, Atsushi; Konomi, Shin'ichi

    2017-01-01

    A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

  19. Transforming Bloom's Taxonomy into Classroom Practice: A Practical yet Comprehensive Approach to Promote Critical Reading and Student Participation

    Science.gov (United States)

    Mulcare, Daniel M.; Shwedel, Allan

    2017-01-01

    This article presents the Critical Reading Topics approach, a pedagogical method employed to promote deep thinking in a variety of politics courses. Derived from principles articulated in active learning, critical thinking, backward design, and flipped classroom literature, this method utilizes Bloom's Taxonomy as the scaffolding for students to…

  20. Alignment of Assessment Objectives with Instructional Objectives Using Revised Bloom's Taxonomy--The Case for Food Science and Technology Education

    Science.gov (United States)

    Jideani, V. A.; Jideani, I. A.

    2012-01-01

    Nine food science and technology (FST) subjects were assessed for alignment between the learning outcomes and assessment using revised Bloom's taxonomy (RBT) of cognitive knowledge. Conjoint analysis was used to estimate the utilities of the levels of cognitive, knowledge, and the attribute importance (cognitive process and knowledge dimension)…

  1. Unusual blooms of green Noctiluca miliaris (Dinophyceae) in the Arabian Sea during the winter monsoon

    Digital Repository Service at National Institute of Oceanography (India)

    Gomes, H.R.; Matondkar, S.G.P.; Parab, S.G.; Goes, J.I.; Pednekar, S.; Al-Azri, A.R.N.; Thoppil, P.G.

    A large-scale, ongoing study conducted by the National Institute of Oceanography, India, from 2003 onward in support of India’s ocean color program, has documented for the first time ever the appearance of extensive blooms of Noctiluca miliaris...

  2. Comparative Analysis of Flower Volatiles from Nine Citrus at Three Blooming Stages

    Directory of Open Access Journals (Sweden)

    Muhammad Azam

    2013-11-01

    Full Text Available Volatiles from flowers at three blooming stages of nine citrus cultivars were analyzed by headspace-solid phase microextraction (HS-SPME-GC-MS. Up to 110 volatiles were detected, with 42 tentatively identified from citrus flowers for the first time. Highest amounts of volatiles were present in fully opened flowers of most citrus, except for pomelos. All cultivars were characterized by a high percentage of either oxygenated monoterpenes or monoterpene hydrocarbons, and the presence of a high percentage of nitrogen containing compounds was also observed. Flower volatiles varied qualitatively and quantitatively among citrus types during blooming. Limonene was the most abundant flower volatile only in citrons; α-citral and β-citral ranked 2nd and 3rd only for Bergamot, and unopened flowers of Ponkan had a higher amount of linalool and β-pinene while much lower amount of γ-terpinene and p-cymene than Satsuma. Taking the average of all cultivars, linalool and limonene were the top two volatiles for all blooming stages; β-pinene ranked 3rd in unopened flowers, while indole ranked 3rd for half opened and fully opened flower volatiles. As flowers bloomed, methyl anthranilate increased while 2-hexenal and p-cymene decreased. In some cases, a volatile could be high in both unopened and fully opened flowers but low in half opened ones. Through multivariate analysis, the nine citrus cultivars were clustered into three groups, consistent with the three true citrus types. Furthermore, an influence of blooming stages on clustering was observed, especially with hybrids Satsuma and Huyou. Altogether, it was suggested that flower volatiles can be suitable markers for revealing the genetic relationships between citrus cultivars but the same blooming stage needs to be strictly controlled.

  3. The correlation between Prorocentrum donghaiense blooms and the Taiwan warm current in the East China Sea - evidence for the "Pelagic Seed Bank" hypothesis.

    Science.gov (United States)

    Dai, Xinfeng; Lu, Douding; Guan, Weibing; Xia, Ping; Wang, Hongxia; He, Piaoxia; Zhang, Dongsheng

    2013-01-01

    During the last two decades, large-scale high biomass algal blooms of the dinoflagellate Prorocentrum donghaiense Lu have occurred frequently in the East China Sea (ECS). The role of increasing nutrient concentrations in driving those blooms is well-established, but the source population that initiates them is poorly understood. We hypothesized that the front of Taiwan Warm Current (TWC) may serve as a 'seed bank' that initiates P. donghaiense blooms in the ECS, as the physiochemical conditions in the TWC are suitable for the growth of P. donghaiense. In order to test this hypothesis, two surveys at different spatio-temporal scales were conducted in 2010 and 2011. We found a strong correlation in space and time between the abundance of P. donghaiense and the TWC. The spatial extent of the P. donghaiense bloom coincided with the TWC front in both 2010 and 2011. During the early development of the blooms, P. donghaiense concentration was highest at the TWC front, and then the bloom mass shifted inshore over the course of our 2011 survey. The TWC also moved inshore, albeit after the appearance of P. donghaiense. Overall, these results support our hypothesis that P. donghaiense blooms develop from the population at the TWC front in the ECS, suggesting the role of the ocean current front as a seed bank to dinoflagellate blooms.

  4. The correlation between Prorocentrum donghaiense blooms and the Taiwan warm current in the East China Sea - evidence for the "Pelagic Seed Bank" hypothesis.

    Directory of Open Access Journals (Sweden)

    Xinfeng Dai

    Full Text Available During the last two decades, large-scale high biomass algal blooms of the dinoflagellate Prorocentrum donghaiense Lu have occurred frequently in the East China Sea (ECS. The role of increasing nutrient concentrations in driving those blooms is well-established, but the source population that initiates them is poorly understood. We hypothesized that the front of Taiwan Warm Current (TWC may serve as a 'seed bank' that initiates P. donghaiense blooms in the ECS, as the physiochemical conditions in the TWC are suitable for the growth of P. donghaiense. In order to test this hypothesis, two surveys at different spatio-temporal scales were conducted in 2010 and 2011. We found a strong correlation in space and time between the abundance of P. donghaiense and the TWC. The spatial extent of the P. donghaiense bloom coincided with the TWC front in both 2010 and 2011. During the early development of the blooms, P. donghaiense concentration was highest at the TWC front, and then the bloom mass shifted inshore over the course of our 2011 survey. The TWC also moved inshore, albeit after the appearance of P. donghaiense. Overall, these results support our hypothesis that P. donghaiense blooms develop from the population at the TWC front in the ECS, suggesting the role of the ocean current front as a seed bank to dinoflagellate blooms.

  5. Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study.

    Science.gov (United States)

    Li, Ping; Xu, Lei; Yang, Lin; Wang, Rui; Hsieh, Jiang; Sun, Zhonghua; Fan, Zhanming; Leipsic, Jonathon A

    2018-05-02

    The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis.

  6. Coupling planktonic and benthic shifts during a bloom of Alexandrium catenella in southern Chile:Implications for bloom dynamics and recurrence

    OpenAIRE

    Díaz, P.A.; Molinet, C.; Seguel, M.; Díaz, M.; Labra, G.; Figueroa, R.I. (Rosa Isabel)

    2014-01-01

    Cell abundances and distributions of Alexandrium catenella resting cysts in recent sediments were studied along time at two locations in the Chilean Inland Sea exposed to different oceanographic conditions: Low Bay, which is much more open to the ocean than the more interior and protected Ovalada Island. The bloom began in interior areas but maximum cyst concentrations were recorded in locations more open to the ocean, at the end of the Moraleda channel. Our results showed a time lapse of aro...

  7. Phytoplankton-Associated Bacterial Community Composition and Succession during Toxic Diatom Bloom and Non-Bloom Events.

    Science.gov (United States)

    Sison-Mangus, Marilou P; Jiang, Sunny; Kudela, Raphael M; Mehic, Sanjin

    2016-01-01

    Pseudo-nitzschia blooms often occur in coastal and open ocean environments, sometimes leading to the production of the neurotoxin domoic acid that can cause severe negative impacts to higher trophic levels. Increasing evidence suggests a close relationship between phytoplankton bloom and bacterial assemblages, however, the microbial composition and succession during a bloom process is unknown. Here, we investigate the bacterial assemblages before, during and after toxic and non-toxic Pseudo-nitzschia blooms to determine the patterns of bacterial succession in a natural bloom setting. Opportunistic sampling of bacterial community profiles were determined weekly at Santa Cruz Municipal Wharf by 454 pyrosequencing and analyzed together with domoic acid levels, phytoplankton community and biomass, nutrients and temperature. We asked if the bacterial communities are similar between bloom and non-bloom events and if domoic acid or the presence of toxic algal species acts as a driving force that can significantly structure phytoplankton-associated bacterial communities. We found that bacterial diversity generally increases when Pseudo-nitzschia numbers decline. Furthermore, bacterial diversity is higher when the low-DA producing P. fraudulenta dominates the algal bloom while bacterial diversity is lower when high-DA producing P. australis dominates the algal bloom, suggesting that the presence of algal toxin can structure bacterial community. We also found bloom-related succession patterns among associated bacterial groups; Gamma-proteobacteria, were dominant during low toxic P. fraudulenta blooms comprising mostly of Vibrio spp., which increased in relative abundance (6-65%) as the bloom progresses. On the other hand, Firmicutes bacteria comprising mostly of Planococcus spp. (12-86%) dominate during high toxic P. australis blooms, with the bacterial assemblage showing the same bloom-related successional patterns in three independent bloom events. Other environmental

  8. Phytoplankton-associated bacterial community composition and succession during toxic diatom bloom and non-bloom events

    Directory of Open Access Journals (Sweden)

    Marilou P. Sison-Mangus

    2016-09-01

    Full Text Available Pseudo-nitzschia blooms often occur in coastal and open ocean environments, sometimes leading to the production of the neurotoxin domoic acid that can cause severe negative impacts to higher trophic levels. Increasing evidence suggests a close relationship between phytoplankton bloom and bacterial assemblages, however, the microbial composition and succession during a bloom process is unknown. Here, we investigate the bacterial assemblages before, during and after toxic and non-toxic Pseudo-nitzschia blooms to determine the patterns of bacterial succession in a natural bloom setting. Opportunistic sampling of bacterial community profiles were determined weekly at Santa Cruz Municipal Wharf by 454 pyrosequencing and analyzed together with domoic acid levels, phytoplankton community and biomass, nutrients and temperature. We asked if the bacterial communities are similar between bloom and non-bloom events and if domoic acid or the presence of toxic algal species acts as a driving force that can significantly structure phytoplankton-associated bacterial communities. We found that bacterial diversity generally increases when Pseudo-nitzschia numbers decline. Furthermore, bacterial diversity is higher when the low-DA producing P. fraudulenta dominates the algal bloom while bacterial diversity is lower when high-DA producing P. australis dominates the algal bloom, suggesting that the presence of algal toxin can structure bacterial community. We also found bloom-related succession patterns among associated bacterial groups; Gamma-proteobacteria, were dominant during low toxic P. fraudulenta blooms comprising mostly of Vibrio spp., which increased in relative abundance (6%-65% as the bloom progresses. On the other hand, Firmicutes bacteria comprising mostly of Planococcus spp. (12%- 86% dominate during high toxic P. australis blooms, with the bacterial assemblage showing the same bloom-related successional patterns in 3 independent bloom events. Other

  9. Machine learning in heart failure: ready for prime time.

    Science.gov (United States)

    Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

    2018-03-01

    The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

  10. In real time: exploring nursing students' learning during an international experience.

    Science.gov (United States)

    Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

    2013-10-11

    Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

  11. Understanding the Advising Learning Process Using Learning Taxonomies

    Science.gov (United States)

    Muehleck, Jeanette K.; Smith, Cathleen L.; Allen, Janine M.

    2014-01-01

    To better understand the learning that transpires in advising, we used Anderson et al.'s (2001) revision of Bloom's (1956) taxonomy and Krathwohl, Bloom, and Masia's (1964) affective taxonomy to analyze eight student-reported advising outcomes from Smith and Allen (2014). Using the cognitive processes and knowledge domains of Anderson et al.'s…

  12. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  13. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  14. Museums as spaces and times for learning and social participation.

    Directory of Open Access Journals (Sweden)

    César M.

    2014-12-01

    Full Text Available A museum is valued according to its collections, communication and knowledge exchange with visitors (Primo, 1999. Museums should be in dialogue with the public, contributing to their development (Skramstad, 2004 and collective memory (Wertsch, 2004. Social interactions and working in participants’ zone of proximal development (Vygotsky, 1934/1962 play an important role in non-formal learning opportunities that take place at museums. The National Museum of Natural History and Science (Lisbon University offers weekly holiday programmes for children and teenagers, aiming at developing scientific literacy in intercultural and inclusive spaces and times, facilitating knowledge appropriation and social participation. We studied these programmes, assuming an interpretive approach (Denzin, 2002 and developing an intrinsic case study (Stake, 1995. The main participants were these children and teenagers, their parents, and museum educational agents. Data collecting instruments included observation, interviews, questionnaires, children and teenagers’ protocols and tasks inspired in projective techniques. Data treatment and analysis was based on a narrative content analysis (Clandinin & Connelly, 1998 from which inductive categories emerged (Hamido & César, 2009. Some examples illuminate participants’ expectancies, their engagement in activities, and the contributions of social interactions and non-formal education to the development of scientific literacy.

  15. Project Management in Real Time: A Service-Learning Project

    Science.gov (United States)

    Larson, Erik; Drexler, John A., Jr.

    2010-01-01

    This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

  16. Age and time effects on implicit and explicit learning

    NARCIS (Netherlands)

    Verneau, M.; Kamp, J. van der; Savelsbergh, G.J.P.; Looze, M.P. de

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  17. Age and Time Effects on Implicit and Explicit Learning

    NARCIS (Netherlands)

    Verneau, M.M.N.; van der Kamp, J.; Savelsbergh, G.J.P.; de Looze, M.P.

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  18. Language Learning Attitudes: Ingrained Or Shaped In Time?

    Directory of Open Access Journals (Sweden)

    Gökçe DİŞLEN DAĞGÖL

    2017-09-01

    Full Text Available Language learning has become an essential need in today’s world. From academic to social settings, humans need to communicate in a different language to survive in their community. However, despite this increasing importance of language, it is difficult to say we have attained successful language learning on a large scale since there are a lot of factors in language learning process. Language attitudes, one of these factors, influence this process both positively and negatively, depending on how we view learning a foreign language. Therefore, this study deals with the issue of language attitudes to uncover learners’ language conceptions and probable effects on their learning. Moreover, this study aims to reveal the potential role of past learning experiences on the development of language beliefs positively or negatively. Thus, 35 university students in their 1st, 2nd, 3rd and 4th years constitute the participants of the study. Based on mixed research design, the study is comprised of both quantitative and qualitative data. Quantitative data were gathered through Attitude Scale towards English Course, and the analyses were performed with Statistical Packages for Social Sciences (SPSS 17.0 version for Windows. The qualitative data were collected from students’ reports of their own autobiographies regarding their previous language learning experiences in elementary, secondary, high school and university years, and were subjected to the content analysis. The study showed language attitudes from behavioural, cognitive and affective perspectives and found out different factors in shaping their learning conceptions.

  19. The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent

    Science.gov (United States)

    Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie

    2017-01-01

    Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…

  20. Incremental Impact of Time on Students' Use of E-Learning via Facebook

    Science.gov (United States)

    Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem

    2018-01-01

    The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…

  1. Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time

    Science.gov (United States)

    Dorph, Rena; Schunn, Christian D.; Crowley, Kevin

    2017-01-01

    The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…

  2. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma

    2013-06-01

    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  3. Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

    Science.gov (United States)

    Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

    2012-05-01

    In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

  4. Shallow water processes govern system-wide phytoplankton bloom dynamics: A modeling study

    Science.gov (United States)

    Lucas, L.V.; Koseff, Jeffrey R.; Monismith, Stephen G.; Thompson, J.K.

    2009-01-01

    A pseudo-two-dimensional numerical model of estuarine phytoplankton growth and consumption, vertical turbulent mixing, and idealized cross-estuary transport was developed and applied to South San Francisco Bay. This estuary has two bathymetrically distinct habitat types (deep channel, shallow shoal) and associated differences in local net rates of phytoplankton growth and consumption, as well as differences in the water column's tendency to stratify. Because many physical and biological time scales relevant to algal population dynamics decrease with decreasing depth, process rates can be especially fast in the shallow water. We used the model to explore the potential significance of hydrodynamic connectivity between a channel and shoal and whether lateral transport can allow physical or biological processes (e.g. stratification, benthic grazing, light attenuation) in one sub-region to control phytoplankton biomass and bloom development in the adjacent sub-region. Model results for South San Francisco Bay suggest that lateral transport from a productive shoal can result in phytoplankton biomass accumulation in an adjacent deep, unproductive channel. The model further suggests that turbidity and benthic grazing in the shoal can control the occurrence of a bloom system-wide; whereas, turbidity, benthic grazing, and vertical density stratification in the channel are likely to only control local bloom occurrence or modify system-wide bloom magnitude. Measurements from a related field program are generally consistent with model-derived conclusions. ?? 2008 Elsevier B.V.

  5. Spring Blooms Observed with Biochemical Profiling Floats from a Chemical and Biological Perspective

    Science.gov (United States)

    Plant, J. N.; Johnson, K. S.; Sakamoto, C.; Jannasch, H. W.; Coletti, L. J.; Elrod, V.

    2015-12-01

    Recently there has been renewed interest in the mechanisms which control the seasonal increases in plankton biomass (spring blooms). Changes in physical and chemical forcing (light, wind, heat and nutrients) may increase the specific growth rate of phytoplankton. These changes may also shift the predator - prey relationships within the food web structure, which can alter the balance between plankton growth and loss rates. Biogeochemical profiling floats provide a means to observe the seasonal evolution of spring blooms from a physical, chemical and biological perspective in near real time. Floats equipped with optical sensors to measure nitrate, oxygen, chlorophyll fluorescence, and optical backscatter now have a presence in many ocean regions including the North Pacific, Subarctic Pacific, North Atlantic, South Atlantic and the Southern Ocean. Data from these regions are used to compare and contrast the evolution of spring blooms. The evolution of the bloom is examined using both chemical (oxygen, nitrate) and biooptical (phytoplankton from chlorophyll fluorescence and particulate organic carbon from optical backscatter) sensors under vastly different environmental conditions.

  6. Factors controlling the development of phytoplankton blooms in the Antarctic Ocean

    International Nuclear Information System (INIS)

    Sakshaug, Egil; Holm-Hansen, Osmund

    1991-01-01

    A mathematical model describing the development of phytoplankton blooms as a function of the depth of the wind-mixed layer, spectral distribution of light, passage of atmospheric low-pressure systems, size of the initial phytoplankton stock and loss rates is presented. Model runs represent shade-adapted, large-celled, bloom-forming diatoms Periodic deep mixing caused by strong winds may severely retard the development of blooms and frequently abort them before macronutrients are completely exhausted. Moderate depths of mixing (40-50 m) in combination with a moderately large total loss rate (about 0.013h -1 ) can prevent blooms from developing during the brightest time of the year. Complete exhaustion of macronutrients in the upper waters is likely only if the wind-mixed layer is less than 10 m deep, i.e. in very sheltered waters, and also in the marginal ice zone when ice is melting. The authors do not exclude the possibility of control of phytoplankton biomass by iron in ice-free, deep-sea parts of the Antarctic Ocean, but the implied enhancement of export production through addition of iron might be restricted because of limitation by light, i.e. vertical mixing. (author). 32 ref.; 5 figs.; 2 tabs

  7. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom.

    Science.gov (United States)

    Needham, David M; Fuhrman, Jed A

    2016-02-29

    Marine phytoplankton perform approximately half of global carbon fixation, with their blooms contributing disproportionately to carbon sequestration(1), and most phytoplankton production is ultimately consumed by heterotrophic prokaryotes(2). Therefore, phytoplankton and heterotrophic community dynamics are important in modelling carbon cycling and the impacts of global change(3). In a typical bloom, diatoms dominate initially, transitioning over several weeks to smaller and motile phytoplankton(4). Here, we show unexpected, rapid community variation from daily rRNA analysis of phytoplankton and prokaryotic community members following a bloom off southern California. Analysis of phytoplankton chloroplast 16S rRNA demonstrated ten different dominant phytoplankton over 18 days alone, including four taxa with animal toxin-producing strains. The dominant diatoms, flagellates and picophytoplankton varied dramatically in carbon export potential. Dominant prokaryotes also varied rapidly. Euryarchaea briefly became the most abundant organism, peaking over a few days to account for about 40% of prokaryotes. Phytoplankton and prokaryotic communities correlated better with each other than with environmental parameters. Extending beyond the traditional view of blooms being controlled primarily by physics and inorganic nutrients, these dynamics imply highly heterogeneous, continually changing conditions over time and/or space and suggest that interactions among microorganisms are critical in controlling plankton diversity, dynamics and fates.

  8. Machine learning application in the life time of materials

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

  9. Use of Bloom's Taxonomy in Developing Reading Comprehension Specifications

    Science.gov (United States)

    Luebke, Stephen; Lorie, James

    2013-01-01

    This article is a brief account of the use of Bloom's Taxonomy of Educational Objectives (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956) by staff of the Law School Admission Council in the 1990 development of redesigned specifications for the Reading Comprehension section of the Law School Admission Test. Summary item statistics for the…

  10. Salmon mortalities associated with a bloom of Alexandrium ...

    African Journals Online (AJOL)

    Blue mussels Mytilus edulis analysed from areas affected by the bloom reached levels of 18 000ìg STX equivalents 100g–1 of tissue. As a result of the salmon mortalities, a project was initiated to establish a monitoring approach for harmful algal blooms to provide an early warning of potential events and to act as a tool for ...

  11. Bacterial community transcription patterns during a marine phytoplankton bloom.

    Science.gov (United States)

    Rinta-Kanto, Johanna M; Sun, Shulei; Sharma, Shalabh; Kiene, Ronald P; Moran, Mary Ann

    2012-01-01

    Bacterioplankton consume a large proportion of photosynthetically fixed carbon in the ocean and control its biogeochemical fate. We used an experimental metatranscriptomics approach to compare bacterial activities that route energy and nutrients during a phytoplankton bloom compared with non-bloom conditions. mRNAs were sequenced from duplicate bloom and control microcosms 1 day after a phytoplankton biomass peak, and transcript copies per litre of seawater were calculated using an internal mRNA standard. Transcriptome analysis revealed a potential novel mechanism for enhanced efficiency during carbon-limited growth, mediated through membrane-bound pyrophosphatases [V-type H(+)-translocating; hppA]; bloom bacterioplankton participated less in this metabolic energy scavenging than non-bloom bacterioplankton, with possible implications for differences in growth yields on organic substrates. Bloom bacterioplankton transcribed more copies of genes predicted to increase cell surface adhesiveness, mediated by changes in bacterial signalling molecules related to biofilm formation and motility; these may be important in microbial aggregate formation. Bloom bacterioplankton also transcribed more copies of genes for organic acid utilization, suggesting an increased importance of this compound class in the bioreactive organic matter released during phytoplankton blooms. Transcription patterns were surprisingly faithful within a taxon regardless of treatment, suggesting that phylogeny broadly predicts the ecological roles of bacterial groups across 'boom' and 'bust' environmental backgrounds. © 2011 Society for Applied Microbiology and Blackwell Publishing Ltd.

  12. Harmful algal blooms of the Southern Benguela current: A review ...

    African Journals Online (AJOL)

    Harmful algal blooms of the Southern Benguela current: A review and appraisal of monitoring from 1989 to 1997. ... The Benguela upwelling system is subjected to blooms of harmful and toxic algae, the incidence and consequences of which are documented here. Red tides are common and usually attributed to members of ...

  13. Rethinking Bloom's Taxonomy: Implications for Testing and Assessment.

    Science.gov (United States)

    Anderson, Lorin W.

    This paper describes a work in progress on a second edition of "Taxonomy of Educational Objectives, The Classification of Educational Goals, Handbook I: Cognitive Domain," also known as "Bloom's Taxonomy" (B. Bloom and others, Eds., 1956). The new edition will be grounded in the collective wisdom of the original…

  14. Links between Bloom's Taxonomy and Gardener's Multiple Intelligences: The issue of Textbook Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud Abdi Tabari

    2015-02-01

    Full Text Available The major thrust of this research was to investigate the cognitive aspect of the high school textbooks and interchange series, due to their extensive use, through content analysis based on Bloom's taxonomy and Gardner's Multiple Intelligences (MI. This study embraced two perspectives in a grid in order to broaden and deepen the analysis by determining the numbers and the types of intelligences with respect to their learning objectives tapped in the textbooks and comparing them. Through codification of Bloom’s learning objectives and Gardner's MI, the results showed that there was a significant difference between the numbers of intelligences with respect to their learning objectives in the textbooks. However, the interchange series enjoyed a large number of the spatial and the interpersonal intelligences across eight levels of learning objectives, whereas they had the least number of the intrapersonal, the musical, and the bodily-kinesthetic intelligences across knowledge understanding and application levels. Keywords: learning objectives, multiple intelligences, textbook analysis

  15. Establishing the link between Ostreopsis cf.ovata blooms and human health impacts using ecology and epidemiology

    Directory of Open Access Journals (Sweden)

    Magda Vila

    2016-09-01

    Full Text Available Blooms of the benthic dinoflagellate Ostreopsis have been related to sporadic acute respiratory symptoms and general malaise in people exposed to marine aerosols on some Mediterranean beaches. However, the direct link between recurrent Ostreopsis blooms and health problems has not been clearly established. In order to establish and elucidate the connection, we conducted a joint ecology and epidemiology study in an Ostreopsis hot spot. Throughout the bloom, which extended from the end of June until the end of October 2013, 81% of the human cohort that we studied experienced at least one Ostreopsis-related symptom. Paradoxically, the time when the effects were greatest was during a short time window in early August. This corresponded to the transition from the exponential growth to the stationary phase of the bloom. Negligible symptoms were reported from August to mid-October, during the stationary period of the proliferation, when O. cf. ovata maintained high concentrations of epiphytic cells. No clear patterns in the landward wind component were noted during the time when health effects were greatest. Our main hypothesis is that the irritants present in the aerosol are produced during a particular physiological phase of the Ostreopsis cells during the bloom.

  16. A matter of timing: harm reduction in learned helplessness.

    Science.gov (United States)

    Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

    2014-11-03

    Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

  17. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  18. Visual Literacy in Bloom: Using Bloom's Taxonomy to Support Visual Learning Skills

    Science.gov (United States)

    Arneson, Jessie B.; Offerdahl, Erika G.

    2018-01-01

    "Vision and Change" identifies science communication as one of the core competencies in undergraduate biology. Visual representations are an integral part of science communication, allowing ideas to be shared among and between scientists and the public. As such, development of scientific visual literacy should be a desired outcome of…

  19. Towards Real-Time Speech Emotion Recognition for Affective E-Learning

    Science.gov (United States)

    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

    2016-01-01

    This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

  20. Further Studies on the Physical and Biogeochemical Causes for Large Interannual Changes in the Patagonian Shelf Spring-Summer Phytoplankton Bloom Biomass

    Science.gov (United States)

    Signorini, Sergio R.; Garcia, Virginia M.T.; Piola, Alberto R.; Evangelista, Heitor; McClain, Charles R.; Garcia, Carlos A.E.; Mata, Mauricio M.

    2009-01-01

    A very strong and persistent phytoplankton bloom was observed by ocean color satellites during September - December 2003 along the northern Patagonian shelf. The 2003 bloom had the highest extent and chlorophyll a (Chl-a) concentrations of the entire Sea-viewing Wide Field-of-view Sensor (SeaWiFS) period (1997 to present). SeaWiFS-derived Chl-a exceeded 20 mg/cu m in November at the bloom center. The bloom was most extensive in December when it spanned more than 300 km across the shelf and nearly 900 km north-south (35degS to 43degS). The northward reach and the deep penetration on the shelf of the 2003 bloom were quite anomalous when compared with other years, which showed the bloom more confined to the Patagonian shelf break (PSB). The PSB bloom is a conspicuous austral spring-summer feature detected by ocean color satellites and its timing can be explained using the Sverdrup critical depth theory. Based on high-resolution numerical simulations, in situ and remote sensing data, we provide some suggestions for the probable mechanisms responsible for that large interannual change of biomass as seen by ocean color satellites. Potential sources of macro and micro (e.g., Fe) nutrients that sustain the high phytoplankton productivity of the Patagonian shelf waters are identified, and the most likely physical processes that maintain the nutrient balance in the region are discussed.

  1. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    The focus of discussion is the intergenerational interactions and learning ... pastoralism and, to a lesser degree, cultivation (Mayer, 1971; Mostert, 1992). ... discouraged about the hard physical work and rather limited economic ... in the Amanzi for Food project, a middle-aged female participant, Mrs Peters, has involved a.

  2. Real-Time Barcode Detection and Classification Using Deep Learning

    DEFF Research Database (Denmark)

    Hansen, Daniel Kold; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

    2017-01-01

    Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-the- art deep learning-based detector of You Only Look Once (YOLO) for the purpose...

  3. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    Uncertainty is a universal phenomenon, a lived experience, an unease about acting ... uncertainty through mediations of knowledge, the formation of new social relations and ... Environmental Affairs and Tourism, 53% of young people in the country are ... Bubomi learning network connected to the Amanzi for Food project.

  4. Harmful Algal Blooms and Public Health.

    Science.gov (United States)

    Grattan, Lynn M; Holobaugh, Sailor; Morris, J Glenn

    2016-07-01

    The five most commonly recognized Harmful Algal Bloom related illnesses include Ciguatera poisoning, Paralytic Shellfish poisoning, Neurotoxin Shellfish poisoning, Diarrheic Shellfish Poisoning and Amnesic Shellfish poisoning. Although they are each the product of different toxins, toxin assemblages or HAB precursors these clinical syndromes have much in common. Exposure occurs through the consumption of fish or shellfish; routine clinical tests are not available for diagnosis; there is no known antidote for exposure; and the risk of these illnesses can negatively impact local fishing and tourism industries. Thus, illness prevention is of paramount importance to minimize human and public health risks. To accomplish this, close communication and collaboration is needed among HAB scientists, public health researchers and local, state and tribal health departments at academic, community outreach, and policy levels.

  5. Harmful Algal Blooms and Public Health

    Science.gov (United States)

    Grattan, Lynn M.; Holobaugh, Sailor; Morris, J. Glenn

    2015-01-01

    The five most commonly recognized Harmful Algal Bloom related illnesses include Ciguatera poisoning, Paralytic Shellfish poisoning, Neurotoxin Shellfish poisoning, Diarrheic Shellfish Poisoning and Amnesic Shellfish poisoning. Although they are each the product of different toxins, toxin assemblages or HAB precursors these clinical syndromes have much in common. Exposure occurs through the consumption of fish or shellfish; routine clinical tests are not available for diagnosis; there is no known antidote for exposure; and the risk of these illnesses can negatively impact local fishing and tourism industries. Thus, illness prevention is of paramount importance to minimize human and public health risks. To accomplish this, close communication and collaboration is needed among HAB scientists, public health researchers and local, state and tribal health departments at academic, community outreach, and policy levels. PMID:27616971

  6. Identification of non-indigenous phytoplankton species dominated bloom off Goa using inverted microscopy and pigment (HPLC) analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Bhaskar, P.V.; Roy, R.; Gauns, M.; Shenoy, D.M.; Rao, V.D.; Mochemadkar, S.

    site and sampling The mixed phytoplankton bloom was observed during one of the monthly sampling at the Can- dolim time series (CaTS) transect (figure 1) in the near-shore waters off Goa, west coast of India. Sea- water was sampled on two days (27 and 29... January, Figure 1. Map showing the CaTS (Candolim time-series) stations G1 to G5 and one station off Morjim north of CaTS. The approximate spread of the bloom is indicated by the shaded portion. Identification of non-indigenous phytoplankton off Goa 1147...

  7. A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

    Directory of Open Access Journals (Sweden)

    Vivian V. Valentin

    2014-07-01

    Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

  8. Microcystin in cyanobacterial blooms in a Chilean lake.

    Science.gov (United States)

    Campos, V; Cantarero, S; Urrutia, H; Heinze, R; Wirsing, B; Neumann, U; Weckesser, J

    1999-05-01

    Cyanobacterial blooms dominated by Microcystis sp. occurred in lake Rocuant ("marisma", near Concepción/Chile) in February 1995 and 1996. In the bloom samples collected in both years the hepatotoxin microcystin was detected by RP-HPLC in both samples and in the sample of 1995 also by a toxicity assay using primary rat hepatocytes. In the bloom of 1995, the microcystin content of the dry bloom biomass was determined to be 130 micrograms/g on the basis of the RP-HPLC peak area and 800 micrograms/g on the basis of the rat hepatotoxicity assay, respectively. In the bloom of 1996, RP-HPLC analysis revealed a microcystin content of 8.13 micrograms/g bloom material dry weight. In this year no hepatotoxicity was measured using a concentration range up to 0.8 mg (d. w.) of bloom material per ml in the rat hepatotoxicity assay. This is the first report on the detection of microcystins in Chilean water bodies.

  9. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

    Science.gov (United States)

    Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

    2017-01-01

    Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  10. Exploring Complex Engineering Learning Over Time with Epistemic Network Analysis

    OpenAIRE

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math andscience and introducing young people to the profession. However, the National Academy of Engineering found that many K-12engineering programs focus heavily on engineering design and science and math learning while minimizing the development ofengineering habits of mind. This narrowly-focused engineering activity can leave young people – and in particular, girls – with...

  11. Once upon a time.... Storytelling to enhance teaching and learning.

    Science.gov (United States)

    Lordly, Daphne

    2007-01-01

    The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.

  12. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  13. Optical researches for cyanobacteria bloom monitoring in Curonian Lagoon

    Science.gov (United States)

    Shirshin, Evgeny A.; Budylin, Gleb B.; Yakimov, Boris P.; Voloshina, Olga V.; Karabashev, Genrik S.; Evdoshenko, Marina A.; Fadeev, Victor V.

    2016-04-01

    Cyanobacteria bloom is a great ecological problem of Curonian Lagoon and Baltic Sea. The development of novel methods for the on-line control of cyanobacteria concentration and, moreover, for prediction of bloom spreading is of interest for monitoring the state of ecosystem. Here, we report the results of the joint application of hyperspectral measurements and remote sensing of Curonian Lagoon in July 2015 aimed at the assessment of cyanobacteria communities. We show that hyperspectral data allow on-line detection and qualitative estimation of cyanobacteria concentration, while the remote sensing data indicate the possibility of cyanobacteria bloom detection using the spectral features of upwelling irradiation.

  14. Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment

    Science.gov (United States)

    Schumacher, Christie; Arif, Sally

    2016-01-01

    Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839

  15. The right time to learn: mechanisms and optimization of spaced learning

    Science.gov (United States)

    Smolen, Paul; Zhang, Yili; Byrne, John H.

    2016-01-01

    For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

  16. Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

    Science.gov (United States)

    Zhang, Chao; Tao, Dacheng

    2012-12-01

    Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

  17. Online Learning Solutions for Freeway Travel Time Prediction

    NARCIS (Netherlands)

    Van Lint, J.W.C.

    2008-01-01

    Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time

  18. Business Faculty Time Management: Lessons Learned from the Trenches

    Science.gov (United States)

    Cummings, Richard G.; Holmes, Linda E.

    2009-01-01

    Teaching, research, and service expectations of the academic profession may sometimes seem overwhelming. Although much has been written about time management in general, there has not been much written about time management in the academic professions and even less written about time management for academics in the business disciplines. This paper…

  19. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  20. Learning of pitch and time structures in an artificial grammar setting.

    Science.gov (United States)

    Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

    2018-04-12

    Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  1. Identification of critical time-consuming student support activities in e-learning

    NARCIS (Netherlands)

    De Vries, Fred; Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Pannekeet, Kees; Koper, Rob

    2005-01-01

    Please cite the original publication: De Vries, F., Kester, L., Sloep, P., Van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time-consuming student support activities in e-learning. Research in Learning Technology (ALT-J), 13(3), 219-229.

  2. The Role of Age and Occupational Future Time Perspective in Workers' Motivation to Learn

    Science.gov (United States)

    Kochoian, Nané; Raemdonck, Isabel; Frenay, Mariane; Zacher, Hannes

    2017-01-01

    The purpose of this paper is to better understand the relationship between employees' chronological age and their motivation to learn, by adopting a lifespan perspective. Based on socioemotional selectivity theory, we suggest that occupational future time perspective mediates the relationship between age and motivation to learn. In accordance with…

  3. Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

    NARCIS (Netherlands)

    Noroozi, O.; Busstra, M.C.; Mulder, M.; Biemans, H.J.A.; Tobi, H.; Geelen, A.; Veer, van 't P.; Chizari, M.

    2012-01-01

    This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs.

  4. Effects of Business School Student's Study Time on the Learning Process

    Science.gov (United States)

    Tetteh, Godson Ayertei

    2016-01-01

    Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…

  5. A major seasonal phytoplankton bloom in the Madagascar Basin

    Science.gov (United States)

    Longhurst, Alan

    2001-11-01

    A hitherto-unnoticed phytoplankton bloom, of dimension 3000×1500 km, occupies the Madagascar Basin in late austral summer, being a prominent feature in SeaWiFS images. A first-order interpretation of the bloom mechanism invokes the seasonal deepening of the mixed layer within a strong mesoscale eddy-field and the consequent entrainment of nutrients into the photic zone. Features of the bloom correspond closely and appropriately with features of the eddy-field as observed by TOPEX-POSEIDON sea level anomalies. The bloom failed to develop in 1998, the second year of a two-year ENSO episode, when anomalously weak Southeast Trades will have failed to deepen the mixed layer as in other years.

  6. Spatial analysis of freshwater lake cyanobacteria blooms, 2008-2011

    Science.gov (United States)

    Background/Question/Methods Cyanobacteria and associated harmful algal blooms cause significant social, economic, and environmental impacts. Cyanobacteria synthesize hepatotoxins, neurotoxins, and dermatotoxins, affecting the health of humans and other species. The Cyanobacteria ...

  7. Nutrient control of cyanobacterial blooms in the Baltic Sea

    NARCIS (Netherlands)

    Stal, L.J.; Staal, M.J.; Villbrandt, M.

    1999-01-01

    Cyanobacterial blooms in the Baltic Sea were investigated with respect to growth Limitation and nitrogen fixation. The community was composed predominantly of Synechococcus spp., and large, heterocystous, nitrogen-fixing cyanobacteria (Aphanizomenon spp, and Nodularia spp.), that usually formed

  8. Algal blooms: an emerging threat to seawater reverse osmosis desalination

    KAUST Repository

    Villacorte, Loreen O.

    2014-08-04

    Seawater reverse osmosis (SWRO) desalination technology has been rapidly growing in terms of installed capacity and global application over the last decade. An emerging threat to SWRO application is the seasonal proliferation of microscopic algae in seawater known as algal blooms. Such blooms have caused operational problems in SWRO plants due to clogging and poor effluent quality of the pre-treatment system which eventually forced the shutdown of various desalination plants to avoid irreversible fouling of downstream SWRO membranes. This article summarizes the current state of SWRO technology and the emerging threat of algal blooms to its application. It also highlights the importance of studying the algal bloom phenomena in the perspective of seawater desalination, so proper mitigation and preventive strategies can be developed in the near future. © 2014 © 2014 Balaban Desalination Publications. All rights reserved.

  9. A review of carbon blooms on JET and TFTR

    International Nuclear Information System (INIS)

    Ulrickson, M.

    1990-01-01

    Operation of JET and TFTR at high auxiliary heating power has resulted in the occurrence of phenomena called carbon blooms. The carbon bloom is characterized by a rapid increases in the emission of carbon spectral lines, the Z eff , the radiated power, and the plasma density. There is also a concurrent decrease in the neutron emission rate, stored energy, and plasma pressure. On both machines the source of the carbon is observed to be at localized (both toroidally and polidally) hot spots on either the divertor plates or limiters. The localized hot spots are due to one or more of the following: disruption damage spots, misalignment of tiles, and/or exposed edges of tiles. The occurrence of carbon blooms limits the performance of the highest input power plasmas on both machines. This paper reviews the carbon bloom phenomenon as it occurs on both JET and TFTR. (orig.)

  10. Bacterial and protist community changes during a phytoplankton bloom

    KAUST Repository

    Pearman, John K.; Casas, Laura; Merle, Tony; Michell, Craig; Irigoien, Xabier

    2015-01-01

    )] as well as a control. This approach allowed us to discriminate the changes in species composition across a broad range of phylogenetic groups using a common taxonomic level. Diatoms dominated the bloom in the NPSc treatment while dinoflagellates were

  11. Algal blooms: an emerging threat to seawater reverse osmosis desalination

    KAUST Repository

    Villacorte, Loreen O.; Tabatabai, S. Assiyeh Alizadeh; Dhakal, N.; Amy, Gary L.; Schippers, Jan Cornelis; Kennedy, Maria Dolores

    2014-01-01

    Seawater reverse osmosis (SWRO) desalination technology has been rapidly growing in terms of installed capacity and global application over the last decade. An emerging threat to SWRO application is the seasonal proliferation of microscopic algae in seawater known as algal blooms. Such blooms have caused operational problems in SWRO plants due to clogging and poor effluent quality of the pre-treatment system which eventually forced the shutdown of various desalination plants to avoid irreversible fouling of downstream SWRO membranes. This article summarizes the current state of SWRO technology and the emerging threat of algal blooms to its application. It also highlights the importance of studying the algal bloom phenomena in the perspective of seawater desalination, so proper mitigation and preventive strategies can be developed in the near future. © 2014 © 2014 Balaban Desalination Publications. All rights reserved.

  12. Eddy-driven stratification initiates North Atlantic spring phytoplankton blooms.

    Science.gov (United States)

    Mahadevan, Amala; D'Asaro, Eric; Lee, Craig; Perry, Mary Jane

    2012-07-06

    Springtime phytoplankton blooms photosynthetically fix carbon and export it from the surface ocean at globally important rates. These blooms are triggered by increased light exposure of the phytoplankton due to both seasonal light increase and the development of a near-surface vertical density gradient (stratification) that inhibits vertical mixing of the phytoplankton. Classically and in current climate models, that stratification is ascribed to a springtime warming of the sea surface. Here, using observations from the subpolar North Atlantic and a three-dimensional biophysical model, we show that the initial stratification and resulting bloom are instead caused by eddy-driven slumping of the basin-scale north-south density gradient, resulting in a patchy bloom beginning 20 to 30 days earlier than would occur by warming.

  13. Flexible Learning and Teaching: Looking Beyond the Binary of Full-time/Part-time Provision in South African Higher Education

    Directory of Open Access Journals (Sweden)

    Barbara M Jones

    2015-06-01

    Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery.  The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.

  14. Application of first order rate kinetics to explain changes in bloom toxicity—the importance of understanding cell toxin quotas

    Science.gov (United States)

    Orr, Philip T.; Willis, Anusuya; Burford, Michele A.

    2018-04-01

    Cyanobacteria are oxygenic photosynthetic Gram-negative bacteria that can form potentially toxic blooms in eutrophic and slow flowing aquatic ecosystems. Bloom toxicity varies spatially and temporally, but understanding the mechanisms that drive these changes remains largely a mystery. Changes in bloom toxicity may result from changes in intracellular toxin pool sizes of cyanotoxins with differing molecular toxicities, and/or from changes in the cell concentrations of toxic and non-toxic cyanobacterial species or strains within bloom populations. We show here how first-order rate kinetics at the cellular level can be used to explain how environmental conditions drive changes in bloom toxicity at the ecological level. First order rate constants can be calculated for changes in cell concentration (μ c: specific cell division rate) or the volumetric biomass concentration (μ g: specific growth rate) between short time intervals throughout the cell cycle. Similar first order rate constants can be calculated for changes in nett volumetric cyanotoxin concentration (μ tox: specific cyanotoxin production rate) over similar time intervals. How μ c (or μ g ) covaries with μ tox over the cell cycle shows conclusively when cyanotoxins are being produced and metabolised, and how the toxicity of cells change in response to environment stressors. When μ tox/μ c>1, cyanotoxin cell quotas increase and individual cells become more toxic because the nett cyanotoxin production rate is higher than the cell division rate. When μ tox/μ c=1, cell cyanotoxin quotas remains fixed because the nett cyanotoxin production rate matches the cell division rate. When μ tox/μ ccyanotoxin cell quota decreases because either the nett cyanotoxin production rate is lower than the cell division rate, or metabolic breakdown and/or secretion of cyanotoxins is occurring. These fundamental equations describe cyanotoxin metabolism dynamics at the cellular level and provide the necessary

  15. The paradox of algal blooms in oligotrophic waters

    Science.gov (United States)

    Sundareshwar, P. V.; Upadhyay, S.; Abessa, M. B.; Honomichl, S.; Berdanier, B.; Spaulding, S.; Sandvik, C.; Trennepohl, A.

    2010-12-01

    Nutrient inputs to streams and lakes, primarily from anthropogenic sources, lead to eutrophic conditions that favor algal blooms with undesirable consequences. In contrast, low nutrient or oligotrophic waters rarely support algal blooms; such ecosystems are typically lower in productivity. Since the mid-1980’s however, the diatom Didymosphenia geminata has dramatically expanded its range colonizing oligotrophic rivers worldwide with blooms appearing as thick benthic mats. This recent global occurrence of Didymosphenia geminata blooms in temperate rivers has been perplexing in its pace of spread and the paradoxical nature of the nuisance growths. The blooms occur primarily in oligotrophic flowing waters, where phosphorus (P) availability often limits primary production. We present a biogeochemical process by which D. geminata mats adsorb both P and iron (Fe) from flowing waters and make P available for cellular uptake. The adsorbed P becomes bioavailable through biogeochemical processes that occur within the mat. The biogeochemical processes observed here while well accepted in benthic systems are novel for algal blooms in lotic habits. Enzymatic and bacterial processes such as Fe and sulfate reduction can release the adsorbed P and increase its bioavailability, creating a positive feedback between total stalk biomass and nutrient availability. Stalk affinity for Fe, Fe-P biogeochemistry, and interaction between watershed processes and climatic setting explain the paradoxical blooms, and the recent global spread of this invasive aquatic species. At a broader scale the study also implies that such algal blooms in oligotrophic environments can fundamentally alter the retention and longitudinal transfer of important nutrients such as P in streams and rivers.

  16. State of knowledge and concerns on cyanobacterial blooms and cyanotoxins.

    OpenAIRE

    Merel , Sylvain; Walker , David; Chicana , Ruth; Snyder , Shane; Baurès , Estelle; Thomas , Olivier

    2013-01-01

    International audience; Cyanobacteria are ubiquitous microorganisms considered as important contributors to the formation of Earth's atmosphere and nitrogen fixation. However, they are also frequently associated with toxic blooms. Indeed, the wide range of hepatotoxins, neurotoxins and dermatotoxins synthesized by these bacteria is a growing environmental and public health concern. This paper provides a state of the art on the occurrence and management of harmful cyanobacterial blooms in surf...

  17. Peculiarities of the Woody Plants Re-Bloom

    OpenAIRE

    Opalko Olga Anatolievna; Opalko Anatoly Ivanovich

    2015-01-01

    The data of literary sources concerning the bloom of angiosperm plants and deviation in the development of a flower and inflorescence, in particular untimely flowering, was generalized; our observation results of some peculiarities of re-bloom of woody plants in the National Dendrological Park “Sofiyivka” of NAS of Ukraine (NDP “Sofiyivka”) were discussed. The flowering process was formed during a long-term evolution of a propagation system of angiosperm plants as a basis of fertilization and...

  18. Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.

    Science.gov (United States)

    Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R

    2015-01-01

    Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.

  19. The 2008 North Atlantic Spring Bloom Experiment II: Autonomous Platforms and Mixed Layer Evolution

    Science.gov (United States)

    Lee, C. M.; D'Asaro, E. A.; Perry, M.; Fennel, K.; Gray, A.; Rehm, E.; Briggs, N.; Sackmann, B. S.; Gudmundsson, K.

    2008-12-01

    experience similar broad, long-timescale trends. Initial mixed layer depths exceeded 200 m, with gradual shoaling punctuated by periods of rapid, storm-driven deepening. In mid-April, a period of calm weather, rapid restratification and exponentially growing chlorophyll fluorescence marks the bloom's start. Although one-dimensional processes (e.g. diapycnal mixing and solar warming) clearly play important roles in producing the spring bloom, the rate and vertical extent of upper ocean restratification indicate that lateral mixing, perhaps wind- or eddy-driven exchange or the slumping of lateral density contrasts, play a more important role in restratifying the upper ocean. These important trigger events present a severe observational challenge as they take place at small (kilometers) spatial scales, are fully three-dimensional and episodic in time. The NAB08 efforts demonstrate how mobile, autonomous platforms can be exploited to resolve these events and their impact over the course of an entire bloom cycle.

  20. Effects of fertilizers used in agricultural fields on algal blooms

    Science.gov (United States)

    Chakraborty, Subhendu; Tiwari, P. K.; Sasmal, S. K.; Misra, A. K.; Chattopadhyay, Joydev

    2017-06-01

    The increasing occurrence of algal blooms and their negative ecological impacts have led to intensified monitoring activities. This needs the proper identification of the most responsible factor/factors for the bloom formation. However, in natural systems, algal blooms result from a combination of factors and from observation it is difficult to identify the most important one. In the present paper, using a mathematical model we compare the effects of three human induced factors (fertilizer input in agricultural field, eutrophication due to other sources than fertilizers, and overfishing) on the bloom dynamics and DO level. By applying a sophisticated sensitivity analysis technique, we found that the increasing use of fertilizers in agricultural field causes more rapid algal growth and decreases DO level much faster than eutrophication from other sources and overfishing. We also look at the mechanisms how fertilizer input rate affects the algal bloom dynamics and DO level. The model can be helpful for the policy makers in determining the influential factors responsible for the bloom formation.

  1. Geological Time, Biological Events and the Learning Transfer Problem

    Science.gov (United States)

    Johnson, Claudia C.; Middendorf, Joan; Rehrey, George; Dalkilic, Mehmet M.; Cassidy, Keely

    2014-01-01

    Comprehension of geologic time does not come easily, especially for students who are studying the earth sciences for the first time. This project investigated the potential success of two teaching interventions that were designed to help non-science majors enrolled in an introductory geology class gain a richer conceptual understanding of the…

  2. Learning for sustainability in times of accelerating change

    NARCIS (Netherlands)

    Wals, A.E.J.; Corcoran, P.B.

    2012-01-01

    We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the

  3. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

    Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

    2017-01-01

    Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

  4. Hydroclimatic conditions trigger record harmful algal bloom in western Patagonia (summer 2016).

    Science.gov (United States)

    León-Muñoz, Jorge; Urbina, Mauricio A; Garreaud, René; Iriarte, José Luis

    2018-01-22

    A harmful algal bloom (HAB) of the raphidophyta alga Pseudochattonella cf. verruculosa during the 2016 austral summer (February-March) killed nearly 12% of the Chilean salmon production, causing the worst mass mortality of fish and shellfish ever recorded in the coastal waters of western Patagonia. The HAB coincided with a strong El Niño event and the positive phase of the Southern Annular Mode that altered the atmospheric circulation in southern South America and the adjacent Pacific Ocean. This led to very dry conditions and higher than normal solar radiation reaching the surface. Using time series of atmospheric, hydrologic and oceanographic data we show here that an increase in surface water temperature and reduced freshwater input resulted in a weakening of the vertical stratification in the fjords and sounds of this region. This allowed the advection of more saline and nutrient-rich waters, ultimately resulting in an active harmful algal bloom in coastal southern Chile.

  5. Eutrophic waters, algal bloom and fish kill in fish farming areas in Bolinao, Pangasinan, Philippines

    International Nuclear Information System (INIS)

    San Diego-McGlone, Maria Lourdes; Azanza, Rhodora V.; Villanoy, Cesar L.; Jacinto, Gil S.

    2008-01-01

    The coastal waters of Bolinao, Pangasinan, Philippines experienced environmental changes over a 10-year period (1995-2005), the most significant effect of which was the major fish kill event in 2002 that coincided with the first reported Philippine bloom of a dinoflagellate Prorocentrum minimum. Days before the bloom, dissolved oxygen was <2.0 mg/l in the waters that were stratified. These conditions may be linked to the uncontrolled proliferation of fish pens and cages to more than double the allowable limit of 544 units for Bolinao waters. Mariculture activities release organic matter from unconsumed feed and fecal material that accumulate in the water and sediments. In over 10 years, water quality conditions have become eutrophic with ammonia increasing by 56%, nitrite by 35%, nitrate by 90%, and phosphate by 67%. The addition of more fish pens and cages placed additional stress to this poorly flushed, shallow area that affected water quality due to changes in the water residence time

  6. Intensive aggregate formation with low vertical flux during an upwelling-induced diatom bloom

    DEFF Research Database (Denmark)

    Kiørboe, Thomas; Tiselius, P.; Mitchell-Innes, B.

    1998-01-01

    of turbulent shear in the ocean such stickiness coefficients predict very high specific coagulation rates (0.3 d(-1)). In situ video observation demonstrated the occurrence of abundant diatom aggregates with surface water concentrations between 1,000 and 3,000 ppm. Despite the very high concentration......The surfaces of most pelagic diatoms are sticky at times and may therefore form rapidly settling aggregates by physical coagulation. Stickiness and aggregate formation may be particularly adaptive in upwelling systems by allowing the retention of diatom populations in the vicinity of the upwelling...... center. We therefore hypothesized that upwelling diatom blooms are terminated by aggregate formation and rapid sedimentation. We monitored the development of a maturing diatom (mainly Chaetoceros spp.) bloom in the Benguela upwelling current during 7 d in February. Chlorophyll concentrations remained...

  7. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

  9. Evaluation of Harmful Algal Bloom Outreach Activities

    Directory of Open Access Journals (Sweden)

    Richard Weisman

    2007-12-01

    Full Text Available With an apparent increase of harmful algal blooms (HABs worldwide,healthcare providers, public health personnel and coastal managers are struggling toprovide scientifically-based appropriately-targeted HAB outreach and education. Since1998, the Florida Poison Information Center-Miami, with its 24 hour/365 day/year freeAquatic Toxins Hotline (1-888-232-8635 available in several languages, has received over 25,000 HAB-related calls. As part of HAB surveillance, all possible cases of HAB-relatedillness among callers are reported to the Florida Health Department. This pilot studyevaluated an automated call processing menu system that allows callers to access bilingualHAB information, and to speak directly with a trained Poison Information Specialist. Themajority (68% of callers reported satisfaction with the information, and many provided specific suggestions for improvement. This pilot study, the first known evaluation of use and satisfaction with HAB educational outreach materials, demonstrated that the automated system provided useful HAB-related information for the majority of callers, and decreased the routine informational call workload for the Poison Information Specialists, allowing them to focus on callers needing immediate assistance and their healthcare providers. These results will lead to improvement of this valuable HAB outreach, education and surveillance tool. Formal evaluation is recommended for future HAB outreach and educational materials.

  10. Satellite monitoring of cyanobacterial harmful algal bloom ...

    Science.gov (United States)

    Cyanobacterial harmful algal blooms (cyanoHABs) cause extensive problems in lakes worldwide, including human and ecological health risks, anoxia and fish kills, and taste and odor problems. CyanoHABs are a particular concern because of their dense biomass and the risk of exposure to toxins in both recreational waters and drinking source waters. Successful cyanoHAB assessment by satellites may provide a first-line of defense indicator for human and ecological health protection. In this study, assessment methods were developed to determine the utility of satellite technology for detecting cyanoHAB occurrence frequency at locations of potential management interest. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) was evaluated to prepare for the equivalent Sentinel-3 Ocean and Land Colour Imager (OLCI) launched in 2016. Based on the 2012 National Lakes Assessment site evaluation guidelines and National Hydrography Dataset, there were 275,897 lakes and reservoirs greater than 1 hectare in the 48 U.S. states. Results from this evaluation show that 5.6 % of waterbodies were resolvable by satellites with 300 m single pixel resolution and 0.7 % of waterbodies were resolvable when a 3x3 pixel array was applied based on minimum Euclidian distance from shore. Satellite data was also spatially joined to US public water surface intake (PWSI) locations, where single pixel resolution resolved 57% of PWSI and a 3x3 pixel array resolved 33% of

  11. Giving English Language Learners the Time They Need to Succeed: Profiles of Three Expanded Learning Time Schools

    Science.gov (United States)

    Farbman, David A.

    2015-01-01

    With the number of students who are English language learners (ELLs) likely to double in coming years, it is more important than ever for schools across the U.S. to design and implement educational practices and strategies that best meet ELLs' learning needs, says the report, "Giving English Language Learners the Time They Need to…

  12. Dissociable effects of practice variability on learning motor and timing skills.

    Science.gov (United States)

    Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

    2018-01-01

    Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

  13. Making time for learning-oriented leadership in multidisciplinary hospital management groups.

    Science.gov (United States)

    Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

    2015-01-01

    Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

  14. Toxic cyanobacteria blooms in the Lithuanian part of the Curonian Lagoon

    Directory of Open Access Journals (Sweden)

    Artūras Razinkovas

    2009-06-01

    Full Text Available The phenomenon of cyanobacteria (blue-green algae blooms in the Baltic and the surrounding freshwater bodies has been known for several decades. The presence of cyanobacterial toxic metabolites in the Curonian Lagoon has been investigated and demonstrated for the first time in this work (2006-2007. Microcystis aeruginosa was the most common and widely distributed species in the 2006 blooms. Nodularia spumigena was present in the northern part of the Curonian Lagoon, following the intrusion of brackish water from the Baltic Sea; this is the first time that this nodularin-(NOD-producing cyanobacterium has been recorded in the lagoon. With the aid of high-performance liquid chromatography (HPLC, four microcystins (MC-LR, MC-RR, MC-LY, MC-YR and nodularin were detected in 2006. The presence of these cyanobacterial hepatotoxic cyclic peptides was additionally confirmed by enzyme-linked immunosorbent assay (ELISA and protein phosphatase inhibition assay (PP1. Microcystin-LR, the most frequent of them, was present in every sample at quite high concentrations (from <0.1 to 134.2 µg dm-3. In 2007, no cyanobacterial bloom was recorded and cyanotoxins were detected in only 4% of the investigated samples. A comparably high concentration of nodularin was detected in the northern part of the Curonian Lagoon. In one sample dimethylated MC-RR was also detected (concentration 7.5 µg dm-3.

  15. Eye on the Gemba: Using Student-Created Videos and the Revised Bloom's Taxonomy to Teach Lean Management

    Science.gov (United States)

    Marley, Kathryn A.

    2014-01-01

    Developing exercises that lead students to use higher order thinking skills is a challenge for faculty in any discipline. An excellent way to approach this problem is to use the Revised Bloom's Taxonomy as a guide. In the taxonomy, the highest level of learning is to create. The author describes an assignment that builds on the use of…

  16. Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times

    Science.gov (United States)

    Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven

    2018-01-01

    Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…

  17. The Blooming Anatomy Tool (BAT): A Discipline-Specific Rubric for Utilizing Bloom's Taxonomy in the Design and Evaluation of Assessments in the Anatomical Sciences

    Science.gov (United States)

    Thompson, Andrew R.; O'Loughlin, Valerie D.

    2015-01-01

    Bloom's taxonomy is a resource commonly used to assess the cognitive level associated with course assignments and examination questions. Although widely utilized in educational research, Bloom's taxonomy has received limited attention as an analytical tool in the anatomical sciences. Building on previous research, the Blooming Anatomy Tool (BAT)…

  18. Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

    Science.gov (United States)

    Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

    2017-12-01

    Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

  19. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  20. A presentation system for just-in-time learning in radiology.

    Science.gov (United States)

    Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

    2007-03-01

    There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

  1. [A technological device for optimizing the time taken for blind people to learn Braille].

    Science.gov (United States)

    Hernández, Cesar; Pedraza, Luis F; López, Danilo

    2011-10-01

    This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.

  2. Peer-assisted learning: time for nomenclature clarification

    Directory of Open Access Journals (Sweden)

    Alexander Olaussen

    2016-07-01

    Full Text Available Background: Peer-assisted learning (PAL is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1 describe different PAL programmes, 2 clarify the terminology surrounding PAL, and 3 propose a simple pragmatic way of defining PAL programmes based on their design. Methods: A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results: The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2, ‘Tutoring’ (1:3–10, and ‘Didactic’ (1:>10. From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions: We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy.

  3. Peer-assisted learning: time for nomenclature clarification

    Science.gov (United States)

    Olaussen, Alexander; Reddy, Priya; Irvine, Susan; Williams, Brett

    2016-01-01

    Background Peer-assisted learning (PAL) is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1) describe different PAL programmes, 2) clarify the terminology surrounding PAL, and 3) propose a simple pragmatic way of defining PAL programmes based on their design. Methods A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2), ‘Tutoring’ (1:3–10), and ‘Didactic’ (1:>10). From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy. PMID:27415590

  4. Dynamics of cyanobacterial bloom formation during short-term hydrodynamic fluctuation in a large shallow, eutrophic, and wind-exposed Lake Taihu, China.

    Science.gov (United States)

    Wu, Tingfeng; Qin, Boqiang; Zhu, Guangwei; Luo, Liancong; Ding, Yanqing; Bian, Geya

    2013-12-01

    Short-term hydrodynamic fluctuations caused by extreme weather events are expected to increase worldwide because of global climate change, and such fluctuations can strongly influence cyanobacterial blooms. In this study, the cyanobacterial bloom disappearance and reappearance in Lake Taihu, China, in response to short-term hydrodynamic fluctuations, was investigated by field sampling, long-term ecological records, high-frequency sensors and MODIS satellite images. The horizontal drift caused by the dominant easterly wind during the phytoplankton growth season was mainly responsible for cyanobacterial biomass accumulation in the western and northern regions of the lake and subsequent bloom formation over relatively long time scales. The cyanobacterial bloom changed slowly under calm or gentle wind conditions. In contrast, the short-term bloom events within a day were mainly caused by entrainment and disentrainment of cyanobacterial colonies by wind-induced hydrodynamics. Observation of a westerly event in Lake Taihu revealed that when the 30 min mean wind speed (flow speed) exceeded the threshold value of 6 m/s (5.7 cm/s), cyanobacteria in colonies were entrained by the wind-induced hydrodynamics. Subsequently, the vertical migration of cyanobacterial colonies was controlled by hydrodynamics, resulting in thorough mixing of algal biomass throughout the water depth and the eventual disappearance of surface blooms. Moreover, the intense mixing can also increase the chance for forming larger and more cyanobacterial colonies, namely, aggregation. Subsequently, when the hydrodynamics became weak, the cyanobacterial colonies continuously float upward without effective buoyancy regulation, and cause cyanobacterial bloom explosive expansion after the westerly. Furthermore, the results of this study indicate that the strong wind happening frequently during April and October can be an important cause of the formation and expansion of cyanobacterial blooms in Lake Taihu.

  5. An algorithm for learning real-time automata (extended abstract)

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which

  6. No Time to Think: Policy, Pedagogy and Professional Learning

    Science.gov (United States)

    Leonard, Simon N.; Roberts, Philip

    2016-01-01

    In this study, we seek to illuminate the effects of the global policy convergence in education through a close study of its enactment within an Australian Teacher Education course. Building on an examination of the changing priorities of a cohort of pre-service teachers over a short space of time, we argue that the enactment of New Public…

  7. Time and Practice: Learning to Become a Geographer

    Science.gov (United States)

    Downs, Roger M.

    2014-01-01

    A goal of geography education is fostering geographic literacy for all and building significant expertise for some. How much time and practice do students need to become literate or expert in geography? There is not an answer to this question. Using two concepts from cognitive psychology--the ideas of ten thousand hours and deliberate…

  8. Problem based learning: the effect of real time data on the website to student independence

    Science.gov (United States)

    Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

    2018-05-01

    Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

  9. Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

    Science.gov (United States)

    Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

    2017-03-01

    The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

  10. The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

    Science.gov (United States)

    Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

    2016-01-01

    One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

  11. Public Perception of Blue-Algae Bloom Risk in Hongze Lake of China

    Science.gov (United States)

    Huang, Lei; Sun, Kai; Ban, Jie; Bi, Jun

    2010-05-01

    In this work we characterize the public perception of one kind of ecological risk—blue-algae bloom in Hongze Lake, China, based on the psychometric paradigm method. In the first survey of May 2008, 300 respondents of Sihong County adjacent to Hongze Lake were investigated, with a total of 156 questionnaires returned. Then in a second survey of July 2008, 500 respondents from the same research area were investigated, with 318 questionnaires collected. This research firstly attempted to explore the local respondents’ degree of concern regarding ecological changes to Hongze Lake in the last ten years. Secondly, to explore the public perception of blue-algae bloom compared to three typical kinds of hazards including earthquake, nuclear power and public traffic. T-test was used to examine the difference of risk perception in these four hazards over time. The third part of this research, with demographic analysis and nonparametric statistical test, predicted the different groups of respondents’ willingness to accept (WTA) risk of blue-algae bloom in two surveys. Using multiple linear regression analysis, the risk perception model explained 28.3% of variance in the WTA blue-algae bloom risk. The variables of Knowledge, Social effect, Benefit, Controllability and Trust in government were significantly correlated with WTA, which implied that these variables were the main influencing factors explaining the respondents’ willingness to accept risk. The results would help the Chinese government to comprehend the public’s risk perception of the lake ecosystem, inducing well designed communication of risks with public and making effective mitigation policies to improve people’s rational risk judgment.

  12. On the Recurrence of Enigmatic Nannoplankton Blooms in the Subtropical South Atlantic during the Early Oligocene

    Science.gov (United States)

    Shanks, L. V.; Kelly, D. C.; Meyers, S. R.

    2015-12-01

    Climatic cooling and expansion of Antarctic ice sheets was accompanied by a global reorganization in ocean circulation during the early Oligocene. Such a change in the ocean-climate system is expected to alter the pelagic ecosystem through elevated rates of extinction and increased biogeographic provincialism. A well documented, but poorly understood, example of this provincialism is the recurrence of unusual chalks composed of the nannofossil genus Braarudosphaera across the subtropical South Atlantic Ocean. Here we present preliminary findings from a study of the paleoceanographic conditions that fostered these Braarudosphaera "blooms" at Deep Sea Drilling Site 516 (Rio Grande Rise, southwestern Atlantic). Within the early Oligocene stratigraphy at this site, there are four chalky (recrystallized) layers in which braarudosphaerids compose ~70% of the nannofossil assemblages. Astronomical tuning was performed on conventional benthic foraminiferal δ18O and δ13C records encompassing the four layers to determine the timing of their recurrence. A strong astronomical rhythm is preserved with the blooms occurring during nodes in the theoretical obliquity solution. In addition, planktic foraminiferal stable isotope (δ18O, δ13C) records were generated for the study section using both secondary ion mass spectrometry (SIMS) and conventional gas-source isotope ratio mass spectrometry (IRMS). The SIMS-based δ13C record for the thermocline-dwelling genus Catapsydrax registers substantial (~1.5‰) decreases during the blooms, signaling pulsed increases in the upwelling of 13C-depleted waters. By contrast, the IRMS-based δ13C record for this same genus show no appreciable change in hydrographic conditions during the blooms. We attribute the invariant nature of the IRMS-based δ13C record to the smoothing effects of diagenesis. These results demonstrate how marine plankton respond to changing oceanographic conditions driven by astronomical forcing of ice-sheet dynamics.

  13. Characterisation of transparent exopolymer particles (TEP) produced during algal bloom: A membrane treatment perspective

    KAUST Repository

    Villacorte, Loreen O.

    2013-01-01

    Algal blooms are currently a major concern of the membrane industry as it generates massive concentrations of organic matter (e.g. transparent exopolymer particles [TEP]), which can adversely affect the operation of membrane filtration systems. The goal of this study is to understand the production, composition and membrane rejection of these organic materials using different characterisation techniques. Two common species of bloom-forming freshwater and marine algae were cultivated in batch cultures for 30days and the productions of TEP and other organic matter were monitored at different growth phases. TEP production of the marine diatom, Chaetoceros affinis, produced 6-9 times more TEP than the freshwater blue-green algae, Microcystis. The organic substances produced by both algal species were dominated by biopolymeric substances such as polysaccharides (45-64%) and proteins (2-17%) while the remaining fraction comprises of low molecular weight refractory (humic-like) and/ or biogenic organic substances. MF/UF membranes mainly rejected the biopolymers but not the low molecular weight organic materials. MF membranes (0.1-0.4 lm) rejected 42-56% of biopolymers, while UF membranes (10-100 kDa) rejected 65-95% of these materials. Further analysis of rejected organic materials on the surface of the membranes revealed that polysac-charides and proteins are likely responsible for the fouling of MF/UF systems during an algal bloom situation. © 2013 Desalination Publications.

  14. Mitigating Harmful Cyanobacterial Blooms in a Human- and Climatically-Impacted World

    Directory of Open Access Journals (Sweden)

    Hans W. Paerl

    2014-12-01

    Full Text Available Bloom-forming harmful cyanobacteria (CyanoHABs are harmful from environmental, ecological and human health perspectives by outcompeting beneficial phytoplankton, creating low oxygen conditions (hypoxia, anoxia, and by producing cyanotoxins. Cyanobacterial genera exhibit optimal growth rates and bloom potentials at relatively high water temperatures; hence, global warming plays a key role in their expansion and persistence. CyanoHABs are regulated by synergistic effects of nutrient (nitrogen:N and phosphorus:P supplies, light, temperature, vertical stratification, water residence times, and biotic interactions. In most instances, nutrient control strategies should focus on reducing both N and P inputs. Strategies based on physical, chemical (nutrient and biological manipulations can be effective in reducing CyanoHABs; however, these strategies are largely confined to relatively small systems, and some are prone to ecological and environmental drawbacks, including enhancing release of cyanotoxins, disruption of planktonic and benthic communities and fisheries habitat. All strategies should consider and be adaptive to climatic variability and change in order to be effective for long-term control of CyanoHABs. Rising temperatures and greater hydrologic variability will increase growth rates and alter critical nutrient thresholds for CyanoHAB development; thus, nutrient reductions for bloom control may need to be more aggressively pursued in response to climatic changes globally.

  15. Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertainties.

    Science.gov (United States)

    Hoffmann, Holger; Rath, Thomas

    2013-01-01

    The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

  16. Rhythm and timing in autism: Learning to dance

    Directory of Open Access Journals (Sweden)

    Pat eAmos

    2013-04-01

    Full Text Available In recent years, a significant body of research has focused on challenges to neural connectivity as a key to understanding autism. In contrast to attempts to identify a single static, primarily brain-based deficit, children and adults diagnosed with autism are increasingly perceived as out of sync with their internal and external environments in dynamic ways that must also involve operations of the peripheral nervous systems. The noisiness that seems to occur in both directions of neural flow may help explain challenges to movement and sensing, and ultimately to entrainment with circadian rhythms and social interactions. across the autism spectrum. Profound differences in the rhythm and timing of movement have been tracked to infancy. Difficulties with self-synchrony inhibit praxis, and can disrupt the dance of relationships through which caregiver and child build meaning. Different sensory aspects of a situation may fail to match up; ultimately, intentions and actions themselves may be uncoupled. This uncoupling may help explain the expressions of alienation from the actions of one’s body which recur in the autobiographical autism literature. Multi-modal/cross-modal coordination of different types of sensory information into coherent events may be difficult to achieve because amodal properties (e.g. rhythm and tempo that help unite perceptions are unreliable. One question posed to the connectivity research concerns the role of rhythm and timing in this operation, and whether these can be mobilized to reduce overload and enhance performance. A case is made for developmental research addressing how people with autism actively explore and make sense of their environments. The parent/author recommends investigating approaches such as scaffolding interactions via rhythm, following the person’s lead, slowing the pace, discriminating between intentional communication and stray motor patterns, and organizing information through one sensory mode at

  17. The time course of ethanol tolerance: associative learning

    Directory of Open Access Journals (Sweden)

    J.L.O. Bueno

    2007-11-01

    Full Text Available The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD of ethanol injected intraperitoneally, and the other receiving increasing doses (ID during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone and B (buzzer were presented at "state +" (35 min after drug injection and "state -" (170 min after drug injection, respectively. Conditioned stimuli C (bip and D (white noise were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +" induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -" reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.

  18. A multiomics approach to study the microbiome response to phytoplankton blooms.

    Science.gov (United States)

    Song, Liyan

    2017-06-01

    Phytoplankton blooms are predictable features of marine and freshwater habitats. Despite a good knowledge base of the environmental factors controlling blooms, complex interactions between the bacterial and archaeal communities and phytoplankton bloom taxa are only now emerging. Here, the current research on bacterial community's structural and functional response to phytoplankton blooms is reviewed and discussed and further research is proposed. More attention should be paid on structure and function of autotrophic bacteria and archaea during phytoplankton blooms. A multiomics integration approach is needed to investigate bacterial and archaeal communities' diversity, metabolic diversity, and biogeochemical functions of microbial interactions during phytoplankton blooms.

  19. Physical and biological data collected along the Texas, Mississippi, and Florida Gulf coasts in the Gulf of Mexico as part of the Harmful Algal BloomS Observing System from 19 Aug 1953 to 11 July 2014 (NODC Accession 0120767)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — HABSOS (Harmful Algal BloomS Observing System) is a data collection and distribution system for harmful algal bloom (HAB) information in the Gulf of Mexico. The goal...

  20. Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance

    Institute of Scientific and Technical Information of China (English)

    TAO Bangyi; PAN Delu; MAO Zhihua; SHEN Yuzhang; ZHU Qiankun; CHEN Jianyu

    2013-01-01

    Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang (Yangtze) River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguish-ing dinoflagellate blooms from diatom (Skeletonema costatum) blooms is desired. On the basis of measure-ments of remote sensing reflectance [Rrs(λ)] and inherent optical parameters, the potential of using a mul-tispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R1 =Rrs(560)/Rrs(532) and R2 =Rrs(708)/Rrs(665)], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense: (1) R1 >1.55 and R2 1.75 and R2 ?1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organic matter (CDOM) as well as changes in the backscattering ratio (bbp/bp) on the efficacy of this multispectral approach. Results indicate that the intensity and inherent op-tical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of Rrs(λ), especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of bbp/bp. Since a CDOM(440) in coastal areas of the ECS is typically lower than 1.0 m−1 in most situations, the presence of CDOM does not interfere with this discrimination, even as SCDOM varies from 0.01 to 0.026 nm−1. Despite all of these effects, the dis-crimination of P. donghaiense blooms from diatom blooms based on multispectral

  1. TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

    CERN Document Server

    Hester, Todd

    2013-01-01

    This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

  2. Harmful Algal Blooms of the West Florida Shelf and Campeche Bank: Visualization and Quantification using Remote Sensing Methods

    Science.gov (United States)

    Soto Ramos, Inia Mariel

    % false negatives, similar to those from more complex techniques. The first chapter concludes with a series of recommendations on how to improve the detection techniques and how to take these results a step further into a Gulf wide observing systems for HABs. In chapter two, ocean color techniques were used to examine the extension, evolution and displacement of four Karenia spp. events that occurred in the WFS between 2004 and 2011. Blooms were identified in the imagery using the new Rrs-FLH method and validated using in situ phytoplankton cell counts. The spatial extension of each event was followed in time by delineating the blooms. In 2004 and 2005, the WFS was affected by a series of hurricanes that led to high river discharge and intense sediment resuspension events. Both processes had an impact on HAB occurrence. For example, I tracked a Karenia spp. bloom found in late December 2004 approximately 40-80 km offshore Saint Petersburg, which then expanded reaching an extension of >8000 km2 in February 2005. The bloom weakened in spring 2005 and intensified again in summer reaching >42,000 km 2 after the passage of hurricane Katrina in August 2005. This bloom covered the WFS from Charlotte Harbor to the Florida Panhandle. Two other cases were studied in the WFS. The results of the Hybrid Coordinate Ocean Model from the U.S. Navy aid understanding the dispersal of the blooms. During fall 2011, three field campaigns to study HABs in Mexico were conducted to do an analysis of optical properties and explore the possibility of using ocean color techniques to distinguish between the main phytoplankton blooms in that region. Three main bloom scenarios were observed in the Campeche Bank region: massive diatom blooms, blooms dominated by Scrippsiella spp., and Karenia spp. blooms. The normalized specific phytoplankton absorption spectra were found to be different for Karenia spp. and Scrippsiella sp. blooms. A new technique that combines phytoplankton absorption derived from

  3. The time course of location-avoidance learning in fear of spiders.

    Science.gov (United States)

    Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S

    2016-01-01

    Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.

  4. Time course influences transfer of visual perceptual learning across spatial location.

    Science.gov (United States)

    Larcombe, S J; Kennard, C; Bridge, H

    2017-06-01

    Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Climbing Bloom's taxonomy pyramid: Lessons from a graduate histology course.

    Science.gov (United States)

    Zaidi, Nikki B; Hwang, Charles; Scott, Sara; Stallard, Stefanie; Purkiss, Joel; Hortsch, Michael

    2017-09-01

    Bloom's taxonomy was adopted to create a subject-specific scoring tool for histology multiple-choice questions (MCQs). This Bloom's Taxonomy Histology Tool (BTHT) was used to analyze teacher- and student-generated quiz and examination questions from a graduate level histology course. Multiple-choice questions using histological images were generally assigned a higher BTHT level than simple text questions. The type of microscopy technique (light or electron microscopy) used for these image-based questions did not result in any significant differences in their Bloom's taxonomy scores. The BTHT levels for teacher-generated MCQs correlated positively with higher discrimination indices and inversely with the percent of students answering these questions correctly (difficulty index), suggesting that higher-level Bloom's taxonomy questions differentiate well between higher- and lower-performing students. When examining BTHT scores for MCQs that were written by students in a Multiple-Choice Item Development Assignment (MCIDA) there was no significant correlation between these scores and the students' ability to answer teacher-generated MCQs. This suggests that the ability to answer histology MCQs relies on a different skill set than the aptitude to construct higher-level Bloom's taxonomy questions. However, students significantly improved their average BTHT scores from the midterm to the final MCIDA task, which indicates that practice, experience and feedback increased their MCQ writing proficiency. Anat Sci Educ 10: 456-464. © 2017 American Association of Anatomists. © 2017 American Association of Anatomists.

  6. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    OpenAIRE

    Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

    2010-01-01

    This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...

  7. A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

    Science.gov (United States)

    Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

    2015-10-01

    Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

  8. Learning characteristics of a space-time neural network as a tether skiprope observer

    Science.gov (United States)

    Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

    1993-01-01

    The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

  9. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI)

    Science.gov (United States)

    Alawadi, Fahad

    2010-10-01

    Quantifying ocean colour properties has evolved over the past two decades from being able to merely detect their biological activity to the ability to estimate chlorophyll concentration using optical satellite sensors like MODIS and MERIS. The production of chlorophyll spatial distribution maps is a good indicator of plankton biomass (primary production) and is useful for the tracing of oceanographic currents, jets and blooms, including harmful algal blooms (HABs). Depending on the type of HABs involved and the environmental conditions, if their concentration rises above a critical threshold, it can impact the flora and fauna of the aquatic habitat through the introduction of the so called "red tide" phenomenon. The estimation of chlorophyll concentration is derived from quantifying the spectral relationship between the blue and the green bands reflected from the water column. This spectral relationship is employed in the standard ocean colour chlorophyll-a (Chlor-a) product, but is incapable of detecting certain macro-algal species that float near to or at the water surface in the form of dense filaments or mats. The ability to accurately identify algal formations that sometimes appear as oil spill look-alikes in satellite imagery, contributes towards the reduction of false-positive incidents arising from oil spill monitoring operations. Such algal formations that occur in relatively high concentrations may experience, as in land vegetation, what is known as the "red-edge" effect. This phenomena occurs at the highest reflectance slope between the maximum absorption in the red due to the surrounding ocean water and the maximum reflectance in the infra-red due to the photosynthetic pigments present in the surface algae. A new algorithm termed the surface algal bloom index (SABI), has been proposed to delineate the spatial distributions of floating micro-algal species like for example cyanobacteria or exposed inter-tidal vegetation like seagrass. This algorithm was

  10. Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

    OpenAIRE

    Shi, Peizhi; Chen, Ke

    2017-01-01

    Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

  11. Why Hong Kong students favour more face-to-face classroom time in blended learning

    OpenAIRE

    Henri,James; Lee,Sandra

    2007-01-01

    A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students in...

  12. [Ecological Effects of Algae Blooms Cluster: The Impact on Chlorophyll and Photosynthesis of the Water Hyacinth].

    Science.gov (United States)

    Liu, Guo-feng; He, Jun; Yang, Yi-zhong; Han, Shi-qun

    2015-08-01

    The response of chlorophyll and photosynthesis of water hyacinth leaves in different concentrations of clustered algae cells was studied in the simulation experiment, and the aim was to reveal the mechanism of the death of aquatic plants during algae blooms occurred through studying the physiological changes of the macrophytes, so as to play the full function of the ecological restoration of the plants. And results showed the dissolved oxygen quickly consumed in root zone of aquatic plants after algae blooms gathered and showed the lack of oxygen (DO algae cell died and concentration of DTN in treatment 1 and 2 were 44.49 mg x L(-1) and 111.32 mg x L(-1), and the content of DTP were 2.57 mg x L(-1) and 9.10 mg x L(-1), respectively. The NH4+ -N concentrations were as high as 32.99 mg x L(-1) and 51.22 mg x L(-1), and the root zone with the anoxia, strong reducing, higher nutrients environment had a serious stress effects to the aquatic plants. The macrophytes photosynthesis reduced quickly and the plant body damaged with the intimidation of higher NH4+ -N concentration (average content was 45.6 mg x L(-1)) and hypoxia after algae cell decomposed. The average net photosynthesis rate, leaf transpiration rate of the treatment 2 reduced to 3.95 micromol (M2 x S)(-1), 0.088 micromol x (m2 x s)(-1), and only were 0.18 times, 0.11 times of the control group, respectively, at the end of the experiment, the control group were 22 micromol x (m2 x s)(-1), 0.78 micromol x (M2 x s)(-1). Results indicated the algae bloom together had the irreversible damage to the aquatic plants. Also it was found large amounts of new roots and the old roots were dead in the treatment 1, but roots were all died in the treatment 2, and leaves were yellow and withered. Experiment results manifested that the serious environment caused by the algae blooms together was the main reason of the death of aquatic plants during the summer. So in the practice of ecological restoration, it should avoid the

  13. Localization and Tracking of Submerged Phytoplankton Bloom Patches by an Autonomous Underwater Vehicle

    Science.gov (United States)

    Godin, M. A.; Ryan, J. P.; Zhang, Y.; Bellingham, J. G.

    2012-12-01

    Observing plankton in their drifting frame of reference permits effective studies of marine ecology from the perspective of microscopic life itself. By minimizing variation caused simply by advection, observations in a plankton-tracking frame of reference focus measurement capabilities on the processes that influence the life history of populations. Further, the patchy nature of plankton populations motivates use of sensor data in real-time to resolve patch boundaries and adapt observing resources accordingly. We have developed capabilities for population-centric plankton observation and sampling by autonomous underwater vehicles (AUVs). Our focus has been on phytoplankton populations, both because of their ecological significance - as the core of the oceanic food web and yet potentially harmful under certain bloom conditions, as well as the accessibility of their signal to simple optical sensing. During the first field deployment of these capabilities in 2010, we tracked a phytoplankton patch containing toxigenic diatoms and found that their toxicity correlated with exposure to resuspended sediments. However, this first deployment was labor intensive as the AUV drove in a pre-programmed pattern centered around a patch-marking drifter; it required a boat deployment of the patch-marking drifter and required full-time operators to periodically estimate of the position of the patch with respect to the drifter and adjust the AUV path accordingly. In subsequent field experiments during 2011 and 2012, the Tethys-class long-range AUVs ran fully autonomous patch tracking algorithms which detected phytoplankton patches and continually updated estimates of each patch center by driving adaptive patterns through the patch. Iterations of the algorithm were generated to overcome the challenges of tracking advecting and evolving patches while minimizing human involvement in vehicle control. Such fully autonomous monitoring will be necessary to perform long-term in

  14. A Study of Time Spent Working at Learning Centers. Technical Report #17.

    Science.gov (United States)

    Omori, Sharon; And Others

    This study examined the proportion of time children in the Kamehameha Early Education Program schools spend at actual school work in learning centers. Systematic time-sampled observations using multiple observers were conducted in December-January and again in March-April. The subjects, 12 children (6 kindergarteners and 6 first graders) were…

  15. An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

    Science.gov (United States)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    2007-01-01

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

  16. Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance

    Science.gov (United States)

    Mei, Jianyang

    2016-01-01

    This research project uses a large research university in the Midwest as a research site to explore the time management skills of international students and analyzes how using the Course Hack, an online Learning Management System (LMS) calendar tool, improves participants' time management skills and positively impacts their academic performance,…

  17. Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

    Science.gov (United States)

    Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

    2016-01-01

    The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

  18. Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story

    Science.gov (United States)

    Llinás, Rodolfo R

    2011-01-01

    Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816

  19. Time-place learning over a lifetime : Absence of memory loss in trained old mice

    NARCIS (Netherlands)

    Mulder, Cornelis K; Reckman, Gerlof A R; Gerkema, Menno P; van der Zee, Eddy A

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an

  20. A longitudinal study on time perspectives: relations with academic delay of gratification and learning environment

    NARCIS (Netherlands)

    Peetsma, T.; Schuitema, J.; van der Veen, I.

    2012-01-01

    After they start secondary school (at age 12 in the Netherlands), students' time perspectives on school and professional career and self-regulated learning decrease, while their perspectives on leisure increase. We aimed to investigate relations in the developments in time perspectives and delay of

  1. An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

    NARCIS (Netherlands)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

  2. Time-Place Learning over a Lifetime: Absence of Memory Loss in Trained Old Mice

    Science.gov (United States)

    Mulder, Cornelis K.; Reckman, Gerlof A. R.; Gerkema, Menno P.; Van der Zee, Eddy A.

    2015-01-01

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an experience-based daily schedule. Mice were tested for…

  3. Real-time yield estimation based on deep learning

    Science.gov (United States)

    Rahnemoonfar, Maryam; Sheppard, Clay

    2017-05-01

    Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

  4. Examining the Effect of Time Constraint on the Online Mastery Learning Approach towards Improving Postgraduate Students' Achievement

    Science.gov (United States)

    Ee, Mong Shan; Yeoh, William; Boo, Yee Ling; Boulter, Terry

    2018-01-01

    Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies--personalised system of instructions and learning for mastery (LFM)--by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate…

  5. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  6. On learning science and pseudoscience from prime-time television programming

    Science.gov (United States)

    Whittle, Christopher Henry

    The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels

  7. Effects of fertilizers used in agricultural fields on algal blooms

    DEFF Research Database (Denmark)

    Chakraborty, Subhendu; Tiwari, P. K.; Sasmal, S. K.

    2017-01-01

    of factors and from observation it is difficult to identify the most important one. In the present paper, using a mathematical model we compare the effects of three human induced factors (fertilizer input in agricultural field, eutrophication due to other sources than fertilizers, and overfishing......) on the bloom dynamics and DO level. By applying a sophisticated sensitivity analysis technique, we found that the increasing use of fertilizers in agricultural field causes more rapid algal growth and decreases DO level much faster than eutrophication from other sources and overfishing. We also look...... at the mechanisms how fertilizer input rate affects the algal bloom dynamics and DO level. The model can be helpful for the policy makers in determining the influential factors responsible for the bloom formation....

  8. Why Hong Kong students favour more face-to-face classroom time in blended learning

    Directory of Open Access Journals (Sweden)

    James Henri

    Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.

  9. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

    Science.gov (United States)

    Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

    2017-08-01

    When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or

  10. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  11. Retrieved bacteria from Noctiluca miliaris (green) bloom of the northeastern Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Basu, S.; Matondkar, S.G.P.; Furtado, I.

    In recent years, seasonal blooms of the dinoflagellate Noctiluca miliaris have appeared in the open-waters of the northern Arabian Sea (NAS). This study provides the first characterization of bacteria from a seasonal bloom of green Noctiluca of NAS...

  12. Deep carbon export from a Southern Ocean iron-fertilized diatom bloom

    Digital Repository Service at National Institute of Oceanography (India)

    Smetacek, V.; Klaas, C.; Strass, V.H.; Assmy, P.; Montresor, M.; Cisewski, B.; Savoye, N.; Webb, A.; d’Ovidio, F.; Arrieta, J.M.; Bathmann, U.; Bellerby, R.; Berg, G.M.; Croot, P.; Gonzalez, S.; Henjes, J.; Herndl, G.J.; Hoffmann, L.J.; Leach, H.; Losch, M.; Mills, M.M.; Neill, C.; Peeken, I.; Rottgers, R.; Sachs, O.; Sauter, E.; Schmidt, M.M.; Schwarz, J.; Terbruggen, A.; Wolf-Gladrow, D.

    Fertilization of the ocean by adding iron compounds has induced diatom-dominated phytoplankton blooms accompanied by considerable carbon dioxide drawdown in the ocean surface layer. However, because the fate of bloom biomass could not be adequately...

  13. Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-05-01

    We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

  14. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

    Directory of Open Access Journals (Sweden)

    Mingchen Yao

    2015-01-01

    Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

  15. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  16. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  17. Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

    Science.gov (United States)

    Bunger, Alicia C; Lengnick-Hall, Rebecca

    Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future

  18. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    International Nuclear Information System (INIS)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

    2016-01-01

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  19. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

    2016-06-15

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  20. Time to learn: the outlook for renewal of patient-centred education in the digital age.

    Science.gov (United States)

    Glick, T H; Moore, G T

    2001-05-01

    Major forces in society and within health systems are fragmenting patient care and clinical learning. The distancing of physician and trainee from the patient undermines learning about the patient-doctor relationship. The disconnection of care and learning from one successive venue to another impedes the ability of trainees to learn about illness longitudinally. As a conceptual piece, our methods have been those of witnessing the experiences of patients, practitioners, and students over time and observing the impact of fragmented systems and changing expectations on care and learning. We have reflected on the opportunities created by digital information systems and interactive telemedicine to help renew essential relationships. Although there is, as yet, little in the literature on educational or health outcomes of this kind of technological enablement, we anticipate opportunities for a renewed focus on the patient in that patient's own space and time. Multimedia applications can achieve not only real-time connections, but can help construct a "virtual patient" as a platform for supervision and assessment, permitting preceptors to evaluate trainee-patient interactions, utilization of Web-based data and human resources, and on-line professionalism. Just as diverse elements in society are capitalizing upon digital technology to create advantageous relationships, all of the elements in the complex systems of health care and medical training can be better connected, so as to put the patient back in the centre of care and the trainee's ongoing relationship to the patient back in the centre of education.

  1. Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

    Directory of Open Access Journals (Sweden)

    Pekka Ihanainen

    2011-11-01

    Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.

  2. Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

    Directory of Open Access Journals (Sweden)

    Wulfram eGerstner

    2016-01-01

    Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  3. Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

    Science.gov (United States)

    Le, Long N; Jones, Douglas L

    2018-03-01

    Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

  4. Pushing Critical Thinking Skills With Multiple-Choice Questions: Does Bloom's Taxonomy Work?

    Science.gov (United States)

    Zaidi, Nikki L Bibler; Grob, Karri L; Monrad, Seetha M; Kurtz, Joshua B; Tai, Andrew; Ahmed, Asra Z; Gruppen, Larry D; Santen, Sally A

    2018-06-01

    Medical school assessments should foster the development of higher-order thinking skills to support clinical reasoning and a solid foundation of knowledge. Multiple-choice questions (MCQs) are commonly used to assess student learning, and well-written MCQs can support learner engagement in higher levels of cognitive reasoning such as application or synthesis of knowledge. Bloom's taxonomy has been used to identify MCQs that assess students' critical thinking skills, with evidence suggesting that higher-order MCQs support a deeper conceptual understanding of scientific process skills. Similarly, clinical practice also requires learners to develop higher-order thinking skills that include all of Bloom's levels. Faculty question writers and examinees may approach the same material differently based on varying levels of knowledge and expertise, and these differences can influence the cognitive levels being measured by MCQs. Consequently, faculty question writers may perceive that certain MCQs require higher-order thinking skills to process the question, whereas examinees may only need to employ lower-order thinking skills to render a correct response. Likewise, seemingly lower-order questions may actually require higher-order thinking skills to respond correctly. In this Perspective, the authors describe some of the cognitive processes examinees use to respond to MCQs. The authors propose that various factors affect both the question writer and examinee's interaction with test material and subsequent cognitive processes necessary to answer a question.

  5. Learning of temporal motor patterns: An analysis of continuous vs. reset timing

    Directory of Open Access Journals (Sweden)

    Rodrigo eLaje

    2011-10-01

    Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.

  6. Annually recurrent macroalgal blooms (Ulva prolifera) resulting in the world's largest green-tides caused by expansion of coastal aquaculture in the Yellow Sea off China

    Science.gov (United States)

    Keesing, John; Liu, Dongyan

    2013-04-01

    The largest macroalgal blooms ever recorded occurred in the Yellow Sea of China in 2008 and 2009 and resulted in extensive green tides along the Shandong Province coastline, including at Qingdao. At their peak these Ulva prolifera blooms covered more than 4,000 km2 and affected 40,000 km2. A smaller bloom was recorded in 2007, but not earlier. Since then massive blooms have occurred annually in summer from 2008 to 2012. Using remote sensing methods, we tracked the source of the 2008 and 2009 blooms to an area along the Jiangsu Province coastline near Yancheng, over 200 km south of Qingdao, where there had been rapid expansion of Porphyra aquaculture to as much as 13 km offshore, prior to the appearance of the first bloom in 2007. Porphyra is grown on rafts which can become heavily fouled with U. prolifera which is disposed of into the sea when the Porphyra is harvested. The timing of the blooms occurred post the April harvest period when daily tidal ranges in this region can be in excess of 7 m. This provides the mechanism for transportation of the floating algae offshore and into the warm nutrient rich waters of the Yellow Sea where it grows rapidly forming large patches. As the patches of algae grow and join, they gradually move north, as a result of wind driven surface currents that prevail in the Yellow Sea in summer, ultimately washing ashore on the Shandong Peninsula. We present a range of oceanographic, biological, ecological and genetic data to support the hypothesis that Porphyra aquaculture provides the source biomass for the Yellow Sea green-tides. Improved aquaculture waste disposal methods in the southern area of Jiangsu Province are likely to reduce or prevent the Yellow Sea green tides and present a feasible solution to a recurrent problem.

  7. A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    Science.gov (United States)

    Halloran, John T; Rocke, David M

    2018-05-04

    Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

  8. Friend or Foe: Variability in How Sea Ice Can Both Hinder and Enhance Phytoplankton Blooms Across the Southern Ocean

    Science.gov (United States)

    Rohr, T.

    2016-02-01

    Globally, a suite of physical and biogeochemical controls govern the structure, size, and timing of seasonal phytoplankton blooms. In the Southern Ocean, the introduction of seasonal sea ice provides an additional constraining factor. From a bottom-up perspective, a reduction in sea ice can both enhance bloom development by permitting greater levels of surface PAR uninhibited by ice and suppress a bloom when reduced fresh melt-water inputs and increased vulnerability to wind stress combine to create deeper mixed layers and decrease depth integrated light availability. Regions along the Western Antarctic Peninsula have already seen a contradictory response to reduced ice cover, with enhanced summertime chlorophyll concentrations in the South, and large declines to the North. This dichotomy is thought to arise from differences in the interannual mean sea ice state, with extensively ice covered regions benefiting from reduced coverage and more sparsely covered regions hindered by further reductions. The questions arises: 1) At what threshold does a reduction in sea ice transition from amplifying blooms to suppressing them? 2) How do additional environmental considerations such as nutrient availability and trophic interactions complicate this transition? Here, we combine remote sensing observations and in-situ data (from PAL LTER) with a hierarchy of 1-D water column and global general circulation (CESM) models to access the variability in how regional differences in mean ice state combine with other environmental forcings to dictate how interannual variability (or long term trends) in ice coverage will affect bloom structure, size and dynamics. In doing so we will gain a better understanding of how predicted changes in sea ice will effect Southern Ocean productivity, which of course will have important consequences in the global carbon cycle and sustainability of healthy marine ecosystems.

  9. Then the Wilderness Shall Bloom like a Rosy Bower

    DEFF Research Database (Denmark)

    Nielsen, Kirsten

    2014-01-01

    intertextual connections to the rest of the book. In my article, I have analysed how the Danish poet N.F.S. Grundtvig reworks Isa 35 in his hymn “Then the wilderness shall bloom like a rosy bower”, and how he reinterprets the wild animals as the Enemy (the Devil). In my view, the animals in Isa 35 have...

  10. Physical processes contributing to harmful algal blooms in Saldanha ...

    African Journals Online (AJOL)

    Since 1994, disruption of harvesting as a result of the presence of harmful algal species has been a regular late-summer phenomenon. Toxic blooms that are ultimately advected into the bay develop on the continental shelf to the north between 32°S and St Helena Bay, a region characterized by favourable conditions for ...

  11. Detection of macroalgae blooms by complex SAR imagery

    International Nuclear Information System (INIS)

    Shen, Hui; Perrie, William; Liu, Qingrong; He, Yijun

    2014-01-01

    Highlights: • Complex SAR imagery enables better recognition of macroalgae patches. • Combination of different information in SAR matrix forms new index factors. • Proposed index factors contribute to unsupervised recognition of macroalgae. -- Abstract: Increased frequency and enhanced damage to the marine environment and to human society caused by green macroalgae blooms demand improved high-resolution early detection methods. Conventional satellite remote sensing methods via spectra radiometers do not work in cloud-covered areas, and therefore cannot meet these demands for operational applications. We present a methodology for green macroalgae bloom detection based on RADARSAT-2 synthetic aperture radar (SAR) images. Green macroalgae patches exhibit different polarimetric characteristics compared to the open ocean surface, in both the amplitude and phase domains of SAR-measured complex radar backscatter returns. In this study, new index factors are defined which have opposite signs in green macroalgae-covered areas, compared to the open water surface. These index factors enable unsupervised detection from SAR images, providing a high-resolution new tool for detection of green macroalgae blooms, which can potentially contribute to a better understanding of the mechanisms related to outbreaks of green macroalgae blooms in coastal areas throughout the world ocean

  12. Context discovery using attenuated Bloom codes: model description and validation

    NARCIS (Netherlands)

    Liu, F.; Heijenk, Geert

    A novel approach to performing context discovery in ad-hoc networks based on the use of attenuated Bloom filters is proposed in this report. In order to investigate the performance of this approach, a model has been developed. This document describes the model and its validation. The model has been

  13. Apples, Bloom, and Creativity: The ABC's of Reading Alphabet Books.

    Science.gov (United States)

    Taylor, Mary Agnes

    Benjamin Bloom's taxonomy of educational objectives (knowledge, comprehension, application, analysis, synthesis, and evaluation) combined with commonly accepted steps of the creative process (gathering material, reflection, inspiration, first draft or model, and evaluation) can be used to explore some of the possibilities of working with alphabet…

  14. Fungal parasitism: life cycle, dynamics and impact on cyanobacterial blooms.

    Directory of Open Access Journals (Sweden)

    Mélanie Gerphagnon

    Full Text Available Many species of phytoplankton are susceptible to parasitism by fungi from the phylum Chytridiomycota (i.e. chytrids. However, few studies have reported the effects of fungal parasites on filamentous cyanobacterial blooms. To investigate the missing components of bloom ecosystems, we examined an entire field bloom of the cyanobacterium Anabaena macrospora for evidence of chytrid infection in a productive freshwater lake, using a high resolution sampling strategy. A. macrospora was infected by two species of the genus Rhizosiphon which have similar life cycles but differed in their infective regimes depending on the cellular niches offered by their host. R. crassum infected both vegetative cells and akinetes while R. akinetum infected only akinetes. A tentative reconstruction of the developmental stages suggested that the life cycle of R. crassum was completed in about 3 days. The infection affected 6% of total cells (and 4% of akinètes, spread over a maximum of 17% of the filaments of cyanobacteria, in which 60% of the cells could be parasitized. Furthermore, chytrids may reduce the length of filaments of Anabaena macrospora significantly by "mechanistic fragmentation" following infection. All these results suggest that chytrid parasitism is one of the driving factors involved in the decline of a cyanobacteria blooms, by direct mortality of parasitized cells and indirectly by the mechanistic fragmentation, which could weaken the resistance of A. macrospora to grazing.

  15. Monitoring of harmful algal blooms along the Norwegian coast using ...

    African Journals Online (AJOL)

    A Norwegian monitoring system for harmful algal blooms, consisting of an Observer Network, the State Food Hygiene Control Agency, the Oceanographic Company of Norway, the Institute of Marine Research and the Directorate for Fisheries, is reviewed. Potentially harmful algae on the Norwegian coast are found primarily ...

  16. Hydrodynamic control of microphytoplankton bloom in a coastal sea

    Indian Academy of Sciences (India)

    K Narasimha Murty

    2017-08-31

    Aug 31, 2017 ... surface water to depths in regions where there is no barrier layer at the ... ent availability (and light) alone does not give place to blooms in the ...... ics in a coastal upwelling system off southwestern Africa;. Deep Sea Res.

  17. Termination of a toxic Alexandrium bloom with hydrogen peroxide

    NARCIS (Netherlands)

    Burson, A.; Matthijs, H.C.P.; Bruijne, de W.; Talens, R.; Hoogenboom, L.A.P.; Gerssen, A.; Visser, P.M.; Stomp, M.; Steur, K.; Scheppingen, van Y.; Huisman, J.

    2014-01-01

    The dinoflagellate Alexandrium ostenfeldii is a well-known harmful algal species that can potentially cause paralytic shellfish poisoning (PSP). Usually A. ostenfeldii occurs in low background concentrations only, but in August of 2012 an exceptionally dense bloom of more than 1 million cells L-1

  18. Biological control of Microcystis dominated harmful algal blooms ...

    African Journals Online (AJOL)

    Freshwater resources are now threatened by the presence and increase of harmful algal blooms (HAB) all over the world. The HABs are sometimes a direct result of anthropogenic pollution entering water bodies, such as partially treated nutrient-rich effluents and the leaching of fertilisers and animal wastes. The impact of ...

  19. The Unfortunate Consequences of Bloom's Taxonomy

    Science.gov (United States)

    Case, Roland

    2013-01-01

    The sequenced levels of thinking articulated in Bloom's original taxonomy (or in the multitude of subsequent variations) is the most widely known list in education. In addition to enduring popularity, it is arguably one of the most destructive theories in education. In this article, the author explains what makes it so damaging and how…

  20. Mitigating cyanobacterial blooms: how effective are 'effective microorganisms'?

    NARCIS (Netherlands)

    Lürling, M.F.L.L.W.; Tolman, Y.; Euwe, M.

    2009-01-01

    This study examined the effects of 'Effective Microorganisms (EM)' on the growth of cyanobacteria, and their ability to terminate cyanobacterial blooms. The EM was tested in the form of 'mudballs' or 'Bokashi-balls', and as a suspension (EM-A) in laboratory experiments. No growth inhibition was

  1. A Social Practice Theory of Learning and Becoming across Contexts and Time

    Science.gov (United States)

    Penuel, William R.; DiGiacomo, Daniela K.; Van Horne, Katie; Kirshner, Ben

    2016-01-01

    This paper presents a social practice theory of learning and becoming across contexts and time. Our perspective is rooted in the Danish tradition of critical psychology (Dreier, 1997; Mørck & Huniche, 2006; Nissen, 2005), and we use social practice theory to interpret the pathway of one adolescent whom we followed as part of a longitudinal…

  2. Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

    Science.gov (United States)

    Liberatore, Matthew W.

    2013-01-01

    The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

  3. Assessment of Stand-Alone Displays for Time Management in a Creativity-Driven Learning Environment

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren

    2017-01-01

    This paper considers the pros and cons of stand-alone displays, analog (e.g. billboards, blackboards, whiteboards, large pieces of paper etc.) as well as digital (e.g. large shared screens, digital whiteboards or similar), as tools for time management processes in a creativity-driven learning...

  4. Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

    Science.gov (United States)

    Paterson, John W.

    2017-01-01

    Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

  5. Nuclear power plant monitoring using real-time learning neural network

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

    1994-01-01

    In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

  6. Focusing on Student Learning to Guide the Use of Staff Time

    Science.gov (United States)

    Bates, Imelda; Baume, David; Assinder, Susan

    2010-01-01

    The paper develops and illustrates a model for designing courses. The model gives explicit attention to educational considerations, principally to the importance of active, goal-directed student learning. It also explores economic considerations, principally how to make the best possible use of the time of the teacher in planning and running the…

  7. Pre-Service Post Graduate Teachers' First Time Experience with Constructivist Learning Environment (CLE) Using MOODLE

    Science.gov (United States)

    Boopathiraj, C.; Chellamani, K.

    2015-01-01

    The aim of this study is to enlighten and discuss Post Graduate student teachers' first time experiences and their level of satisfaction with the use of Moodle Learning Management System (LMS) during their "Research Methods in Education" course offered online. This study investigated 30 pre-service Post Graduate student teachers' to…

  8. Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

    Science.gov (United States)

    Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

    2016-01-01

    An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

  9. Examining the Relations of Time Management and Procrastination within a Model of Self-Regulated Learning

    Science.gov (United States)

    Wolters, Christopher A.; Won, Sungjun; Hussain, Maryam

    2017-01-01

    The primary goal of this study was to investigate whether college students' academic time management could be used to understand their engagement in traditional and active forms of procrastination within a model of self-regulated learning. College students (N = 446) completed a self-report survey that assessed motivational and strategic aspects of…

  10. Cerebral activation related to implicit sequence learning in a Double Serial Reaction Time task

    NARCIS (Netherlands)

    van der Graaf, FHCE; Maguire, RP; Leenders, KL; de Jong, BM

    2006-01-01

    Using functional magnetic resonance imaging (fMRI), we examined the distribution of cerebral activations related to implicitly learning a series of fixed stimulus-response combinations. In a novel - bimanual - variant of the Serial Reaction Time task (SRT), simultaneous finger movements of the two

  11. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

    Science.gov (United States)

    Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

    2018-04-09

    Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  12. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  13. Autonomous learning by simple dynamical systems with a discrete-time formulation

    Science.gov (United States)

    Bilen, Agustín M.; Kaluza, Pablo

    2017-05-01

    We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

  14. Tropical cyanobacterial blooms: a review of prevalence, problem taxa, toxins and influencing environmental factors

    Directory of Open Access Journals (Sweden)

    Maxine A.D. Mowe

    2014-12-01

    Full Text Available Toxic cyanobacterial blooms are a major issue in freshwater systems in many countries. The potentially toxic species and their ecological causes are likely to be different in tropical zones from those in temperate water bodies; however, studies on tropical toxic cyanobacterial blooms are sporadic and currently there is no global synthesis. In this review, we examined published information on tropical cyanobacterial bloom occurrence and toxin production to investigate patterns in their growth and distribution. Microcystis was the most frequently occurring bloom genus throughout tropical Asia, Africa and Central America, while Cylindrospermopsis and Anabaena blooms occurred in various locations in tropical Australia, America and Africa. Microcystis blooms were more prevalent during the wet season while Cylindrospermopsis blooms were more prevalent during the dry period. Microcystin was the most encountered toxin throughout the tropics. A meta-analysis of tropical cyanobacterial blooms showed that Microcystis blooms were more associated with higher total nitrogen concentrations, while Cylindrospermopsis blooms were more associated with higher maximum temperatures. Meta-analysis also showed a positive linear relationship between levels of microcystin and N:P (nitrate:phosphate ratio. Tropical African Microcystis blooms were found to have the lowest microcystin levels in relation to biomass and N:P (nitrate:phosphate compared to tropical Asian, Australian and American blooms. There was also no significant correlation between microcystin concentration and cell concentration for tropical African blooms as opposed to tropical Asian and American blooms. Our review illustrates that some cyanobacteria and toxins are more prevalent in tropical areas. While some tropical countries have considerable information regarding toxic blooms, others have few or no reported studies. 

  15. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  16. Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

    Science.gov (United States)

    Vaccaro, James M.; Yaworsky, Paul S.

    1999-06-01

    Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

  17. Children benefit differently from night- and day-time sleep in motor learning.

    Science.gov (United States)

    Yan, Jin H

    2017-08-01

    Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

    Science.gov (United States)

    Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

    2014-04-30

    Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

    Science.gov (United States)

    Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R

    2017-09-01

    It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  20. Experiments with Online Reinforcement Learning in Real-Time Strategy Games

    DEFF Research Database (Denmark)

    Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis

    2009-01-01

    Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....

  1. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Directory of Open Access Journals (Sweden)

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

  2. Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

    Directory of Open Access Journals (Sweden)

    Ayse Yarali

    Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

  3. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  4. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  5. Analyzing Beach Recreationists’ Preferences for the Reduction of Jellyfish Blooms: Economic Results from a Stated-Choice Experiment in Catalonia, Spain

    Science.gov (United States)

    Nunes, Paulo A. L. D.; Loureiro, Maria L.; Piñol, Laia; Sastre, Sergio; Voltaire, Louinord; Canepa, Antonio

    2015-01-01

    Jellyfish outbreaks and their consequences appear to be on the increase around the world, and are becoming particularly relevant in the Mediterranean. No previous studies have quantified tourism losses caused by jellyfish outbreaks. We used a stated-choice questionnaire and a Random Utility Model to estimate the amount of time respondents would be willing to add to their journey, in terms of reported extra travel time, in order to reduce the risk of encountering jellyfish blooms in the Catalan coast. The estimation results indicated that the respondents were willing to spend on average an additional 23.8% of their travel time to enjoy beach recreation in areas with a lower risk of jellyfish blooms. Using as a reference the opportunity cost of time, we found that the subsample of individuals who made a trade-off between the disutility generated by travelling longer in order to lower the risk of jellyfish blooms, and the utility gained from reducing this risk, are willing to pay on average €3.20 per beach visit. This estimate, combined with the respondents’ mean income, yielded annual economic gains associated with reduction of jellyfish blooms on the Catalan coast around €422.57 million, or about 11.95% of the tourism expenditures in 2012. From a policy-making perspective, this study confirms the importance of the economic impacts of jellyfish blooms and the need for mitigation strategies. In particular, providing daily information using social media applications or other technical devices may reduce these social costs. The current lack of knowledge about jellyfish suggests that providing this information to beach recreationists may be a substantially effective policy instrument for minimising the impact of jellyfish blooms. PMID:26053674

  6. Analyzing Beach Recreationists' Preferences for the Reduction of Jellyfish Blooms: Economic Results from a Stated-Choice Experiment in Catalonia, Spain.

    Science.gov (United States)

    Nunes, Paulo A L D; Loureiro, Maria L; Piñol, Laia; Sastre, Sergio; Voltaire, Louinord; Canepa, Antonio

    2015-01-01

    Jellyfish outbreaks and their consequences appear to be on the increase around the world, and are becoming particularly relevant in the Mediterranean. No previous studies have quantified tourism losses caused by jellyfish outbreaks. We used a stated-choice questionnaire and a Random Utility Model to estimate the amount of time respondents would be willing to add to their journey, in terms of reported extra travel time, in order to reduce the risk of encountering jellyfish blooms in the Catalan coast. The estimation results indicated that the respondents were willing to spend on average an additional 23.8% of their travel time to enjoy beach recreation in areas with a lower risk of jellyfish blooms. Using as a reference the opportunity cost of time, we found that the subsample of individuals who made a trade-off between the disutility generated by travelling longer in order to lower the risk of jellyfish blooms, and the utility gained from reducing this risk, are willing to pay on average €3.20 per beach visit. This estimate, combined with the respondents' mean income, yielded annual economic gains associated with reduction of jellyfish blooms on the Catalan coast around €422.57 million, or about 11.95% of the tourism expenditures in 2012. From a policy-making perspective, this study confirms the importance of the economic impacts of jellyfish blooms and the need for mitigation strategies. In particular, providing daily information using social media applications or other technical devices may reduce these social costs. The current lack of knowledge about jellyfish suggests that providing this information to beach recreationists may be a substantially effective policy instrument for minimising the impact of jellyfish blooms.

  7. Analyzing Beach Recreationists' Preferences for the Reduction of Jellyfish Blooms: Economic Results from a Stated-Choice Experiment in Catalonia, Spain.

    Directory of Open Access Journals (Sweden)

    Paulo A L D Nunes

    Full Text Available Jellyfish outbreaks and their consequences appear to be on the increase around the world, and are becoming particularly relevant in the Mediterranean. No previous studies have quantified tourism losses caused by jellyfish outbreaks. We used a stated-choice questionnaire and a Random Utility Model to estimate the amount of time respondents would be willing to add to their journey, in terms of reported extra travel time, in order to reduce the risk of encountering jellyfish blooms in the Catalan coast. The estimation results indicated that the respondents were willing to spend on average an additional 23.8% of their travel time to enjoy beach recreation in areas with a lower risk of jellyfish blooms. Using as a reference the opportunity cost of time, we found that the subsample of individuals who made a trade-off between the disutility generated by travelling longer in order to lower the risk of jellyfish blooms, and the utility gained from reducing this risk, are willing to pay on average €3.20 per beach visit. This estimate, combined with the respondents' mean income, yielded annual economic gains associated with reduction of jellyfish blooms on the Catalan coast around €422.57 million, or about 11.95% of the tourism expenditures in 2012. From a policy-making perspective, this study confirms the importance of the economic impacts of jellyfish blooms and the need for mitigation strategies. In particular, providing daily information using social media applications or other technical devices may reduce these social costs. The current lack of knowledge about jellyfish suggests that providing this information to beach recreationists may be a substantially effective policy instrument for minimising the impact of jellyfish blooms.

  8. Developing Mentors: Adult participation, practices, and learning in an out-of-school time STEM program

    Science.gov (United States)

    Scipio, Deana Aeolani

    This dissertation examines learning within an out-of-school time (OST) Science, Technology, Engineering, and Mathematics (STEM) broadening participation program. The dissertation includes an introduction, three empirical chapters (written as individual articles), and a conclusion. The dissertation context is a chemical oceanography OST program for middle school students called Project COOL---Chemical Oceanography Outside the Lab. The program was a collaboration between middle school OST programming, a learning sciences research laboratory, and a chemical oceanography laboratory. Both labs were located at a research-based university in the Pacific Northwest of the United States. Participants include 34 youth, 12 undergraduates, and five professional scientists. The dissertation data corpus includes six years of ethnographic field notes across three field sites, 400 hours of video and audio recordings, 40 hours of semi-structured interviews, and more than 100 participant generated artifacts. Analysis methods include comparative case analysis, cognitive mapping, semiotic cluster analysis, video interaction analysis, and discourse analysis. The first empirical article focuses on synthesizing productive programmatic features from four years of design-based research.. The second article is a comparative case study of three STEM mentors from non-dominant communities in the 2011 COOL OST Program. The third article is a comparative case study of undergraduates learning to be mentors in the 2014 COOL OST Program. Findings introduce Deep Hanging as a theory of learning in practice. Deep Hanging entails authentic tasks in rich contexts, providing access, capitalizing on opportunity, and building interpersonal relationships. Taken together, these three chapters illuminate the process of designing a rich OST learning environment and the kinds of learning in practice that occurred for adult learners learning to be mentors through their participation in the COOL OST program. In

  9. Real time eye tracking using Kalman extended spatio-temporal context learning

    Science.gov (United States)

    Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

    2017-06-01

    Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

  10. Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

    Science.gov (United States)

    Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

    2009-11-03

    Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

  11. Phytoplankton dynamics in contrasting early stage North Atlantic spring blooms: composition, succession, and potential drivers

    DEFF Research Database (Denmark)

    Daniels, C.J.; Poulton, A. J.; Esposito, M.

    2015-01-01

    The spring bloom is a key annual event in the phenology of pelagic ecosystems, making a major contribution to the oceanic biological carbon pump through the production and export of organic carbon. However, there is little consensus as to the main drivers of spring bloom formation, exacerbated......) of the 2012 North Atlantic spring bloom. The plankton composition and characteristics of the initial stages of the bloom were markedly different between the two basins. The Iceland Basin (ICB) appeared well mixed to > 400 m, yet surface chlorophyll a (0.27–2.2 mg m–3) and primary production (0.06–0.66 mmol C...... suggest micro-zooplankton grazing, potentially coupled with the lack of a seed population of bloom forming diatoms, was restricting diatom growth in the NWB, and that large diatoms may be absent in NWB spring blooms. Despite both phytoplankton communities being in the early stages of bloom formation...

  12. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

    Science.gov (United States)

    Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

    2018-02-01

    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

  13. Adjustment to subtle time constraints and power law learning in rapid serial visual presentation

    Directory of Open Access Journals (Sweden)

    Jacqueline Chakyung Shin

    2015-11-01

    Full Text Available We investigated whether attention could be modulated through the implicit learning of temporal information in a rapid serial visual presentation (RSVP task. Participants identified two target letters among numeral distractors. The stimulus-onset asynchrony immediately following the first target (SOA1 varied at three levels (70, 98, and 126 ms randomly between trials or fixed within blocks of trials. Practice over three consecutive days resulted in a continuous improvement in the identification rate for both targets and attenuation of the attentional blink (AB, a decrement in target (T2 identification when presented 200-400 ms after another target (T1. Blocked SOA1s led to a faster rate of improvement in RSVP performance and more target order reversals relative to random SOA1s, suggesting that the implicit learning of SOA1 positively affected performance. The results also reveal power law learning curves for individual target identification as well as the reduction in the AB decrement. These learning curves reflect the spontaneous emergence of skill through subtle attentional modulations rather than general attentional distribution. Together, the results indicate that implicit temporal learning could improve high level and rapid cognitive processing and highlights the sensitivity and adaptability of the attentional system to subtle constraints in stimulus timing.

  14. Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time

    Directory of Open Access Journals (Sweden)

    Fuxing Wang

    2016-11-01

    Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.

  15. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  16. A reading list for Bill Gates--and you. A conversation with literary critic Harold Bloom. Interview by Diane L. Coutu.

    Science.gov (United States)

    Bloom, H

    2001-05-01

    In today's technology-driven world, who has time to pick up a 400-page novel? Most executives don't--they have urgent e-mails to answer, training seminars to attend, meetings to lead, and trade publications to scan. But according to Harold Bloom, one of America's most influential scholars, they should make time in their hectic schedules to read great works. In a wide-ranging conversation with HBR senior editor Diane Coutu, Bloom discusses the importance of literature: every individual--regardless of profession--needs to stretch his or her mind and reflect now and again on the human condition. "By reading great imaginative literature, you can prepare yourself for surprise and even get a kind of strength that welcomes and exploits the unexpected," he says. Because there are so many great works and there is so little time, Bloom presents a reading list for busy executives. Shakespeare's King Lear can teach businesspeople about change. Ralph Waldo Emerson's essays capture the ethos of the American spirit--individualism and inventiveness. Bloom says Sigmund Freud's conceptions "form the only Western mythology that contemporary intellectuals have in common." And people will never fully understand some aspects of themselves until they read Miguel de Cervantes's Don Quixote. In short, Bloom believes the humanities have much to offer businesspeople: great books broaden their awareness and their range of sensibility, he says. But reading literature will not make businesspeople more moral, he cautions. Bloom also discusses other topics such as how to read well, the state of popular fiction, the role of irony, and the subject of change.

  17. Current-oriented swimming by jellyfish and its role in bloom maintenance.

    Science.gov (United States)

    Fossette, Sabrina; Gleiss, Adrian Christopher; Chalumeau, Julien; Bastian, Thomas; Armstrong, Claire Denise; Vandenabeele, Sylvie; Karpytchev, Mikhail; Hays, Graeme Clive

    2015-02-02

    Cross-flows (winds or currents) affect animal movements [1-3]. Animals can temporarily be carried off course or permanently carried away from their preferred habitat by drift depending on their own traveling speed in relation to that of the flow [1]. Animals able to only weakly fly or swim will be the most impacted (e.g., [4]). To circumvent this problem, animals must be able to detect the effects of flow on their movements and respond to it [1, 2]. Here, we show that a weakly swimming organism, the jellyfish Rhizostoma octopus, can orientate its movements with respect to currents and that this behavior is key to the maintenance of blooms and essential to reduce the probability of stranding. We combined in situ observations with first-time deployment of accelerometers on free-ranging jellyfish and simulated the behavior observed in wild jellyfish within a high-resolution hydrodynamic model. Our results show that jellyfish can actively swim countercurrent in response to current drift, leading to significant life-history benefits, i.e., increased chance of survival and facilitated bloom formation. Current-oriented swimming may be achieved by jellyfish either directly detecting current shear across their body surface [5] or indirectly assessing drift direction using other cues (e.g., magnetic, infrasound). Our coupled behavioral-hydrodynamic model provides new evidence that current-oriented swimming contributes to jellyfish being able to form aggregations of hundreds to millions of individuals for up to several months, which may have substantial ecosystem and socioeconomic consequences [6, 7]. It also contributes to improve predictions of jellyfish blooms' magnitude and movements in coastal waters. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Zone modelling of the thermal performances of a large-scale bloom reheating furnace

    International Nuclear Information System (INIS)

    Tan, Chee-Keong; Jenkins, Joana; Ward, John; Broughton, Jonathan; Heeley, Andy

    2013-01-01

    This paper describes the development and comparison of a two- (2D) and three-dimensional (3D) mathematical models, based on the zone method of radiation analysis, to simulate the thermal performances of a large bloom reheating furnace. The modelling approach adopted in the current paper differs from previous work since it takes into account the net radiation interchanges between the top and bottom firing sections of the furnace and also allows for enthalpy exchange due to the flows of combustion products between these sections. The models were initially validated at two different furnace throughput rates using experimental and plant's model data supplied by Tata Steel. The results to-date demonstrated that the model predictions are in good agreement with measured heating profiles of the blooms encountered in the actual furnace. It was also found no significant differences between the predictions from the 2D and 3D models. Following the validation, the 2D model was then used to assess the impact of the furnace responses to changing throughput rate. It was found that the potential furnace response to changing throughput rate influences the settling time of the furnace to the next steady state operation. Overall the current work demonstrates the feasibility and practicality of zone modelling and its potential for incorporation into a model based furnace control system. - Highlights: ► 2D and 3D zone models of large-scale bloom reheating furnace. ► The models were validated with experimental and plant model data. ► Examine the transient furnace response to changing the furnace throughput rates. ► No significant differences found between the predictions from the 2D and 3D models.

  19. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  20. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

    Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

  1. The first recorded bloom of Pseudochattonella farcimen (Dictyochophyceae, Heterokonta, (Riisberg I., 2008 in the Gulf of Gdańsk

    Directory of Open Access Journals (Sweden)

    Maria Łotocka

    2009-03-01

    Full Text Available In April 2001 a local bloom of the heterokont phytoflagellate Pseudochattonella farcimen (Riisberg I., 2008 (initially named Chattonella aff. verruculosa was observed for the first time in the southern part of the Gulf of Gdańsk.The species occurred in high cell densities: the count was 11.5 × 106 cells dm-3 and the biomass 927.5 µgC dm-3.

  2. Practical framework for Bloom's based teaching and assessment of engineering outcomes

    Science.gov (United States)

    Mead, Patricia F.; Bennett, Mary M.

    2009-06-01

    ABET's outcomes-based assessment and evaluation requirements for engineering school accreditation has been a catalyst for curricular reform for engineering programs across the U.S. and around the world. Norfolk State University launched programs in Electronics and Optical Engineering in 2003. In 2007, Norfolk State became one of only six accredited Optical Engineering programs in the United States. In preparation for their first ABET evaluation in fall 2007, the faculty initiated an embedded-assessment program to insure continuous improvement toward the desired learning outcomes. The initial program design includes embedded assessments that have been generated using a practical framework for the creation of course activities based on Bloom's Learning Taxonomy. The framework includes specific performance criteria for each ABET-defined learning outcome. The embedded assessments are generated by individual faculty for courses that they are assigned to teach, and the performance criteria provide sufficient information to guide the faculty as they generate the embedded assignments. The assignments are typically administered through course exams, projects, electronic portfolio assignments, and other structured educational activities. The effectiveness of the assessment design is being evaluated through faculty surveys, faculty group discussions, and student performance. This paper outlines the assessment and evaluation plan, and the integrated processes that have been used to support the evaluation of learning outcomes using embedded assessment instruments.

  3. Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

    Science.gov (United States)

    Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

    2010-01-01

    Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

  4. Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

    Science.gov (United States)

    Vaughan, Adam; Bohac, Stanislav V

    2015-10-01

    Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  6. Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation

    Science.gov (United States)

    Lekki, John; Anderson, Robert; Avouris, Dulcinea; Becker, RIchard; Churnside, James; Cline, Michael; Demers, James; Leshkevich, George; Liou, Larry; Luvall, Jeffrey; hide

    2017-01-01

    Harmful algal blooms (HABs) in Lake Erie have been prominent in recent years. The bloom in 2014 reached a severe level causing the State of Ohio to declare a state of emergency. At that time NASA Glenn Research Center was requested by stakeholders to help monitor the blooms in Lake Erie. Glenn conducted flights twice a week in August and September and assembled and distributed the HAB information to the shoreline water resource managers using its hyperspectral imaging sensor (in development since 2006), the S??3 Viking aircraft, and funding resources from the NASA Headquarters Earth Science Division. Since then, the State of Ohio, National Oceanic and Atmospheric Administration (NOAA), and U.S. Environmental Protection Agency (EPA) have elevated their funding and activities for observing, monitoring, and addressing the root cause of HABs. Also, the communities and stakeholders have persistently requested NASA Glenn??s participation in HAB observation. Abundant field campaigns and sample analyses have been funded by Ohio and NOAA, which provided a great opportunity for NASA to advance science and airborne hyperspectral remote sensing economically. Capitalizing on this opportunity to advance the science of algal blooms and remote sensing, NASA Glenn conducted the Airborne Hyperspectral Observation of harmful algal blooms campaign in 2015 that was, in many respects, twice as large as the 2014 campaign. Focusing mostly on Lake Erie, but also including other small inland lakes and the Ohio River, the campaign was conducted in partnership with a large number of partners specializing in marine science and remote sensing. Airborne hyperspectral observation of HABs holds promise to distinguish potential HABs from nuisance blooms, determine their concentrations, and delineate their movement in an augmented spatial and temporal resolution and under clouds??all of which are excellent complements to satellite observations. Working with collaborators at several Ohio and Michigan

  7. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  8. Culturing Toxic Benthic Blooms: The Fate of Natural Biofilms in a Microcosm System

    Directory of Open Access Journals (Sweden)

    Francesca Di Pippo

    2017-08-01

    Full Text Available A microcosm designed for culturing aquatic phototrophic biofilms on artificial substrata was used to perform experiments with microphytobenthos sampled during summer toxic outbreaks of Ostreopsis cf. ovata along the Middle Tyrrhenian coast. This dynamic approach aimed at exploring the unique and complex nature of O. cf. ovata bloom development in the benthic system. Epibenthic assemblages were used as inocula for co-cultures of bloom organisms on polycarbonate slides at controlled environmental conditions. Biofilm surface adhesion, growth, and spatial structure were evaluated along with shifts in composition and matrix production in a low disturbance regime, simulating source habitat. Initial adhesion and substratum colonisation appeared as stochastic processes, then community structure and physiognomy markedly changed with time. Dominance of filamentous cyanobacteria and diatoms, and dense clusters of Amphidinium cf. carterae at the mature biofilm phases, were recorded by light and confocal microscopy, whilst O. cf. ovata growth was visibly limited in the late culture phases. Life-form strategies, competitiveness for resources, and possibly allelopathic interactions shaped biofilm structure during culture growth. HPLC (High Performance Liquid Chromatography analysis of exopolysaccharidic matrix revealed variations in sugar total amounts and composition. No toxic compounds were detected in the final communities tested by LC-MS (Liquid Chromatography- Mass Spectrometry and MALDI-TOF MS (Matrix Assisted Laser Desorption Ionization Time OF Flight Mass Spectroscopy techniques.

  9. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  10. Kinesthetic Astronomy: Significant Upgrades to the Sky Time Lesson that Support Student Learning

    Science.gov (United States)

    Morrow, C. A.; Zawaski, M.

    2004-12-01

    This paper will report on a significant upgrade to the first in a series of innovative, experiential lessons we call Kinesthetic Astronomy. The Sky Time lesson reconnects students with the astronomical meaning of the day, year, and seasons. Like all Kinesthetic Astronomy lessons, it teaches basic astronomical concepts through choreographed bodily movements and positions that provide educational sensory experiences. They are intended for sixth graders up through adult learners in both formal and informal educational settings. They emphasize astronomical concepts and phenomenon that people can readily encounter in their "everyday" lives such as time, seasons, and sky motions of the Sun, Moon, stars, and planets. Kinesthetic Astronomy lesson plans are fully aligned with national science education standards, both in content and instructional practice. Our lessons offer a complete learning cycle with written assessment opportunities now embedded throughout the lesson. We have substantially strengthened the written assessment options for the Sky Time lesson to help students translate their kinesthetic and visual learning into the verbal-linguistic and mathematical-logical realms of expression. Field testing with non-science undergraduates, middle school science teachers and students, Junior Girl Scouts, museum education staff, and outdoor educators has been providing evidence that Kinesthetic Astronomy techniques allow learners to achieve a good grasp of concepts that are much more difficult to learn in more conventional ways such as via textbooks or even computer animation. Field testing of the Sky Time lesson has also led us to significant changes from the previous version to support student learning. We will report on the nature of these changes.

  11. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Science.gov (United States)

    Wehmeyer, Christoph; Noé, Frank

    2018-06-01

    Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

  12. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  13. Fish Kill Incidents and Harmful Algal Blooms in Omani Waters

    Directory of Open Access Journals (Sweden)

    Hamed Mohammed Al Gheilani

    2011-01-01

    Full Text Available Red tide, one of the harmful algal blooms (HABs is a natural ecological phenomenon and often this event is accompanied by severe impacts on coastal resources, local economies, and public health. The occurrence of red tides has become more frequent in Omani waters in recent years. Some of them caused fish kill, damaged fishery resources and mariculture, threatened the marine environment and the osmosis membranes of desalination plants. However, a number of them have been harmless. The most common dinoflagellate Noctiluca scintillans is associated with the red tide events in Omani waters. Toxic species like Karenia selliformis, Prorocentrum arabianum, and Trichodesmium erythraeum have also been reported recently. Although red tides in Oman have been considered a consequence of upwelling in the summer season (May to September, recent phytoplankton outbreaks in Oman are not restricted to summer. Frequent algal blooms have been reported during winter (December to March. HABs may have contributed to hypoxia and/or other negative ecological impacts.

  14. Oceans and Human Health: Microplastics and Harmful Algal Bloom

    International Nuclear Information System (INIS)

    Sombrito, Elvira Z.

    2015-01-01

    Traditionally the focus of research and concern of environmental studies in the marine system is the impact of human activities in the ocean: the sources, distribution and fate of pollutants resulting from human activities. More recently, there has been recognition of the potential direct impact health can come from eating contaminated seafood, swimming in polluted water, and exposure to toxins from harmful algal blooms. This paper will present two areas of concern that illustrates the fact that the health of the oceans and the health of humans go hand in hand: chemical pollution from plastics in the ocean and harmful alga bloom. The nuclear methodologies than can be useful in these areas will also be introduced. It is hoped that through the recognition of the inter-dependence of the health of both humans and the oceans, efforts will be made to restore and preserve the oceans. (author)

  15. Didymosphenia geminata: Algal blooms in oligotrophic streams and rivers

    Science.gov (United States)

    Sundareshwar, P. V.; Upadhayay, S.; Abessa, M.; Honomichl, S.; Berdanier, B.; Spaulding, S. A.; Sandvik, C.; Trennepohl, A.

    2011-05-01

    In recent decades, the diatom Didymosphenia geminata has emerged as nuisance species in river systems around the world. This periphytic alga forms large “blooms” in temperate streams, presenting a counterintuitive result: the blooms occur primarily in oligotrophic streams and rivers, where phosphorus (P) availability typically limits primary production. The goal of this study is to examine how high algal biomass is formed under low P conditions. We reveal a biogeochemical process by which D. geminata mats concentrate P from flowing waters. First, the mucopolysaccaride stalks of D. geminata adsorb both iron (Fe) and P. Second, enzymatic and bacterial processes interact with Fe to increase the biological availability of P. We propose that a positive feedback between total stalk biomass and high growth rate is created, which results in abundant P for cell division. The affinity of stalks for Fe in association with iron-phosphorus biogeochemistry suggest a resolution to the paradox of algal blooms in oliogotrophic streams and rivers.

  16. Sedimentation of phytoplankton during a diatom bloom : Rates and mechanisms

    DEFF Research Database (Denmark)

    Kiørboe, Thomas; Hansen, J.L.S.; Alldredge, A.L.

    1996-01-01

    Phytoplankton blooms are uncoupled from grazing and are normally terminated by sedimentation. There are several potential mechanisms by which phytoplankton cells may settle out of the photic zone: sinking of individual cells or chains, coagulation of cells into aggregates with high settling...... velocities, settling of cells attached to marine snow aggregates formed from discarded larvacean houses or pteropod feeding webs, and packaging of cells into rapidly falling zooplankton fecal pellets. We quantified the relative significance of these different mechanisms during a diatom bloom in a temperate...... to marine snow aggregates formed from discarded larvacean houses, whereas settling of unaggregated cells was insignificant. Formation rates of phytoplankton aggregates by physical coagulation was very low, and losses by this mechanism were much less than 0.07 d(-1); phytoplankton aggregates were neither...

  17. KOMPARASI KEMAMPUAN KOMUNIKASI MATEMATIS SISWA DENGAN MODEL LEARNING CYCLE DAN TIME TOKEN

    Directory of Open Access Journals (Sweden)

    Arin Ayundhita

    2014-11-01

    Full Text Available Tujuan penelitian ini untuk mengetahui apakah model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token pada siswa kelas VIII materi keliling dan luas lingkaran dapat mencapai ketuntasan belajar dan untuk mengetahui manakah yang lebih baik antara model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token. Populasi dalam penelitian ini adalah siswa kelas VIII SMP Negeri 1 Sine Kabupaten Ngawi tahun pelajaran 2013/2014. Dengan menggunakan teknik cluster random sampling, terpilih sampel yaitu siswa kelas VIII A sebagai kelas eksperimen 1 dan kelas VIII E sebagai kelas eksperimen 2. Pengumpulan data dilakukan dengan metode dokumentasi, tes, dan observasi. Analisis data menggunakan uji proporsi dan uji perbedaan dua rata-rata. Dari hasil uji ketuntasan belajar diperoleh siswa kelas eksperimen 1 mencapai ketuntasan belajar klasikal sementara kelas eksperimen 2 belum mencapai ketuntasan belajar klasikal. Dari hasil uji perbedaan rata-rata satu pihak diperoleh rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 1 lebih baik daripada rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 2. Simpulan yang diperoleh adalah model pembelajaran Learning Cycle 5E lebih baik dari pembelajaran dengan model Time Token.

  18. Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

    Science.gov (United States)

    Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

    2017-07-01

    Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

  19. Evaluation of fatty acids as biomarkers for a natural plankton community. A field study of a spring bloom and a post-bloom period off West Greenland

    DEFF Research Database (Denmark)

    Reuss, N.; Poulsen, Louise K.

    2002-01-01

    community were taken at the depth of fluorescence maximum. High biomass and diatom dominance during the spring bloom and low biomass and flagellate dominance in the post-bloom period were reflected by the fatty acid profiles. The total amount of fatty acid ranged from 55 to 132 mug 1(-1) during the spring...

  20. Incorporating Real-time Earthquake Information into Large Enrollment Natural Disaster Course Learning

    Science.gov (United States)

    Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.

    2010-12-01

    Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground

  1. Marine harmful algal blooms, human health and wellbeing

    DEFF Research Database (Denmark)

    Berdalet, Elisa; Fleming, Lora E.; Gowen, Richard

    2016-01-01

    cause harm to humans and other organisms. These harmful algal blooms (HABs) have direct impacts on human health and negative influences on human wellbeing, mainly through their consequences to coastal ecosystem services (fisheries, tourism and recreation) and other marine organisms and environments...... maintaining intensive, multidisciplinary and collaborative scientific research, and strengthening the coordination with stakeholders, policymakers and the general public. Here we provide an overview of different aspects of the HABs phenomena, an important element of the intrinsic links between oceans...

  2. Unusual Bloom of Tetraselmis sp. in the Valparaiso Bay, Chile

    Science.gov (United States)

    2012-01-01

    de Valparaíso, Centro de Investigación y Gestión de los Recursos Naturales , Facultad de Ciencias , Gran Bretaña 1111, Valparaíso, Chile. 2University...RESPONSIBLE PERSON 19b. TELEPHONE NUMBER (Include area code) 31-05-2013 Journal Article Unusual Bloom of Tetraselmis sp. in the Valparasiso Bay...Invited speaker Classification X U c (X I Journal article (refereed) ( I Oral Presentation, published Journal article (not refereed) Oral Presentation

  3. The 1987–1989 Phytoplankton Bloom in Kaneohe Bay

    Directory of Open Access Journals (Sweden)

    Edward Laws

    2018-06-01

    Full Text Available A remarkable bloom of phytoplankton occurred in the southeast sector (SE of Kaneohe Bay from 1987 through 1989. During the bloom, concentrations of chlorophyll a at the former site of the Kaneohe municipal wastewater treatment plant outfall averaged a little more than 2 mg m–3 for a period of 40 months. The increase of chl a was accompanied by a roughly twofold increase in the percentage of chl a accounted for by cells retained on a 35-micron filter, a drawdown of silicate concentrations from roughly 10 μM to 3–4 μM, an increase of nitrate concentrations from roughly 0.5 to more than 3 μM, and an increase of phosphate concentrations from roughly 0.2 to 0.5 μM. Extraordinarily heavy rains on 31 December 1987 led to flooding and land runoff that briefly raised chl a concentrations in the bay to as high as 17 mg m–3, but the bloom in question developed more than one year before the 1987 New Year’s Eve flood. It was not caused by unusually heavy rainfall: the average rainfall during 1987–1989 was only 10% above the long-term average. Instead, the bloom appears to have been caused by a leak in the sanitary sewer line that was previously used to discharge secondary treated sewage into Kaneohe Bay. Ultimately, leaks in the sanitary sewer lines maintained by the City and County of Honolulu led to legal action and a consent decree that required upgrading and the renovation of the wastewater collection system.

  4. Environmental Chemistry and Chemical Ecology of "Green Tide" Seaweed Blooms.

    Science.gov (United States)

    Van Alstyne, Kathryn L; Nelson, Timothy A; Ridgway, Richard L

    2015-09-01

    Green tides are large growths or accumulations of green seaweeds that have been increasing in magnitude and frequency around the world. Because green tides consist of vast biomasses of algae in a limited area and are often seasonal or episodic, they go through periods of rapid growth in which they take up large amounts of nutrients and dissolved gases and generate bioactive natural products that may be stored in the plants, released into the environment, or broken down during decomposition. As a result of the use and production of inorganic and organic compounds, the algae in these blooms can have detrimental impacts on other organisms. Here, we review some of the effects that green tides have on the chemistry of seawater and the effects of the natural products that they produce. As blooms are developing and expanding, algae in green tides take up inorganic nutrients, such as nitrate and ortho-phosphate, which can limit their availability to other photosynthetic organisms. Their uptake of dissolved inorganic carbon for use in photosynthesis can cause localized spikes in the pH of seawater during the day with concomitant drops in the pH at night when the algae are respiring. Many of the algae that form green-tide blooms produce allelopathic compounds, which are metabolites that affect other species. The best documented allelopathic compounds include dimethylsulfoniopropionate (DMSP), dopamine, and reactive oxygen species (ROS) and their breakdown products. DMSP and dopamine are involved in defenses against herbivores. Dopamine and ROS are released into seawater where they can be allelopathic or toxic to other organisms. Thus, these macroalgal blooms can have harmful effects on nearby organisms by altering concentrations of nutrients and dissolved gas in seawater and by producing and releasing allelopathic or toxic compounds. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved

  5. Characteristics of picoplankton abundances during a Thalassiosira diporocyclus bloom in the Taiwan Bank in late winter.

    Science.gov (United States)

    Jiang, Xin; Li, Jiajun; Ke, Zhixin; Xiang, Chenhui; Tan, Yehui; Huang, Liangmin

    2017-04-15

    To understand the variations of picoplankton (Prochlorococcus, Synechococcus, picoeukaryotes, and heterotrophic bacteria) abundances during diatom bloom, the distribution of picoplankton in the Taiwan Bank, South China Sea was investigated using flow cytometry during a Thalassiosira diporocyclus bloom in March 2016. The results indicated an abrupt abundance decrease for Prochlorococcus, Synechococcus, and picoeukaryotes within the bloom area while the abundance of heterotrophic bacteria showed no significant difference between the bloom and non-bloom areas. We found two sub-groups of heterotrophic bacteria: high- and low-nucleic acid content (HNA and LNA) bacteria with HNA dominated in the bloom area whereas LNA dominated in the non-bloom area. Among the picoplankton components, HNA represented the highest (61.1%) carbon biomass in the bloom area while picoeukaryotes represented the highest (37.6%) in the non-bloom area. Our findings implied that heterotrophic bacteria, especially HNA, played an essential role during the diatom bloom. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  7. Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

    Science.gov (United States)

    Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

    2015-05-01

    This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

  8. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    Directory of Open Access Journals (Sweden)

    Richard Chiou

    2010-06-01

    Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.

  9. Learning motion concepts using real-time microcomputer-based laboratory tools

    Science.gov (United States)

    Thornton, Ronald K.; Sokoloff, David R.

    1990-09-01

    Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.

  10. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  11. The history of cyanobacterial blooms in the Baltic Sea.

    Science.gov (United States)

    Finni, T; Kononen, K; Olsonen, R; Wallström, K

    2001-08-01

    Long-term information on possible changes in cyanobacterial blooms in the Baltic Sea, formed mainly by Nodularia spumigena and Aphanizomenon sp., was sought in published records in historical (years 1887-1938) and modern (years 1974-1998) phytoplankton data sets. Old and new sampling methods and fixatives were tested to improve the comparison of data that had been collected and analyzed in different ways. A hundred years ago, plankton was mainly of interest as a source of fish food; eutrophication problems were only locally reported from the coast, mainly in southern haffs and the receiving waters of larger cities. There were few recordings of open-sea blooms before World War II. Abundances of Nodularia spumigena and Aphanizomenon sp. were low in the old material, and 137 summer samples from 1887-1938 showed no peak abundance. High abundances are common in the new material, and the range of the numbers of both taxa has increased markedly relative to the old material. Since the 1960s, cyanobacterial blooms have been common in the open sea in both the Baltic proper and the Gulf of Finland, indicating high availability of nutrients.

  12. Cyanobacterial bloom in the world largest freshwater lake Baikal

    Science.gov (United States)

    Namsaraev, Zorigto; Melnikova, Anna; Ivanov, Vasiliy; Komova, Anastasia; Teslyuk, Anton

    2018-02-01

    Lake Baikal is a UNESCO World Heritage Site and holds 20% of the world’s freshwater reserves. On July 26, 2016, a cyanobacterial bloom of a green colour a few kilometers in size with a bad odor was discovered by local people in the Barguzinsky Bay on the eastern shore of Lake Baikal. Our study showed very high concentration of chlorophyll a (41.7 g/m3) in the sample of bloom. We found that the bloom was dominated by a nitrogen-fixing heterocystous cyanobacteria of the genus Dolichospermum. The mass accumulation of cyanobacteria in the lake water with an extremely high chlorophyll a concentration can be explained by a combination of several factors: the discharge of biologicaly-available nutrients, including phosphorus, into the water of Lake Baikal; low wind speed and weak water mixing; buoyant cyanobacterial cells on the lake surface, which drifted towards the eastern coast, where the maximum concentration of chlorophyll a was recorded. In the center of the Barguzinsky Bay and in the open part of Lake Baikal, according to satellite data, the chlorophyll a concentration is several orders of magnitude lower than at the shoreline.

  13. Petal Thicknesses and Shape Transformations in Blooming Lilies

    Energy Technology Data Exchange (ETDEWEB)

    Portet, Thomas; Holmes, Peter N.; Bowden, Mark E.; Stephens, Sean A.; Varga, Tamas; Keller, Sarah L.

    2013-01-29

    During blooming, flower petals undergo significant shape changes. For lilies, various different mechanisms responsible for the change have been suggested [1,2]. One is that cell growth along the edge of a petal, or, more generally, a tepal, drives a transition from a cup shape (within a bud) to a saddle shape (within a bloom). This mechanism has been previously considered for tepals modeled as shallow elliptical shells whose thickness from the center, t, falls off at least as fast as t = t0 (1 - x2/a2 - y2/b2 ) [1]. Here t0 is the maximum thickness of the shell, a and b are the semimajor and semiminoraxes, x and y are the coordinates along the longitudinal and lateral axes. By measuring tepal thicknesses from images collected by x-ray tomography of intact buds and by photography of microtomed buds, we find that this condition is indeed met for both Lilium casablanca and Lilium lancifolium. [1] Liang and Mahadevan. Growth, geometry, and mechanics of a blooming lily.

  14. systemic approaches to teaching and learning a module of ...

    African Journals Online (AJOL)

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    In this article, we introduce the application of SATL in the subject of medical biochemistry. ... The factors that affect the selection of teaching and learning methods. • On the ... assessing: A revision of Bloom's taxonomy of educational objectives.

  15. Evaluation of the Learning and Teaching Environment of the Faculty ...

    African Journals Online (AJOL)

    2017-09-14

    Sep 14, 2017 ... perceptions of atmosphere, and social self-perceptions. Results: The ... to Bloom, the learning environment is a network of physical, social, as well as ..... Medical Licensure Examination in Japan. BMC Med Educ. 2010;10:35.

  16. Investigating the Relationship Between Self-Directed Learning Readiness and Time Management Skills in Turkish Undergraduate Nursing Students.

    Science.gov (United States)

    Ertuğ, Nurcan; Faydali, Saide

    The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.

  17. Peculiarities of the Woody Plants Re-Bloom

    Directory of Open Access Journals (Sweden)

    Opalko Olga Anatolievna

    2015-09-01

    Full Text Available The data of literary sources concerning the bloom of angiosperm plants and deviation in the development of a flower and inflorescence, in particular untimely flowering, was generalized; our observation results of some peculiarities of re-bloom of woody plants in the National Dendrological Park “Sofiyivka” of NAS of Ukraine (NDP “Sofiyivka” were discussed. The flowering process was formed during a long-term evolution of a propagation system of angiosperm plants as a basis of fertilization and further fruit and seed development. As a result of vernalization and photoperiodism reactions, flowering (under regular conditions occurs in the most favorable period for pollination and fertilization of every plant. However, various deviations, in particular, the untimely (most frequently double, sometimes three- and four-fold flowering occurs in this perfect process of generative organ formation of angiosperm plants. An increased number of reports about re-bloom (at the end of summer – at the beginning of fall of the representatives of various woody plant species whose flowers usually blossom in May-June prompts the analysis of the available information concerning the mechanisms of flowering and the causes which lead to deviation of flowering processes. Flowering of the woody plant representatives of the collection fund of the NDP “Sofiyivka” was studied; statistics about re-bloom in different cities of Ukraine were monitored. The classification of re-bloom facts was carried out according to V.L. Vitkovskiy (1984. Although this classification has mostly a stated nature, it was good enough when being formulated and, with certain conditions, it can be applied nowadays. Accordingly, using this classification, abnormal cases can include facts of early summer-fall flowering and early winter flowering. A late spring flowering can be adaptive response of damaged plants to exogenous stresses, due to which the probability of sexual propagation remains

  18. Time to Engage? Texting to Support and Enhance First Year Undergraduate Learning

    Directory of Open Access Journals (Sweden)

    Geraldine Jones

    2009-04-01

    Full Text Available In this paper we discuss a case study investigating how the academic and personal development of first year students on an undergraduate sports education degree can be supported and enhanced with mobile SMS communication. SMS-based technologies were introduced in response to students’ particular needs (in transition to Higher Education and characteristics (‘digital natives’. Despite being unaccustomed to using their mobile phones for academic study, students willingly participated in SMS communication with their tutor via a texting management service. Drawing on evidence from two student surveys, focus groups and a tutor’s journal, we illustrate the potential that mobile SMS communication has to link and establish continuity between face to face teaching sessions and online learning activities in the Virtual Learning Environment (VLE. Many students perceived the SMS communication to have had a positive impact on their management of study time. We link our findings with the existing literature and argue that mobile text based communication has the potential to support the development of time management skills, an important component of self regulatory learning, a skill which has been shown to be key in making a successful transition.

  19. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

    International Nuclear Information System (INIS)

    Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

    2017-01-01

    As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

  20. Large-scale machine learning and evaluation platform for real-time traffic surveillance

    Science.gov (United States)

    Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

    2016-09-01

    In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

  1. Climate Variability and Oceanographic Settings Associated with Interannual Variability in the Initiation of Dinophysis acuminata Blooms

    Directory of Open Access Journals (Sweden)

    Henrick Berger

    2013-08-01

    Full Text Available In 2012, there were exceptional blooms of D. acuminata in early spring in what appeared to be a mesoscale event affecting Western Iberia and the Bay of Biscay. The objective of this work was to identify common climatic patterns to explain the observed anomalies in two important aquaculture sites, the Galician Rías Baixas (NW Spain and Arcachon Bay (SW France. Here, we examine climate variability through physical-biological couplings, Sea Surface Temperature (SST anomalies and time of initiation of the upwelling season and its intensity over several decades. In 2012, the mesoscale features common to the two sites were positive anomalies in SST and unusual wind patterns. These led to an atypical predominance of upwelling in winter in the Galician Rías, and increased haline stratification associated with a southward advection of the Gironde plume in Arcachon Bay. Both scenarios promoted an early phytoplankton growth season and increased stability that enhanced D. acuminata growth. Therefore, a common climate anomaly caused exceptional blooms of D. acuminata in two distant regions through different triggering mechanisms. These results increase our capability to predict intense diarrhetic shellfish poisoning outbreaks in the early spring from observations in the preceding winter.

  2. The highly heterogeneous methylated genomes and diverse restriction-modification systems of bloom-forming Microcystis.

    Science.gov (United States)

    Zhao, Liang; Song, Yulong; Li, Lin; Gan, Nanqin; Brand, Jerry J; Song, Lirong

    2018-05-01

    The occurrence of harmful Microcystis blooms is increasing in frequency in a myriad of freshwater ecosystems. Despite considerable research pertaining to the cause and nature of these blooms, the molecular mechanisms behind the cosmopolitan distribution and phenotypic diversity in Microcystis are still unclear. We compared the patterns and extent of DNA methylation in three strains of Microcystis, PCC 7806SL, NIES-2549 and FACHB-1757, using Single Molecule Real-Time (SMRT) sequencing technology. Intact restriction-modification (R-M) systems were identified from the genomes of these strains, and from two previously sequenced strains of Microcystis, NIES-843 and TAIHU98. A large number of methylation motifs and R-M genes were identified in these strains, which differ substantially among different strains. Of the 35 motifs identified, eighteen had not previously been reported. Strain NIES-843 contains a larger number of total putative methyltransferase genes than have been reported previously from any bacterial genome. Genomic comparisons reveal that methyltransferases (some partial) may have been acquired from the environment through horizontal gene transfer. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  4. CORRELATION OF INTEREST TO LEARN AND USE TIME LEARNING WITH LEARNING ACHIEVEMENT AUTOMOTIVE ELECTRICAL IN CLASS XII LIGHT VEHICLE ENGINEERING SMK PIRI I YOGYAKARTA ACADEMIC YEAR 2013/2014

    Directory of Open Access Journals (Sweden)

    Ari Pujiatmoko

    2014-06-01

    Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014.  This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation.  The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.

  5. Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

    DEFF Research Database (Denmark)

    Sarkar, Achintya Kumar; Tan, Zheng-Hua

    2017-01-01

    In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

  6. Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

  7. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  8. Information extraction from dynamic PS-InSAR time series using machine learning

    Science.gov (United States)

    van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

    2017-12-01

    Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

  9. Succession and fate of the spring diatom bloom in Disko Bay, western Greenland

    DEFF Research Database (Denmark)

    Dünweber, Michael; Swalethorp, Rasmus; Kjellerup, Sanne

    2010-01-01

    Phytoplankton and copepod succession was investigated in Disko Bay, western Greenland from February to July 2008. The spring phytoplankton bloom developed immediately after the breakup of sea ice and reached a peak concentration of 24 mg chl a m–3 2 wk later. The bloom was analyzed during 3 phases...... from the initiation of the bloom but only had a small grazing impact on the phytoplankton. Consequently, there was a close coupling between the spring phytoplankton bloom and sedimentation of particulate organic carbon (POC). Out of 1836 ± 180 mg C m–2 d–1 leaving the upper 50 m, 60% was phytoplankton...... and fate of the phytoplankton spring bloom was controlled by nitrogen limitation and subsequent sedimentation, while grazing-mediated flux by the Calanus-dominated copepod community played a minor role in the termination of the spring bloom of Disko Bay....

  10. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  11. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  12. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    International Nuclear Information System (INIS)

    Veronesi, F; Grassi, S

    2016-01-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

  13. Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2012-01-01

    Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

  14. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  15. Effect of Mastery Learning on Senior Secondary School Students' Cognitive Learning Outcome in Quantitative Chemistry

    Science.gov (United States)

    Mitee, Telimoye Leesi; Obaitan, Georgina N.

    2015-01-01

    The cognitive learning outcome of Senior Secondary School chemistry students has been poor over the years in Nigeria. Poor mathematical skills and inefficient teaching methods have been identified as some of the major reasons for this. Bloom's theory of school learning and philosophy of mastery learning assert that virtually all students are…

  16. Oceanic and atmospheric influences on the variability of phytoplankton bloom in the southwestern Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Raj, R.P.; Peter, B.N.; Pushpadas, D.

    in the Mozambique Basin and inside the Mozambique Channel (MC). For a detailed inspection, we characterized the entire process of the development of this large dendritic bloom into two stages, early stage... period was in March. During 2001 and 2003, traces of the bloom (low concentration of chl-a) are observed in the MC and Mozambique Basin but not in the Madagascar basin. A weak bloom developed in the Madagascar Basin, Mozambique Basin and inside the MC...

  17. A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom

    Science.gov (United States)

    Page, Benjamin P.; Kumar, Abhishek; Mishra, Deepak R.

    2018-04-01

    As the frequency of cyanobacterial harmful algal blooms (CyanoHABs) become more common in recreational lakes and water supply reservoirs, demand for rapid detection and temporal monitoring will be imminent for effective management. The goal of this study was to demonstrate a novel and potentially operational cross-satellite based protocol for synoptic monitoring of rapidly evolving and increasingly common CyanoHABs in inland waters. The analysis involved a novel way to cross-calibrate a chlorophyll-a (Chl-a) detection model for the Landsat-8 OLI sensor from the relationship between the normalized difference chlorophyll index and the floating algal index derived from Sentinel-2A on a coinciding overpass date during the summer CyanoHAB bloom in Utah Lake. This aided in the construction of a time-series phenology of the Utah Lake CyanoHAB event. Spatio-temporal cyanobacterial density maps from both Sentinel-2A and Landsat-8 sensors revealed that the bloom started in the first week of July 2016 (July 3rd, mean cell count: 9163 cells/mL), reached peak in mid-July (July 15th, mean cell count: 108176 cells/mL), and reduced in August (August 24th, mean cell count: 9145 cells/mL). Analysis of physical and meteorological factors suggested a complex interaction between landscape processes (high surface runoff), climatic conditions (high temperature, high rainfall followed by negligible rainfall, stable wind), and water quality (low water level, high Chl-a) which created a supportive environment for triggering these blooms in Utah Lake. This cross satellite-based monitoring methods can be a great tool for regular monitoring and will reduce the budget cost for monitoring and predicting CyanoHABs in large lakes.

  18. The Dynamics of Microcystis Genotypes and Microcystin Production and Associations with Environmental Factors during Blooms in Lake Chaohu, China

    Directory of Open Access Journals (Sweden)

    Li Yu

    2014-12-01

    Full Text Available Lake Chaohu, which is a large, shallow, hypertrophic freshwater lake in southeastern China, has been experiencing lake-wide toxic Microcystis blooms in recent decades. To illuminate the relationships between microcystin (MC production, the genotypic composition of the Microcystis community and environmental factors, water samples and associated environmental data were collected from June to October 2012 within Lake Chaohu. The Microcystis genotypes and MC concentrations were quantified using quantitative real-time PCR (qPCR and HPLC, respectively. The results showed that the abundances of Microcystis genotypes and MC concentrations varied on spatial and temporal scales. Microcystis exists as a mixed population of toxic and non-toxic genotypes, and the proportion of toxic Microcystis genotypes ranged from 9.43% to 87.98%. Both Pearson correlation and stepwise multiple regressions demonstrated that throughout the entire lake, the abundances of total and toxic Microcystis and MC concentrations showed significant positive correlation with the total phosphorus and water temperature, suggesting that increases in temperature together with the phosphorus concentrations may promote more frequent toxic Microcystis blooms and higher concentrations of MC. Whereas, dissolved inorganic carbon (DIC was negatively correlated with the abundances of total and toxic Microcystis and MC concentrations, indicating that rising DIC concentrations may suppress toxic Microcystis abundance and reduce the MC concentrations in the future. Therefore, our results highlight the fact that future eutrophication and global climate change can affect the dynamics of toxic Microcystis blooms and hence change the MC levels in freshwater.

  19. The Dynamics of Microcystis Genotypes and Microcystin Production and Associations with Environmental Factors during Blooms in Lake Chaohu, China

    Science.gov (United States)

    Yu, Li; Kong, Fanxiang; Zhang, Min; Yang, Zhen; Shi, Xiaoli; Du, Mingyong

    2014-01-01

    Lake Chaohu, which is a large, shallow, hypertrophic freshwater lake in southeastern China, has been experiencing lake-wide toxic Microcystis blooms in recent decades. To illuminate the relationships between microcystin (MC) production, the genotypic composition of the Microcystis community and environmental factors, water samples and associated environmental data were collected from June to October 2012 within Lake Chaohu. The Microcystis genotypes and MC concentrations were quantified using quantitative real-time PCR (qPCR) and HPLC, respectively. The results showed that the abundances of Microcystis genotypes and MC concentrations varied on spatial and temporal scales. Microcystis exists as a mixed population of toxic and non-toxic genotypes, and the proportion of toxic Microcystis genotypes ranged from 9.43% to 87.98%. Both Pearson correlation and stepwise multiple regressions demonstrated that throughout the entire lake, the abundances of total and toxic Microcystis and MC concentrations showed significant positive correlation with the total phosphorus and water temperature, suggesting that increases in temperature together with the phosphorus concentrations may promote more frequent toxic Microcystis blooms and higher concentrations of MC. Whereas, dissolved inorganic carbon (DIC) was negatively correlated with the abundances of total and toxic Microcystis and MC concentrations, indicating that rising DIC concentrations may suppress toxic Microcystis abundance and reduce the MC concentrations in the future. Therefore, our results highlight the fact that future eutrophication and global climate change can affect the dynamics of toxic Microcystis blooms and hence change the MC levels in freshwater. PMID:25474494

  20. sxtA-Based Quantitative Molecular Assay To Identify Saxitoxin-Producing Harmful Algal Blooms in Marine Waters ▿ †

    Science.gov (United States)

    Murray, Shauna A.; Wiese, Maria; Stüken, Anke; Brett, Steve; Kellmann, Ralf; Hallegraeff, Gustaaf; Neilan, Brett A.

    2011-01-01

    The recent identification of genes involved in the production of the potent neurotoxin and keystone metabolite saxitoxin (STX) in marine eukaryotic phytoplankton has allowed us for the first time to develop molecular genetic methods to investigate the chemical ecology of harmful algal blooms in situ. We present a novel method for detecting and quantifying the potential for STX production in marine environmental samples. Our assay detects a domain of the gene sxtA that encodes a unique enzyme putatively involved in the sxt pathway in marine dinoflagellates, sxtA4. A product of the correct size was recovered from nine strains of four species of STX-producing Alexandrium and Gymnodinium catenatum and was not detected in the non-STX-producing Alexandrium species, other dinoflagellate cultures, or an environmental sample that did not contain known STX-producing species. However, sxtA4 was also detected in the non-STX-producing strain of Alexandrium tamarense, Tasmanian ribotype. We investigated the copy number of sxtA4 in three strains of Alexandrium catenella and found it to be relatively constant among strains. Using our novel method, we detected and quantified sxtA4 in three environmental blooms of Alexandrium catenella that led to STX uptake in oysters. We conclude that this method shows promise as an accurate, fast, and cost-effective means of quantifying the potential for STX production in marine samples and will be useful for biological oceanographic research and harmful algal bloom monitoring. PMID:21841034

  1. sxtA-based quantitative molecular assay to identify saxitoxin-producing harmful algal blooms in marine waters.

    Science.gov (United States)

    Murray, Shauna A; Wiese, Maria; Stüken, Anke; Brett, Steve; Kellmann, Ralf; Hallegraeff, Gustaaf; Neilan, Brett A

    2011-10-01

    The recent identification of genes involved in the production of the potent neurotoxin and keystone metabolite saxitoxin (STX) in marine eukaryotic phytoplankton has allowed us for the first time to develop molecular genetic methods to investigate the chemical ecology of harmful algal blooms in situ. We present a novel method for detecting and quantifying the potential for STX production in marine environmental samples. Our assay detects a domain of the gene sxtA that encodes a unique enzyme putatively involved in the sxt pathway in marine dinoflagellates, sxtA4. A product of the correct size was recovered from nine strains of four species of STX-producing Alexandrium and Gymnodinium catenatum and was not detected in the non-STX-producing Alexandrium species, other dinoflagellate cultures, or an environmental sample that did not contain known STX-producing species. However, sxtA4 was also detected in the non-STX-producing strain of Alexandrium tamarense, Tasmanian ribotype. We investigated the copy number of sxtA4 in three strains of Alexandrium catenella and found it to be relatively constant among strains. Using our novel method, we detected and quantified sxtA4 in three environmental blooms of Alexandrium catenella that led to STX uptake in oysters. We conclude that this method shows promise as an accurate, fast, and cost-effective means of quantifying the potential for STX production in marine samples and will be useful for biological oceanographic research and harmful algal bloom monitoring.

  2. Why are they late? Timing abilities and executive control among students with learning disabilities.

    Science.gov (United States)

    Grinblat, Nufar; Rosenblum, Sara

    2016-12-01

    While a deficient ability to perform daily tasks on time has been reported among students with learning disabilities (LD), the underlying mechanism behind their 'being late' is still unclear. This study aimed to evaluate the organization in time, time estimation abilities, actual performance time pertaining to specific daily activities, as well as the executive functions of students with LD in comparison to those of controls, and to assess the relationships between these domains among each group. The participants were 27 students with LD, aged 20-30, and 32 gender and age-matched controls who completed the Time Organization and Participation Scale (TOPS) and the Behavioral Rating Inventory of Executive Function-Adult version (BRIEF-A). In addition, their ability to estimate the time needed to complete the task of preparing a cup of coffee as well as their actual performance time were evaluated. The results indicated that in comparison to controls, students with LD showed significantly inferior organization in time (TOPS) and executive function abilities (BRIEF-A). Furthermore, their time estimation abilities were significantly inferior and they required significantly more time to prepare a cup of coffee. Regression analysis identified the variables that predicted organization in time and task performance time among each group. The significance of the results for both theoretical and clinical implications are discussed. What this paper adds? This study examines the underlying mechanism of the phenomena of being late among students with LD. Following a recent call for using ecologically valid assessments, the functional daily ability of students with LD to prepare a cup of coffee and to organize time were investigated. Furthermore, their time estimation and executive control abilities were examined as a possible underlying mechanism for their lateness. Although previous studies have indicated executive control deficits among students with LD, to our knowledge, this

  3. Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

    Science.gov (United States)

    Hulshof, Casper D.; de Jong, Ton

    2006-01-01

    Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

  4. A Virtual Learning Environment for Part-Time MASW Students: An Evaluation of the WebCT

    Science.gov (United States)

    Chan, Charles C.; Tsui, Ming-sum; Chan, Mandy Y. C.; Hong, Joe H.

    2008-01-01

    This study aims to evaluate the perception of a cohort of social workers studying for a part-time master's program in social work in using the popular Web-based learning platform--World Wide Web Course Tools (WebCT) as a complimentary method of teaching and learning. It was noted that social work profession began incorporating computer technology…

  5. Health risk assessment standards of cyanobacteria bloom occurrence in bathing sites

    Directory of Open Access Journals (Sweden)

    Agnieszka Stankiewicz

    2011-03-01

    Full Text Available Threat for human health appears during a massive cyanobacteria bloom in potable water used for human consumption or in basins used for recreational purposes. General health risk assessment standards and preventive measures to be taken by sanitation service were presented in scope of: – evaluation of cyanobacteria bloom occurrence in bathing sites / water bodies, – procedures in case of cyanobacteria bloom, including health risk assessment and decision making process to protect users’ health at bathing sites, – preventive measures, to be taken in case of cyanobacteria bloom occurrence in bathing sites and basins, where bathing sites are located.

  6. Phytoplankton bloom and subpolar gyre induced dynamics in the North Atlantic

    DEFF Research Database (Denmark)

    Ferreira, Ana Sofia; Hátún, H.; Counillon, F.

    Several hypotheses have been promoted for phytoplankton bloom onset in the North Atlantic. First we show that the bloom dynamics in the northeastern corner stand out from the rest of the subpolar Atlantic, and thus warrants focused attention. We hypothesized that, for this region, late and weak...... blooms are expected in years of a strong subpolar gyre, i.e. strong atmospheric forcing, and cold and low saline conditions. We apply novel phenology algorithms to satellite ocean colour data, and analyse the outcome together with the subpolar gyre index. We find that the relationship between the bloom...

  7. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2016-01-01

    Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

  8. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  9. Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

    Science.gov (United States)

    Janik, M; Bossew, P; Kurihara, O

    2018-07-15

    Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical

  10. Automated business process management – in times of digital transformation using machine learning or artificial intelligence

    Directory of Open Access Journals (Sweden)

    Paschek Daniel

    2017-01-01

    Full Text Available The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

  11. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

    Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

  12. Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

    Science.gov (United States)

    Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

    2017-12-01

    Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

  13. Cadmium and phosphate variability during algal blooms of the dinoflagellate Lingulodinium polyedrum in Todos Santos Bay, Baja California, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Gutierrez-Mejia, E. [Posgrado en Oceanografía Costera, Instituto de Investigaciones Oceanológicas/Facultad de Ciencias Marinas, Universidad Autónoma de Baja California, Campus Sauzal, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada, Baja California CP 22860 (Mexico); Lares, M.L., E-mail: llares@cicese.mx [División de Oceanología, Departamento de Oceanografía Biológica, Centro de Investigación Científica y de Educación Superior de Ensenada, Km 107 Carretera Transpeninsular Ensenada-Tijuana, Ensenada, Baja California CP 22880 (Mexico); Huerta-Diaz, M.A.; Delgadillo-Hinojosa, F. [Instituto de Investigaciones Oceanológicas, Universidad Autónoma de Baja California, Campus Sauzal, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada, Baja California CP 22860 (Mexico)

    2016-01-15

    } cells L{sup −1} of L. polyedrum above which Cd and PO{sub 4}{sup 3−} significantly increased due to remineralization in coastal waters during the bloom development. - Highlights: • Dinoflagellate algal blooms have increased over time in coastal areas • Vertical and temporal variability of Cd{sub p}, Cd{sub d}, PO{sub 4}{sup 3-}, and Cd{sub d}/PO{sub 4}{sup 3-} was investigated • High Cd{sub d} and Cd{sub p} seawater concentrations were associated with L. polyedrum abundance • A decoupling in the behavior of Cd and PO{sub 4}{sup 3-} was associated with bloom development • An abundance threshold of ~ 10{sup 6} cells L{sup -1} was associated with remineralization of Cd.

  14. Cadmium and phosphate variability during algal blooms of the dinoflagellate Lingulodinium polyedrum in Todos Santos Bay, Baja California, Mexico

    International Nuclear Information System (INIS)

    Gutierrez-Mejia, E.; Lares, M.L.; Huerta-Diaz, M.A.; Delgadillo-Hinojosa, F.

    2016-01-01

    waters during the bloom development. - Highlights: • Dinoflagellate algal blooms have increased over time in coastal areas • Vertical and temporal variability of Cd_p, Cd_d, PO_4"3"-, and Cd_d/PO_4"3"- was investigated • High Cd_d and Cd_p seawater concentrations were associated with L. polyedrum abundance • A decoupling in the behavior of Cd and PO_4"3"- was associated with bloom development • An abundance threshold of ~ 10"6 cells L"-"1 was associated with remineralization of Cd

  15. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2015-04-01

    Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  16. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  17. Changes in recognition memory over time: an ERP investigation into vocabulary learning.

    Directory of Open Access Journals (Sweden)

    Shekeila D Palmer

    Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

  18. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  19. Using machine learning to identify structural breaks in single-group interrupted time series designs.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

  20. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....